diff --git a/experiment/aim.md b/experiment/aim.md index 4bc1c54..50d714d 100644 --- a/experiment/aim.md +++ b/experiment/aim.md @@ -1 +1 @@ -### Aim of the experiment +### The aim of the experment is to describe the basic statistical features of the data and summaries about the sample and the measures together with the graphical analysis. diff --git a/experiment/contributors.md b/experiment/contributors.md new file mode 100644 index 0000000..d641084 --- /dev/null +++ b/experiment/contributors.md @@ -0,0 +1,17 @@ +### Contributors List + + +### Subject Matter Experts +| SNo. | Name | Email | Institute | ID | +| :---: | :---: | :---: | :---: | :---: | +| 1 | Dr. S. Dharmaraja | dharmar@maths.iitd.ac.in | Indian Institute of Technology Delhi | 15984 | +| 2 | Dr. Vidyottama Jain | vidyottama.jain@curaj.ac.in | Central University of Rajasthan | 131042 | + + + +### Developers +| SNo. | Name | Email | Institute | ID | +| :---: | :---: | :---: | :---: | :---: | +| 1 | Anisha | maz188445@iitd.ac.in | Indian Institute of Technology Delhi | 2018MAZ8445 | +| 2 | Shakti Singh | maz208241@iitd.ac.in | Indian Institute of Technology Delhi | 2020MAZ8241 | + diff --git a/experiment/experiment-name.md b/experiment/experiment-name.md index b0d364b..e69d7cf 100644 --- a/experiment/experiment-name.md +++ b/experiment/experiment-name.md @@ -1 +1 @@ -## Experiment name +## Descriptive statistics diff --git a/experiment/images/Image 27-03-23 at 12.37 PM (1).jpg b/experiment/images/Image 27-03-23 at 12.37 PM (1).jpg new file mode 100644 index 0000000..bc37373 Binary files /dev/null and b/experiment/images/Image 27-03-23 at 12.37 PM (1).jpg differ diff --git a/experiment/images/Image 27-03-23 at 12.37 PM.jpg b/experiment/images/Image 27-03-23 at 12.37 PM.jpg new file mode 100644 index 0000000..8368587 Binary files /dev/null and b/experiment/images/Image 27-03-23 at 12.37 PM.jpg differ diff --git a/experiment/images/Image 27-03-23 at 12.38 PM.jpg b/experiment/images/Image 27-03-23 at 12.38 PM.jpg new file mode 100644 index 0000000..0829656 Binary files /dev/null and b/experiment/images/Image 27-03-23 at 12.38 PM.jpg differ diff --git a/experiment/images/Image 27-03-23 at 12.39 PM (1).jpg b/experiment/images/Image 27-03-23 at 12.39 PM (1).jpg new file mode 100644 index 0000000..a2596d1 Binary files /dev/null and b/experiment/images/Image 27-03-23 at 12.39 PM (1).jpg differ diff --git a/experiment/images/Image 27-03-23 at 12.39 PM.jpg b/experiment/images/Image 27-03-23 at 12.39 PM.jpg new file mode 100644 index 0000000..4e2baef Binary files /dev/null and b/experiment/images/Image 27-03-23 at 12.39 PM.jpg differ diff --git a/experiment/images/Image 27-03-23 at 12.40 PM (1).jpg b/experiment/images/Image 27-03-23 at 12.40 PM (1).jpg new file mode 100644 index 0000000..56692c4 Binary files /dev/null and b/experiment/images/Image 27-03-23 at 12.40 PM (1).jpg differ diff --git a/experiment/images/Image 27-03-23 at 12.40 PM.jpg b/experiment/images/Image 27-03-23 at 12.40 PM.jpg new file mode 100644 index 0000000..e511081 Binary files /dev/null and b/experiment/images/Image 27-03-23 at 12.40 PM.jpg differ diff --git a/experiment/images/Image 27-03-23 at 12.43 PM.jpg b/experiment/images/Image 27-03-23 at 12.43 PM.jpg new file mode 100644 index 0000000..59cedd1 Binary files /dev/null and b/experiment/images/Image 27-03-23 at 12.43 PM.jpg differ diff --git a/experiment/images/Image 27-03-23 at 4.19 PM.jpg b/experiment/images/Image 27-03-23 at 4.19 PM.jpg new file mode 100644 index 0000000..682fca8 Binary files /dev/null and b/experiment/images/Image 27-03-23 at 4.19 PM.jpg differ diff --git a/experiment/posttest.json b/experiment/posttest.json index e0e6fc2..819e33a 100644 --- a/experiment/posttest.json +++ b/experiment/posttest.json @@ -2,37 +2,88 @@ "version": 2.0, "questions": [ { - "question": "This is a Sample Question 1?", + "question": "Luke counts the number of gummy bears he eats every day for 1 week: {39, 18, 24, 51, 40, 15, 23}. On average, how many gummy bears does Luke eat each day?", "answers": { - "a": "answer1", - "b": "answer2", - "c": "answer3", - "d": "answer4" + "a": "30", + "b": "40", + "c": "45", + "d": "10" }, "explanations": { - "a": "Explanation 1 here", - "b": "Explanation 2", - "c": "Explanation 2", - "d": "Explanation 2" + "a": "average=(39+18+24+51+40+15+23)/7=30.", + "b": "average=(39+18+24+51+40+15+23)/7=30.", + "c": "average=(39+18+24+51+40+15+23)/7=30.", + "d": "average=(39+18+24+51+40+15+23)/7=30." }, "correctAnswer": "a", "difficulty": "beginner" }, { - "question": "This is a Sample Question 2?", + "question": "Consider this data set: {1,3,4,6,6,7,7,7,9,10}. Order the mean, median, mode, and midrange of the data set from least to greatest.?", "answers": { - "a": "answer1", - "b": "answer2", - "c": "answer3", - "d": "answer4" + "a": " mean, Midrange, median, mode", + "b": "Midrange, mean, mode, median", + "c": "Midrange,median, mean, mode", + "d": "Midrange, mean, median, mode" }, "explanations": { - "a": "Explanation 1 here", - "b": "Explanation 2", - "c": "Explanation 2", - "d": "Explanation 2" + "a": "The median of a data set with an even number of elements is the arithmetic mean of the two elements that fall in the middle when the elements are arranged in ascending order. These two elements ar 6 and 7, so the median is (6+72)/2=6.5. mean= 6, The mode of the data set is the element which occurs the most frequently. Since 7 appears three times, 6 appears twice, and all other elements appear once, the mode is 7. The midrange of the data set is the arithmetic mean of the least and greatest elements. These two elements are 1 and 10, so the midrange is 1+102=5.5. Hence, option d ", + "b": "The median of a data set with an even number of elements is the arithmetic mean of the two elements that fall in the middle when the elements are arranged in ascending order. These two elements ar 6 and 7, so the median is (6+72)/2=6.5. mean= 6, The mode of the data set is the element which occurs the most frequently. Since 7 appears three times, 6 appears twice, and all other elements appear once, the mode is 7. The midrange of the data set is the arithmetic mean of the least and greatest elements. These two elements are 1 and 10, so the midrange is 1+102=5.5. Hence, option d", + "c": "The median of a data set with an even number of elements is the arithmetic mean of the two elements that fall in the middle when the elements are arranged in ascending order. These two elements ar 6 and 7, so the median is (6+72)/2=6.5. mean= 6, The mode of the data set is the element which occurs the most frequently. Since 7 appears three times, 6 appears twice, and all other elements appear once, the mode is 7. The midrange of the data set is the arithmetic mean of the least and greatest elements. These two elements are 1 and 10, so the midrange is 1+102=5.5. Hence, option d", + "d": "The median of a data set with an even number of elements is the arithmetic mean of the two elements that fall in the middle when the elements are arranged in ascending order. These two elements ar 6 and 7, so the median is (6+72)/2=6.5. mean= 6, The mode of the data set is the element which occurs the most frequently. Since 7 appears three times, 6 appears twice, and all other elements appear once, the mode is 7. The midrange of the data set is the arithmetic mean of the least and greatest elements. These two elements are 1 and 10, so the midrange is 1+102=5.5. Hence, option d" }, - "correctAnswer": "c", + "correctAnswer": "d", + "difficulty": "beginner" + }, + { + "question": "What is the median of the following numbers? 1/2,1/3,1/4,1/5,1/6,1/7?", + "answers": { + "a": "1/4", + "b": "9/40", + "c": "1/5", + "d": "2/9" + }, + "explanations": { + "a": "The median of a data set with an even number of elements is the mean of its two middle elements, when ranked. The set is already ranked, so just find the mean of middle elements 1/4 and 1/5: 1/2⋅(5/20+4/20) = 9/40.", + "b": "The median of a data set with an even number of elements is the mean of its two middle elements, when ranked. The set is already ranked, so just find the mean of middle elements 1/4 and 1/5: 1/2⋅(5/20+4/20) = 9/40.", + "c": "The median of a data set with an even number of elements is the mean of its two middle elements, when ranked. The set is already ranked, so just find the mean of middle elements 1/4 and 1/5: 1/2⋅(5/20+4/20) = 9/40.", + "d": "The median of a data set with an even number of elements is the mean of its two middle elements, when ranked. The set is already ranked, so just find the mean of middle elements 1/4 and 1/5: 1/2⋅(5/20+4/20) = 9/40." + }, + "correctAnswer": "b", + "difficulty": "beginner" + }, + { + "question": "Rita keeps track of the number of times she goes to the gym each week for 1260 weeks. She goes 1 day a week for 119 weeks, 2 days a week for 254 weeks, 3 days a week for 376 weeks, and 4 days a week for 511 weeks. What is the mode of the number of days she goes to the gym each week?", + "answers": { + "a": "30", + "b": "10", + "c": "5", + "d": "4 " + }, + "explanations": { + "a": "The mode is the number that comes up most frequently in a set. Rita goes to the gym 4 times a week for 511 weeks. She clearly goes 4 times per week far more often than she goes 1, 2, or 3 times per week. Therefore the mode is 4 days/week. It is NOT 511 weeks. That is the frequency with which 4 days/week occurs, but not the mode.", + "b": "The mode is the number that comes up most frequently in a set. Rita goes to the gym 4 times a week for 511 weeks. She clearly goes 4 times per week far more often than she goes 1, 2, or 3 times per week. Therefore the mode is 4 days/week. It is NOT 511 weeks. That is the frequency with which 4 days/week occurs, but not the mode.", + "c": "The mode is the number that comes up most frequently in a set. Rita goes to the gym 4 times a week for 511 weeks. She clearly goes 4 times per week far more often than she goes 1, 2, or 3 times per week. Therefore the mode is 4 days/week. It is NOT 511 weeks. That is the frequency with which 4 days/week occurs, but not the mode.", + "d": "The mode is the number that comes up most frequently in a set. Rita goes to the gym 4 times a week for 511 weeks. She clearly goes 4 times per week far more often than she goes 1, 2, or 3 times per week. Therefore the mode is 4 days/week. It is NOT 511 weeks. That is the frequency with which 4 days/week occurs, but not the mode." + }, + "correctAnswer": "d", + "difficulty": "beginner" + }, + { + "question": "A biosphere reserve contains 280 trees. Trees were chosen at random, and their heights were recorded in cm: 51, 46, 79, 38, and 57. Calculate their height's standard deviation.?", + "answers": { + "a": "15.51", + "b": "13.31", + "c": "17", + "d": "10" + }, + "explanations": { + "a": "Number of observations = 5, mean = 54.2 cm. therefore sd = 15.51. ", + "b": "Number of observations = 5, mean = 54.2 cm. therefore sd = 15.51.", + "c": "Number of observations = 5, mean = 54.2 cm. therefore sd = 15.51.", + "d": "Number of observations = 5, mean = 54.2 cm. therefore sd = 15.51." + }, + "correctAnswer": "a", "difficulty": "beginner" } ] diff --git a/experiment/pretest.json b/experiment/pretest.json index e0e6fc2..a67b235 100644 --- a/experiment/pretest.json +++ b/experiment/pretest.json @@ -2,38 +2,89 @@ "version": 2.0, "questions": [ { - "question": "This is a Sample Question 1?", + "question": "The observations X,...,Xn have a mean of 52, a median 52.1, and a standard deviation of 7. Eight percent of the observation are greater than 66%; 7.9% of the observations are below 38. Based on this information, which of the following statements best describes the data ?", "answers": { - "a": "answer1", - "b": "answer2", - "c": "answer3", - "d": "answer4" + "a": "The distribution has positive skewness", + "b": "The distribution has negative skewness", + "c": "The distribution has high kurtosis", + "d": "The distribution conforms to a normal distribution" }, "explanations": { - "a": "Explanation 1 here", - "b": "Explanation 2", - "c": "Explanation 2", - "d": "Explanation 2" + "a": "Data has high kurtosis.", + "b": "Data has high kurtosis.", + "c": "Data has high kurtosis.", + "d": "Data has high kurtosis." }, - "correctAnswer": "a", + "correctAnswer": "c", "difficulty": "beginner" }, { - "question": "This is a Sample Question 2?", + "question": "In a week the prices of a bag of rice were 350, 280, 340, 290, 320, 310, 300. The range is?", "answers": { - "a": "answer1", - "b": "answer2", - "c": "answer3", - "d": "answer4" + "a": "60", + "b": "80", + "c": "70", + "d": "100" }, "explanations": { - "a": "Explanation 1 here", - "b": "Explanation 2", - "c": "Explanation 2", - "d": "Explanation 2" + "a": "Range= max value - minimum value = 70", + "b": "Range= max value - minimum value = 70", + "c": "Range= max value - minimum value = 70", + "d": "Range= max value - minimum value = 70" }, "correctAnswer": "c", "difficulty": "beginner" + }, + { + "question": " The mean of 10 observations is 10. All observations are increased by 10%. The mean of the increased observations shall be?", + "answers": { + "a": "20", + "b": "11", + "c": "10", + "d": "25" + }, + "explanations": { + "a": "When the observations are increased by 10%, the mean increases by 10%. The mean = 10, The mean increased by 10% of 10 = 10*10/100 = 1. Therefore, the mean of increased observation shall be 11 ", + "b": "When the observations are increased by 10%, the mean increases by 10%. The mean = 10, The mean increased by 10% of 10 = 10*10/100 = 1. Therefore, the mean of increased observation shall be 11 ", + "c": "When the observations are increased by 10%, the mean increases by 10%. The mean = 10, The mean increased by 10% of 10 = 10*10/100 = 1. Therefore, the mean of increased observation shall be 11 ", + "d": "When the observations are increased by 10%, the mean increases by 10%. The mean = 10, The mean increased by 10% of 10 = 10*10/100 = 1. Therefore, the mean of increased observation shall be 11 " + }, + "correctAnswer": "b", + "difficulty": "beginner" + }, + { + "question": " If the mean (x) is 4 and the data points are 2, 3, 4, 5, and 6, what will be the sum of the squared deviations from the mean (x)?", + "answers": { + "a": "8", + "b": "6", + "c": "10", + "d": "12" + }, + "explanations": { + "a": "The sum of the squared deviations from the mean(X)= Sum_{1}^{n}[(Xi-mean(X))^2] = 10 ", + "b": "The sum of the squared deviations from the mean(X)= Sum_{1}^{n}[(Xi-mean(X))^2] = 10 ", + "c": "The sum of the squared deviations from the mean(X)= Sum_{1}^{n}[(Xi-mean(X))^2] = 10 ", + "d": "The sum of the squared deviations from the mean(X)= Sum_{1}^{n}[(Xi-mean(X))^2] = 10 " + }, + "correctAnswer": "c", + "difficulty": "beginner" + }, + { + "question": " If a distribution is skewed to the left, then it is?", + "answers": { + "a": "Negatively skewed", + "b": "Positively skewed", + "c": "Symmetrically skewed", + "d": "Symmetrical" + }, + "explanations": { + "a": "Negatively skewed ", + "b": "Negatively skewed ", + "c": "Negatively skewed ", + "d": "Negatively skewed " + }, + "correctAnswer": "a", + "difficulty": "beginner" } ] } diff --git a/experiment/procedure.md b/experiment/procedure.md index 37929cc..145dd9e 100644 --- a/experiment/procedure.md +++ b/experiment/procedure.md @@ -1 +1,17 @@ -### Procedure \ No newline at end of file +##### In order to perform the experiment, one needs to go through the following steps sequentially: + +###### Step 1: Select Type of Statistics +In the simulation step, choose the type of statistics you want to compute. This could include measures such as mean, median, mode, variance, standard deviation, quartiles, etc. + +###### Step 2: Enter Data and Frequencies +Enter the data along with their corresponding frequencies. This step allows you to input the dataset for which you want to compute descriptive statistics. + +###### Step 3: Select Graph Type +Choose what type of graph you want to plot to visualize the descriptive statistics. Options may include histograms, box plots, bar charts, etc., depending on the nature of your data and the statistics being calculated. + +###### Step 4: Click the 'Calculate' Button +Initiate the experiment by clicking the 'Calculate' button. This triggers the computation of the selected descriptive statistics based on the provided data and frequencies. + +###### Step 5: View Descriptive Statistics +After clicking 'Calculate', observe the descriptive statistics of the entered data. This includes measures such as mean, median, mode, variance, standard deviation, quartiles, etc., depending on what you selected in Step 1. + diff --git a/experiment/references.md b/experiment/references.md index b15b47e..ec48a95 100644 --- a/experiment/references.md +++ b/experiment/references.md @@ -1 +1,6 @@ -### Link your references in here \ No newline at end of file +### Selvamuthu, D., & Das, D. (2018). Introduction to statistical methods, design of experiments and statistical quality control. Singapore: Springer Singapore. [a link] {https://link.springer.com/book/10.1007/978-981-13-1736-1} + +### Castañeda, L. B., Arunachalam, V., & Dharmaraja, S. (2012). Introduction to probability and stochastic processes with applications. John Wiley & Sons.[a link]{https://www.wiley.com/en-us/Introduction+to+Probability+and+Stochastic+Processes+with+Applications-p-9781118344972} + + +## Sheldon Ross, Introduction to Probability and Statistics for Engineers and Scientists, 5th Edition, Academic Press, 2014. [a link]{https://www.pearson.com/store/p/probability-and-statistical-inference-global-edition/P200000004474/9781292062358} diff --git a/experiment/simulation/Experiments/Descriptive/descriptive.css b/experiment/simulation/Experiments/Descriptive/descriptive.css new file mode 100644 index 0000000..7997872 --- /dev/null +++ b/experiment/simulation/Experiments/Descriptive/descriptive.css @@ -0,0 +1,321 @@ + +/* this file should contain base reset, header and footer styles, theme colors */ + + + + + + + + +/* Start of theme variables ---------------------------------------------------------------------------*/ + +:root { + + --main-white: #f0f0f0; + --main-red: #ee9329; + --main-blue: #0951a5; + --main-gray: #303841; + + --main-background: #f6efef; + --main-Paragraph: #2e2e2e; + --main-Headline: #181818; + --main-Button-text: #181818; + --main-button: #4fc4cf; + + --main-stroke: #181818; + --main-highlight: #4fc4cf; + --main-primary: #ffffff; + --main-secondary: #37474F; + --main-tertiary: #fbdd74; 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+ + diff --git a/experiment/simulation/Experiments/Descriptive/script.js b/experiment/simulation/Experiments/Descriptive/script.js new file mode 100644 index 0000000..c5ce21a --- /dev/null +++ b/experiment/simulation/Experiments/Descriptive/script.js @@ -0,0 +1,438 @@ +//javascript file for descriptive statistics experiments + +function getData() { + // extacting data points into an array from the string of numbers + + // extacting data points into an array from the string of numbers + let dataString = document.getElementById("numbers").value; + let freqString = document.getElementById("frequencies").value; + + // removing extra space char at the bigginnig and end of inputed data + dataString = dataString.trim(); + freqString = freqString.trim(); + + // removing extra space in-between the data values in the data strings + dataString = dataString.replace(/\s+/g, " "); + freqString = freqString.replace(/\s+/g, " "); + + // defining a test to check whether the data in class data or not + let classtest = /\d+[-]\d+/; + + // testing data string for classinput + //if is is class data data we will return classdatafreq array if not we will return normal freqdataAray + if (classtest.test(dataString)) { + let dataArrayString = ""; + dataArrayString = dataString.split(" "); + console.log(dataArrayString); + } + + // making an num array from the string of point data + let dataArray = dataString.split(" "); + for (let x = 0; x < dataArray.length; x++) { + dataArray[x] = +dataArray[x]; + } + + // making an num array from the string of freq data + let freqArray = freqString.split(" "); + for (let x = 0; x < freqArray.length; x++) { + freqArray[x] = +freqArray[x]; + } + + let freqDataArray = []; + + if (freqArray.length !== dataArray.length) { + alert("number of data and freqencies don't match"); + } else { + freqDataArray.push(dataArray); + freqDataArray.push(freqArray); + } + console.log(freqDataArray); + return freqDataArray; +} + + +function getFlatData() { + // extacting data points into an array from the string of numbers + + // extacting data points into an array from the string of numbers + let dataString = document.getElementById("numbers").value; + let freqString = document.getElementById("frequencies").value; + + // removing extra space char at the bigginnig and end of inputed data + dataString = dataString.trim(); + freqString = freqString.trim(); + + // removing extra space in-between the data values in the data strings + dataString = dataString.replace(/\s+/g, " "); + freqString = freqString.replace(/\s+/g, " "); + + // defining a test to check whether the data in class data or not + let classtest = /\d+[-]\d+/; + + // testing data string for classinput + //if is is class data data we will return classdatafreq array if not we will return normal freqdataAray + if (classtest.test(dataString)) { + let dataArrayString = ""; + dataArrayString = dataString.split(" "); + console.log(dataArrayString); + } + + // making an num array from the string of point data + let dataArray = dataString.split(" "); + for (let x = 0; x < dataArray.length; x++) { + dataArray[x] = +dataArray[x]; + } + + // making an num array from the string of freq data + let freqArray = freqString.split(" "); + for (let x = 0; x < freqArray.length; x++) { + freqArray[x] = +freqArray[x]; + } + + let freqDataArray = []; + + if (freqArray.length !== dataArray.length) { + alert("number of data and freqencies don't match"); + } else { + + for(let k = 0; k < dataArray.length ; k++) + { + let tempArray = [] + for(let i = freqArray[k]; i > 0; i--){ + tempArray.push(dataArray[k]); + } + // tempArray.push(freqArray[k]); + freqDataArray.push(tempArray) ; + } + } + + freqDataArray = freqDataArray.flat(); + // console.log(freqDataArray) + return freqDataArray; +} + +//--------------------------------------------------------------------- +// Start of all Charting Functions +//--------------------------------------------------------------------- + +// Start of Line Chart Plot funtion +google.charts.load("current", { packages: ["corechart"] }); + +function makeLineChart() { + //getting data + let mydata = getData(); + + let tempdata = []; + tempdata.push(mydata[0].map(String)); + tempdata.push(mydata[1]); + + +mydata = tempdata; + + console.log(mydata) + + let data = new google.visualization.DataTable(); + data.addColumn("string", "Data"); + data.addColumn("number", "Frequency"); + + let rows = []; + + + for (let i = 0; i < mydata[1].length; i++) { + let tempArray = []; + tempArray.push(mydata[0][i]); + tempArray.push(mydata[1][i]); + rows.push(tempArray); + } + + +data.addRows(rows) + // Set Options + var options = { + title: "Line Chart", + hAxis: { title: "Data" }, + vAxis: { title: "Frequency" }, + legend: "none", + }; + // Draw Chart + var chart = new google.visualization.LineChart( + document.getElementById("myChart") + ); + chart.draw(data, options); +} +// End of Line Chart Plot funtion + + + + + +//--------------------------------------------------------------------- +// Start of Pie Chart plot function +function makePieChart() { + let mydata = getData(); + + let tempdata = []; + tempdata.push(mydata[0].map(String)); + tempdata.push(mydata[1]); + + +mydata = tempdata; + + console.log(mydata) + + let data = new google.visualization.DataTable(); + data.addColumn("string", "Data"); + data.addColumn("number", "Frequency"); + + let rows = []; + + + for (let i = 0; i < mydata[1].length; i++) { + let tempArray = []; + tempArray.push(mydata[0][i]); + tempArray.push(mydata[1][i]); + rows.push(tempArray); + } + + +data.addRows(rows) + + // Set Options + var options = { + title: "Pie Chart", + }; + // Draw Chart + var chart = new google.visualization.PieChart( + document.getElementById("myChart") + ); + chart.draw(data, options); +} +// End of Pie Chart plot function +//--------------------------------------------------------------------- + + + + +// Start of Bar Chart plot function +function makeBarChart() { + let mydata = getData(); + + let tempdata = []; + tempdata.push(mydata[0].map(String)); + tempdata.push(mydata[1]); + + +mydata = tempdata; + + console.log(mydata) + + let data = new google.visualization.DataTable(); + data.addColumn("string", "Data"); + data.addColumn("number", "Frequency"); + + let rows = []; + + + for (let i = 0; i < mydata[1].length; i++) { + let tempArray = []; + tempArray.push(mydata[0][i]); + tempArray.push(mydata[1][i]); + rows.push(tempArray); + } + + +data.addRows(rows) + // Set Options + let options = { + title: "Bar Chart", + }; + // Draw Chart + let chart = new google.visualization.BarChart( + document.getElementById("myChart") + ); + chart.draw(data, options); +} +// End of Bar graph plot function + + + + +//--------------------------------------------------------------------- +// Start of scatter chart plot function +function makeScatterChart() { + let mydata = getData(); + + let tempdata = []; + tempdata.push(mydata[0].map(String)); + tempdata.push(mydata[1]); + + +mydata = tempdata; + + console.log(mydata) + + let data = new google.visualization.DataTable(); + data.addColumn("string", "Data"); + data.addColumn("number", "Frequency"); + + let rows = []; + + + for (let i = 0; i < mydata[1].length; i++) { + let tempArray = []; + tempArray.push(mydata[0][i]); + tempArray.push(mydata[1][i]); + rows.push(tempArray); + } + + +data.addRows(rows) + + // Set Options + var options = { + title: "Scatter Plot", + }; + // Draw Chart + var chart = new google.visualization.ScatterChart( + document.getElementById("myChart") + ); + chart.draw(data, options); +} +// End of Scatter Chart graph plot function +//-------------------------------------------------------------------- + + + + + + + + + + +// Start of Area chart plot function +function makeAreaChart() { + let mydata = getData(); + + let tempdata = []; + tempdata.push(mydata[0].map(String)); + tempdata.push(mydata[1]); + + +mydata = tempdata; + + console.log(mydata) + + let data = new google.visualization.DataTable(); + data.addColumn("string", "Data"); + data.addColumn("number", "Frequency"); + + let rows = []; + + + for (let i = 0; i < mydata[1].length; i++) { + let tempArray = []; + tempArray.push(mydata[0][i]); + tempArray.push(mydata[1][i]); + rows.push(tempArray); + } + + +data.addRows(rows) + // Set Options + var options = { + title: "Area Graph", + }; + // Draw Chart + var chart = new google.visualization.AreaChart( + document.getElementById("myChart") + ); + chart.draw(data, options); +} +// End of Scatter Chart graph plot function +//-------------------------------------------------------------------- + +google.charts.load("current", { packages: ["corechart"] }); +// Start of Box-Whisker plot function +function makeBoxWhiskerChart() { + let mydata = getData(); + console.log(mydata); + + + let row = []; + + for (let k = 0; k < mydata[0].length; k++) { + let tempArray = []; + for (let i = mydata[1][k]; i > 0; i--) { + tempArray.push(mydata[0][k]); + } + // tempArray.push(freqArray[k]); + row.push(tempArray); + } + let data = google.visualization.arrayToDataTable([["keshav", ...row.flat()]], true); + + // Set Options + let options = { + title: "Box Whisker Plot", + }; + + + // Draw Chart + var chart = new google.visualization.CandlestickChart( + document.getElementById("myChart") + ); + chart.draw(data, options); +} +// End of box-whisker Chart graph plot function + + + + + + + + +//-------------------------------------------------------------------- + +// Start of Table plot code +google.charts.load("current", { packages: ["table"] }); +// Start of Table plot function +function makeTableChart() { + let mydata = getData(); + console.log(mydata); + + let data = new google.visualization.DataTable(); + data.addColumn("number", "Data"); + data.addColumn("number", "Frequency"); + + let rows = []; + + for (let i = 0; i < mydata[1].length; i++) { + let tempArray = []; + tempArray.push(mydata[0][i]); + tempArray.push(mydata[1][i]); + rows.push(tempArray); + } + console.log(rows); + + data.addRows(rows); + + // Set Options + let options = { + title: "Data Frequency Table", + showRowNumber: true, + alternatingRowStyle: true, + explorer: { axis: "horizontal", keepInBounds: true }, + }; + // Draw Chart + var chart = new google.visualization.Table( + document.getElementById("myChart") + ); + chart.draw(data, options); +} +// End of Table Chart graph plot function +//-------------------------------------------------------------------- + +//--------------------------------------------------------------------- +// End of all Charting Functions +//--------------------------------------------------------------------- diff --git a/experiment/simulation/Experiments/Descriptive/skewness/skewness.html b/experiment/simulation/Experiments/Descriptive/skewness/skewness.html new file mode 100644 index 0000000..ff540d5 --- /dev/null +++ b/experiment/simulation/Experiments/Descriptive/skewness/skewness.html @@ -0,0 +1,261 @@ + + + + + + + Skewness | statsVL + + + + + + + + + + + + + + + + +
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+ + diff --git a/experiment/simulation/js/README.md b/experiment/simulation/Scripts/README.md similarity index 100% rename from experiment/simulation/js/README.md rename to experiment/simulation/Scripts/README.md diff --git a/experiment/simulation/Scripts/stats.js b/experiment/simulation/Scripts/stats.js new file mode 100644 index 0000000..c8ad1d4 --- /dev/null +++ b/experiment/simulation/Scripts/stats.js @@ -0,0 +1,2918 @@ +"use strict"; +var _Mathsign = Math.sign, + _MathPI = Math.PI, + _NumberisInteger = Number.isInteger, + _Mathabs = Math.abs, + _Mathexp = Math.exp, + _Mathlog = Math.log, + _Mathsqrt = Math.sqrt, + _Mathpow = Math.pow, + _Mathround = Math.round, + _Mathfloor = Math.floor, + Statistics = function (o, l, u = {}) { + (this.data = void 0), + (this.columns = void 0), + (this.valueMaps = void 0), + (this.storedResults = void 0), + (this.lastUpdated = void 0), + (this.validScales = ["nominal", "ordinal", "interval", "metric"]), + (this.zTable = void 0), + (this.factorials = [ + 1, 1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, + 479001600, 6227020800, 87178291200, 1307674368000, 20922789888000, + 355687428096000, 6402373705728000, 121645100408832000, + 2432902008176640000, 5109094217170944000, + ]), + (this.defaultOptions = { + epsilon: 1e-5, + excludeColumns: ["ID", "id"], + incompleteBetaIterations: 40, + incompleteGammaIterations: 80, + maxBarnardsN: 200, + spougeConstant: 40, + suppressWarnings: !1, + zTableIterations: 25, + }), + (this.init = function (M, S, T = {}) { + for (var C in ("undefined" == typeof M && + this.errorMessage("No data was supplied."), + this.defaultOptions)) { + var N = + "object" == typeof T && + this.has(T, C) && + this.has(this.defaultOptions, C) + ? T[C] + : this.defaultOptions[C]; + Object.defineProperty(this, C, { value: N, writable: !1 }); + } + return "undefined" != typeof M && this.updateData(M, S), this; + }), + (this.updateData = function (M, S) { + this.addData(M), + "object" == typeof S && this.assignValueMap(S), + M.constructor === Array && + "object" != typeof S && + (this.errorMessage( + "It is strongly encouraged to initalise statistics.js with a variable table that defines the scale of measurement of each variable (e.g. nominal, metric.). All variables will be assumed as nominal and subsequent analyses will likely be flawed." + ), + g.apply(this)); + }), + (this.addData = function (M) { + try { + let S = typeof M; + if (("string" != S && "object" != S) || null === M) + throw ( + "Input variable data is neither an object nor a JSON encoded string. The variable type is " + + S + + ". The data could not be properly imported." + ); + return ( + "string" == S && (M = JSON.parse(M)), + (this.data = this.data ? this.data.concat(M) : M), + (this.lastUpdated = Date.now()), + !0 + ); + } catch (S) { + return this.errorMessage(S); + } + }), + (this.addRow = function (M) { + if ("undefined" == typeof M) + return this.errorMessage("Add Row: No data was given."); + let S = "undefined" == typeof this.data ? [] : this.data; + return S.push(M), (this.data = S), (this.lastUpdated = Date.now()), !0; + }), + (this.removeRow = function (M, S = !1) { + if ("undefined" == typeof M) + return this.errorMessage("Remove row: No index was given."); + let T = this.data, + C = this.has(this.columns, "id") || this.has(this.columns, "ID"); + if (S && C) { + let N = -1, + I = 0; + for (; -1 == N && I < T.length; ) + ((this.has(T[I], "id") && T[I].id === S) || + (this.has(T[I], "ID") && T[I].ID === S)) && + (N = I); + T.splice(I, 1); + } else { + if ( + (S && + !C && + this.errorMessage( + 'Remove row: There is no column "id" or "ID". The index ' + + M + + " will be treated as the number of the row, starting at 0." + ), + M > T.length - 1) + ) + return this.errorMessage( + "Remove row: The stored data has only " + + T.length + + " rows and index " + + M + + " is too large. Indexes start at 0." + ); + T.splice(M, 1); + } + return (this.data = T), (this.lastUpdated = Date.now()), !0; + }), + (this.reset = function () { + try { + return ( + (this.data = void 0), + (this.storedResults = void 0), + (this.lastUpdated = Date.now()), + !0 + ); + } catch (M) { + this.errorMessage(M.message); + } + }), + (this.assignValueMap = function (M) { + let S = {}, + T = {}; + for (var C in M) { + if (!this.has(M, C) || -1 < this.excludeColumns.indexOf(C)) continue; + let V, + N = M[C], + I = N; + if ( + ("object" == typeof N && + this.has(N, "scale") && + (this.has(N, "valueMap") && (V = N.valueMap), (I = N.scale)), + "undefined" == typeof V && + ("nominal" === I || "ordinal" === I) && + (V = this.getUniqueValues( + this.data.map((D) => { + return D[C]; + }) + )), + "undefined" != typeof V && (T[C] = V), + -1 < this.validScales.indexOf(I)) + ) + S[C] = I; + else { + S[C] = "nominal"; + let D = + '"' + + I + + '" scale for variable "' + + C + + '" is invalid. It was assumed as nominal. Valid scales of measurement include: ' + + this.validScales.join(", "); + this.errorMessage(D); + } + } + (this.columns = S), (this.valueMaps = T), this.sanitizeColumns(); + }); + var g = function () { + let M = {}, + S = ["th", "st", "nd", "rd"]; + for (var T = 0; T < this.data.length; T++) + if ("object" != typeof this.data[T]) { + let N = T % 100, + I = T + (S[(N - 20) % 10] || S[N] || S[0]); + this.errorMessage( + "The " + I + " row was ignored because it is not an object." + ); + } else + for (var C in Object.keys(this.data[T])) + !C in M && (M[C] = "nominal"); + (this.columns = M), (this.lastUpdated = Date.now()); + }; + (this.sanitizeColumns = function () { + let M = this.columns, + S = this.data; + for (var T = 0; T < S.length; T++) { + let N = S[T]; + for (var C in M) { + let I = N[C], + V = I; + switch (M[C]) { + case "metric": + V = this.isNumeric(I) ? I : NaN; + break; + case "interval": + V = this.isNumeric(I) ? I : NaN; + break; + case "ordinal": + if ("undefined" != typeof this.valueMaps) { + let D = this.valueMaps[C]; + "undefined" != typeof D && (V = D.indexOf(I)); + } + break; + default: + } + S[T][C] = V; + } + } + (this.data = S), (this.lastUpdated = Date.now()); + }), + (this.setScale = function (M, S) { + return "undefined" == typeof S + ? (this.errorMessage( + "This method needs to be called with valid values for both the variable and the scale of measurement to set." + ), + !1) + : this.has(this.columns, M) + ? -1 === this.validScales.indexOf(S) + ? (this.errorMessage( + '"' + + S + + '" is not a valid scale of measurement. Valid scales include: ' + + this.validScales.join(", ") + ), + !1) + : ((this.columns[M] = S), !0) + : (this.errorMessage('There is no variable "' + M + '" defined.'), + !1); + }), + (this.getScale = function (M) { + if ("undefined" != typeof M && this.has(this.columns, M)) + return this.columns[M]; + }), + (this.getValueMap = function (M) { + if ("undefined" != typeof M && this.has(this.columns, M)) + return this.valueMaps[M]; + }), + (this.applyValueMap = function (M, S) { + let T = this.getValueMap(M); + if ("ordinal" === this.getScale(M) && "undefined" != typeof T) + return ( + "undefined" === S && (S = this.getColumn(M)), + S.map((C) => { + return T[C]; + }) + ); + }), + (this.checkLastUpdated = function (M, S) { + return ( + "" !== M && + this.has(this.columns, M) && + "undefined" != typeof M && + "undefined" != typeof S && + ("undefined" == typeof this.storedResults || + "undefined" == typeof this.storedResults[M] || + "undefined" == typeof this.storedResults[M][S] || + "undefined" == typeof this.storedResults[M][S].lastUpdated || + this.storedResults[M][S].lastUpdated < this.lastUpdated) + ); + }), + (this.updateStatistics = function (M, S, T) { + if ( + "undefined" != typeof M && + "undefined" != typeof S && + "undefined" != typeof T + ) { + var C = { value: T, lastUpdated: Date.now() }; + "undefined" == typeof this.storedResults + ? (this.storedResults = { column: { parameter: C } }) + : "undefined" == typeof this.storedResults[M] + ? (this.storedResults[M] = { parameter: C }) + : (this.storedResults[M][S] = C); + } + }), + (this.getStatistics = function (M, S) { + return "undefined" != typeof M && + "undefined" != typeof S && + this.has(this.columns, M) && + "undefined" != typeof this.storedResults[M][S].value + ? this.storedResults[M][S].value + : void 0; + }), + (this.getColumn = function (M) { + return "undefined" == typeof M + ? this.errorMessage("Get column: No column to sort was specified.") + : this.has(this.columns, M) + ? this.data.map((S) => { + return S[M]; + }) + : this.errorMessage( + 'Get column: The column "' + M + '" was not found.' + ); + }), + (this.sortColumn = function (M, S = "asc") { + return "undefined" == typeof M + ? this.errorMessage("Sort column: No column to sort was specified.") + : this.has(this.columns, M) + ? this.sort(this.getColumn(M), S) + : this.errorMessage( + 'Sort column: The column "' + M + '" was not found.' + ); + }), + (this.sortDataByColumn = function ( + M, + { data: S = this.data, order: T = "asc", changeOriginal: C = !1 } = {} + ) { + return S !== this.data || + ("undefined" != typeof M && this.has(this.columns, M)) + ? S === this.data || this.has(S[0], M) + ? (function (N, I, V, D) { + return I.sort(function (F, P) { + return D.isNumeric(F[N]) && D.isNumeric(P[N]) + ? ("asc" === V ? 1 : -1) * (F[N] - P[N]) + : 0; + }); + })(M, C ? S : this.deepCopy(S), T, this) + : this.errorMessage( + 'Sort data by column: The column "' + M + '" does not exist.' + ) + : this.errorMessage( + "Sort data by column: No column was specified or this column does not exist." + ); + }), + (this.sort = function (M, S = "asc") { + return "undefined" == typeof M + ? this.errorMessage("Sort: No values given.") + : M.constructor !== Array || 0 === M.length + ? this.errorMessage( + "Sort: No array or an empty array of values was given." + ) + : (function (T, C, N) { + return T.sort((I, V) => { + return C.isNumeric(I) && C.isNumeric(V) + ? ("asc" === N ? 1 : -1) * (I - V) + : 0; + }); + })(this.deepCopy(M), this, S); + }), + (this.getUniqueValues = function (M) { + if ("undefined" == typeof M) + return this.errorMessage("Get unique values: No values given."); + let S = this.validateInput(M, "nominal", "get unique values"); + return !1 === S + ? void 0 + : this.sort( + S.data.filter((T, C) => { + return S.data.indexOf(T) === C; + }) + ); + }), + (this.reduceToPairs = function (M, S) { + if ("undefined" == typeof S || "undefined" == typeof M) + return this.errorMessage( + "This method requires two variables to be compared." + ); + let T = this.validateInput(M, "nominal"); + if (!1 !== T) { + let C = this.validateInput(S, "nominal"); + if (!1 !== C) { + let N = T.length >= C.length ? T.length : C.length, + I = [], + V = [], + D = [], + F = "string" == typeof M ? M : "first", + P = "string" == typeof S ? S : "second"; + for (var B = 0; B < N; B++) { + let U = T.data[B], + O = C.data[B]; + if ( + void 0 !== typeof U && + void 0 !== typeof O && + !isNaN(U) && + !isNaN(O) + ) { + I.push(U), V.push(O); + let R = {}; + (R[F] = U), (R[P] = O), D.push(R); + } + } + return { + length: I.length, + missings: N - I.length, + valuesFirst: I, + valuesSecond: V, + valuesCombined: D, + }; + } + } + }), + (this.validateInput = function (M, S = "metric", T = "") { + let C = {}; + if ( + "string" != typeof M && + (M.constructor !== Array || + (M.constructor === Array && 0 == M.length)) + ) + return ( + this.errorMessage( + "No properly formatted data was supplied. Specify a column by its name (string) or supply an array of values." + ), + !1 + ); + if ("string" == typeof M && "" !== M) + (C.data = this.getColumn(M)), + (C.scale = this.getScale(M)), + (C.length = C.data.length); + else if (M.constructor === Array && 0 < M.length) { + if (!1 === this.validateData(M, S)) return !1; + (C.data = M), (C.scale = S), (C.length = C.data.length); + } else return !1; + if (0 == C.length) + return ( + this.errorMessage( + "The supplied data or the data of the supplied column contains no values." + ), + !1 + ); + if (!h.apply(this, [C.scale, S])) { + let N = this.validScales + .slice(this.validScales.indexOf(S)) + .join(", "), + I = + "" === T + ? "This statistical method" + : T.charAt(0).toUpperCase() + T.slice(1); + return ( + (I += + " is only defined for these scales of measurement: " + + N + + ". The scale of the supplied data is " + + C.scale + + "."), + this.errorMessage(I), + !1 + ); + } + return C; + }), + (this.validateData = function (M, S = "metric") { + if ("undefined" == typeof M || o.constructor !== Array) + return this.errorMessage( + "Validate data: Specify an array storing the values to be validated." + ); + if ("nominal" === S) return !0; + for (var T = 0; T < M.length; T++) + if (!this.isNumeric(M[T])) + return ( + this.errorMessage( + "Validate data: The supplied data contains non-numeric values: " + + M[T] + + " at index " + + T + ), + !1 + ); + return !0; + }), + (this.isNumeric = function (M) { + return "undefined" == typeof M + ? void 0 + : !Array.isArray(M) && !isNaN(parseFloat(M)) && isFinite(M); + }); + var h = function (M, S) { + return this.validScales.indexOf(M) >= this.validScales.indexOf(S); + }; + return ( + (this.errorMessage = function (M) { + if (!this.suppressWarnings) + try { + throw new TypeError("string" == typeof M ? M : M.message); + } catch (S) { + console.error("statistics.js: " + S.message); + } + }), + (this.has = function (M, S) { + var T = Object.prototype.hasOwnProperty; + return T.call(M, S); + }), + (this.deepCopy = function (M) { + let S = Array.isArray(M) ? [] : {}; + for (let T in M) { + let C = M[T]; + S[T] = "object" == typeof C ? this.deepCopy(C) : C; + } + return S; + }), + (Statistics.prototype.assignRanks = function ( + M, + { + data: S = this.data, + order: T = "asc", + handleTiedValues: C = "mean", + returnFrequencies: N = !1, + } = {} + ) { + if ("undefined" == typeof M) + return this.errorMessage( + "Assign ranks: You need to specify a column to be ranked." + ); + let I = this.deepCopy( + this.sortDataByColumn(M, { data: S, order: T, changeOriginal: !1 }) + ), + V = {}; + for (var D = 0; D < I.length; D++) { + let U = I[D][M]; + V[U] = V[U] ? V[U] + 1 : 1; + } + let P = 0, + B = []; + for (var D = 0; D < I.length; D++) { + let U = I[D][M], + O = D + 1; + 1 === V[U] + ? (P = 0) + : "mean" === C + ? (P++, (O = D + V[U] / 2 - P + 1.5)) + : "random" === C && + (0 === B.length && + (B = Array.from(Array(V[U]), (G, A) => A + D + 1)), + (O = B[_Mathfloor(Math.random() * B.length)]), + B.splice(B.indexOf(O), 1)), + P == V[U] && (P = 0), + (I[D]["rank-" + M] = O); + } + return N ? { data: I, frequencies: V } : I; + }), + (Statistics.prototype.contingencyTable = function (M, S) { + if ("undefined" == typeof S) + return this.errorMessage( + "A contingency table requires two columns to analyze." + ); + if (!this.has(this.columns, M)) + return this.errorMessage('There is no variable "' + M + '" defined.'); + if (!this.has(this.columns, S)) + return this.errorMessage('There is no variable "' + S + '" defined.'); + let T = this.getScale(M), + C = this.getScale(S); + if ( + ("nominal" !== T && "ordinal" !== T) || + ("nominal" !== C && "ordinal" !== C) + ) + return this.errorMessage( + "Both variables need to be nominal for. They are " + + T + + " and " + + C + + "." + ); + let N = this.getValueMap(M), + I = this.getValueMap(S); + if ("undefined" == typeof N || "undefined" == typeof I) + return this.errorMessage( + "Contingency table: There are no valid values." + ); + let V = { total: { total: 0 } }, + D = this.data; + for (var F = 0; F < D.length; F++) { + let B = D[F][M], + U = D[F][S]; + "undefined" == typeof V[B] && (V[B] = { total: 0 }), + (V[B][U] = "undefined" == typeof V[B][U] ? 1 : V[B][U] + 1), + (V.total[B] = + "undefined" == typeof V.total[B] ? 1 : V.total[B] + 1), + (V.total[U] = + "undefined" == typeof V.total[U] ? 1 : V.total[U] + 1), + V[B].total++, + V.total.total++; + } + let P = { detailled: V }; + return ( + 2 >= N.length && + 2 >= I.length && + ((P.a = V[N[0]][I[0]] || 0), + (P.b = V[N[0]][I[1]] || 0), + (P.c = V[N[1]][I[0]] || 0), + (P.d = V[N[1]][I[1]] || 0)), + P + ); + }), + (this.showData = function (M) { + if ("string" == typeof M && this.has(this.columns, M)) + "ordinal" === this.getScale(M) + ? console.log(this.applyValueMap(M)) + : console.log(this.getColumn(M)); + else if ("undefined" == typeof M) { + let C = this.valueMaps, + N = this.data; + if ("undefined" != typeof C) + for (var S in C) + if ( + this.has(C, S) && + "undefined" != typeof C[S] && + "ordinal" === this.getScale(S) + ) + for (var T = 0; T < N.length; T++) N[T][S] = C[S][N[T][S]]; + console.table(N); + } else console.log(M); + }), + (this.scatterPlot = function ( + M = this.data, + { + canvas: S = null, + xAxis: T = null, + yAxis: C = null, + width: N = null, + height: I = null, + dotRadius: V = 4, + showGrid: D = !1, + minNumberXMarks: F = 8, + minNumberYMarks: P = 8, + background: B = "#FFFFFF", + dotColor: U = "#000000", + gridColor: O = "#CCCCCC", + axisColor: R = "#000000", + } = {} + ) { + if ("undefined" == typeof M) + return this.errorMessage("Scatter plot: No data given."); + if (M.constructor !== Array || 0 === M.length) + return this.errorMessage( + "Scotter plot: Data is not an array or empty." + ); + if (0 >= F || 0 >= P) + return this.errorMessage( + "Scotter plot: The number of line to plot must be larger than 0." + ); + let G = typeof M[0]; + if ("object" == G && (!T || !C)) + return this.errorMessage( + "Scatter plot: The variables for the x and y axes need to be supplied." + ); + let A = {}, + L = Infinity, + E = -Infinity, + W = Infinity, + K = -Infinity, + Y = 0; + for (var X = 0; X < M.length; X++) { + let me, + ce, + ue = []; + if ("number" == G || M[0].constructor === Array) { + if (!this.isNumeric(M[X])) continue; + (me = X), (ce = M[X]); + } else if ("object" == G) { + if ( + !this.has(M[X], T) || + !this.has(M[X], C) || + !this.isNumeric(M[X][T]) || + !this.isNumeric(M[X][C]) + ) + continue; + (me = M[X][T]), (ce = M[X][C]); + } + this.has(A, me) ? (ue = A[me]) : (Y += 1), + ue.push(ce), + (A[me] = ue), + me > E && (E = me), + me < L && (L = me), + ce > K && (K = ce), + ce < W && (W = ce); + } + 0.1 > L / E && (L = 0), + 0.1 > W / K && (W = 0), + null === S && (S = document.createElement("canvas")); + let Q = S.getContext("2d"), + H = N ? 0.1 * N : 0.1 * (E - L); + for (var J in ((N = (N ? N - 2 * H : E - L) + 2 * V), + (I = (I ? I - 2 * H : K - W) + 2 * V), + 400 > N && (N = 400), + 400 > I && (I = 400), + 40 > H && (H = 40), + (S.width = N), + (S.height = I), + (Q.fillStyle = "transparent"), + Q.fillRect(0, 0, N, I), + (Q.fillStyle = U), + A)) + for (var Z = 0; Z < A[J].length; Z++) { + let ue = ((J - L) * (N - V)) / (E - L), + me = I - ((A[J][Z] - W) * (I - V)) / (K - W) - V; + Q.fillRect(ue, me, V, V); + } + const $ = function (ue, me, ce, pe, ge, fe = R) { + (ue.strokeStyle = fe), + ue.beginPath(), + ue.moveTo(_Mathfloor(me), _Mathfloor(ce)), + ue.lineTo(_Mathfloor(pe), _Mathfloor(ge)), + ue.stroke(), + (ue.strokeStyle = R); + }; + let _ = document.createElement("canvas"), + ee = _.getContext("2d"), + ae = N + 2 * H - 2 * V, + te = I + 2 * H - 2 * V; + (_.width = ae), + (_.height = te), + (ee.fillStyle = B), + ee.fillRect(0, 0, ae, te), + (ee.fillStyle = R), + (ee.font = 0.2 * H + "px Arial"), + (ee.textAlign = "center"), + (ee.textBaseline = "middle"); + let ie = _Mathround((E - L) / F), + ne = + 1 <= ie ? _Mathpow(10, parseInt(ie).toString().length - 1) : 0.01; + ie = _Mathround(ie / ne) * ne; + let se = 0, + re = 0, + oe = 0; + if (0 < ie) + for (var X = 0; X < F || (se <= E && re <= N - 2 * H); X++) + (se = _Mathround(L / ne) * ne + X * ie), + (re = 0.5 * H + ((se - L) * (N - V)) / (E - L)), + re > ae - 0.5 * H || + re < 0.5 * H || + ($(ee, re, te - 0.4 * H, re, te - 0.6 * H), + D && $(ee, re, te - 0.6 * H, re, 0.5 * H, O), + ee.fillText(se, re, te - 0.2 * H)); + let de = (K - W) / P, + le = + 1 <= de ? _Mathpow(10, parseInt(de).toString().length - 1) : 0.01; + if (((de = _Mathround(de / le) * le), (se = 0), 0 < de)) + for (var X = 0; X < P || (se <= K && oe <= I - 2 * H); X++) + (se = _Mathround(W / le) * le + X * de), + (oe = 1.5 * H - 2 * V + I - ((se - W) * (I - V)) / (K - W)), + oe > te - 0.5 * H || + oe < 0.5 * H || + ($(ee, 0.4 * H, oe, 0.6 * H, oe), + D && $(ee, 0.6 * H, oe, N + H - 2 * V, oe, O), + ee.fillText(se, 0.2 * H, oe)); + return ( + $(ee, 0.5 * H, te - 0.5 * H, ae - H, te - 0.5 * H), + $(ee, 0.5 * H, te - 0.5 * H, 0.5 * H, 0.5 * H), + ee.drawImage(S, 0.5 * H - 0.5 * V, 1.5 * H - 1.5 * V), + Q.drawImage(_, 0, 0, N, I), + S + ); + }), + this.init(o, l, u) + ); + }; +"undefined" == typeof exports + ? (window.Statistics = Statistics) + : ("undefined" != typeof module && + module.exports && + (exports = module.exports = Statistics), + (exports.Statistics = Statistics)), + (Statistics.prototype.mean = function (o) { + return "undefined" == typeof o ? void 0 : this.arithmeticMean(o); + }), + (Statistics.prototype.arithmeticMean = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Arithmetic mean: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "mean")) + return this.getStatistics(o, "mean"); + let l = this.validateInput(o, "interval", "arithmetic mean"); + if (!1 !== l) { + let u = this.sumExact(l.data) / l.length; + return "string" == typeof o && this.updateStatistics(o, "mean", u), u; + } + }), + (Statistics.prototype.geometricMean = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Geometric mean: No data was supplied."); + if ( + "string" == typeof o && + !1 === this.checkLastUpdated(o, "geometricMean") + ) + return this.getStatistics(o, "geometricMean"); + let l = this.validateInput(o, "metric", "geometric mean"); + if (!1 === l) return; + let u, + g = !1; + return ( + (u = l.data.reduce((h, M) => { + return 0 < M ? h * M : ((g = !0), h); + }, 1)), + !g && 0 < l.length + ? (u = _Mathpow(u, 1 / l.length)) + : (this.errorMessage( + "Geometric mean is not defined because the data contains non-positive values." + ), + (u = void 0)), + "string" == typeof o && this.updateStatistics(o, "geometricMean", u), + u + ); + }), + (Statistics.prototype.harmonicMean = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Harmonic mean: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "harmonicMean")) + return this.getStatistics(o, "harmonicMean"); + let l = this.validateInput(o, "metric", "harmonicMean"); + if (!1 === l) return; + let u = !1, + g = !1, + h = l.data.reduce((M, S) => { + return 0 > S ? ((g = !0), 0) : 0 === S ? ((u = !0), 0) : M + 1 / S; + }, 0); + return ( + (h = u ? 0 : l.length / h), + (h = g ? void 0 : h), + "string" == typeof o && this.updateStatistics(o, "harmonicMean", h), + h + ); + }), + (Statistics.prototype.rootMeanSquare = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Root mean square: No data was supplied."); + if ( + "string" == typeof o && + !1 === this.checkLastUpdated(o, "rootMeanSquare") + ) + return this.getStatistics(o, "rootMeanSquare"); + let l = this.validateInput(o, "interval", "root mean square"); + if (!1 !== l) { + let u = _Mathsqrt( + l.data.reduce((g, h) => { + return g + h * h; + }, 0) / l.length + ); + return ( + "string" == typeof o && this.updateStatistics(o, "rootMeanSquare", u), u + ); + } + }), + (Statistics.prototype.cubicMean = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Cubic mean: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "cubicMean")) + return this.getStatistics(o, "cubicMean"); + let l = this.validateInput(o, "interval", "cubic mean"); + if (!1 === l) return; + let u = l.data.reduce((g, h) => { + return g + h * h * h; + }, 0); + return ( + (u = 0 <= u && 0 < l.length ? _Mathpow(u / l.length, 1 / 3) : void 0), + "string" == typeof o && this.updateStatistics(o, "cubicMean", u), + u + ); + }), + (Statistics.prototype.winsorisedMean = function (o, l = 0.2) { + if ("undefined" == typeof o) + return this.errorMessage("Winsorised mean: No data was supplied."); + if (0 > l || 0.5 < l) + return this.errorMessage( + "winsorisedMean should be called with a percentage value within the range of [0, 0.5]." + ); + if (0.5 === l) return this.median(o); + let u = this.validateInput(o, "interval", "Winsorised (truncated) mean"); + if (!1 === u) return; + let g = _Mathfloor(u.length * l), + h = this.sort(u.data).slice(g, u.length - g); + h = Array(g) + .fill(h[0]) + .concat(h) + .concat(Array(g).fill(h[h.length - 1])); + let M = this.sumExact(h) / u.length; + return M; + }), + (Statistics.prototype.gastwirthCohenMean = function ( + o, + { alpha: l = 0.25, lambda: u = 0.25 } = {} + ) { + if ("undefined" == typeof o) + return this.errorMessage("Gastwirth-Cohen mean: No data was supplied."); + let g = this.validateInput(o, "ordinal", "Gastwirth-Cohen Mean"); + if (!1 === g) return; + if (!this.isNumeric(l) || 0 > l || 0.5 < l) + return this.errorMessage( + "Gastwirth-Cohen mean should be called with an alpha value within the range of [0, 0.5]." + ); + if (!this.isNumeric(u) || 0 > u || 0.5 < u) + return this.errorMessage( + "Gastwirth-Cohen mean should be called with a lambda value within the range of [0, 0.5]." + ); + let T, + h = this.quantile(o, l), + M = this.quantile(o, 1 - l), + S = this.median(o); + return ( + "undefined" != typeof h && + "undefined" != typeof M && + "undefined" != typeof S && + (T = u * (h + M) + (1 - 2 * u) * S), + T + ); + }), + (Statistics.prototype.midRange = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Mid-range: No data was supplied."); + let l = this.validateInput(o, "interval", "mid-range"); + if (!1 !== l) { + let u = this.minimum(o), + g = this.maximum(o); + return "undefined" != typeof u && "undefined" != typeof g + ? 0.5 * (u + g) + : void 0; + } + }), + (Statistics.prototype.median = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Median: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "median")) + return this.getStatistics(o, "median"); + let l = this.quantile(o, 0.5); + return "string" == typeof o && this.updateStatistics(o, "median", l), l; + }), + (Statistics.prototype.mode = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Mode: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "mode")) + return this.getStatistics(o, "mode"); + let l = this.validateInput(o, "nominal", "mode"); + if (!1 === l) return; + let u = l.data, + g = [], + h = [u[0]], + M = 1; + for (var S = 1; S < l.length; S++) { + let T = u[S]; + (g[T] = null == g[T] ? 1 : g[T] + 1), + g[T] > M ? ((h = [T]), (M = g[T])) : g[T] === M && h.push(T); + } + return ( + 1 === h.length && (h = h[0]), + "string" == typeof o && this.updateStatistics(o, "mode", h), + h + ); + }), + (Statistics.prototype.minimum = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Minimum: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "minimum")) + return this.getStatistics(o, "minimum"); + let l = this.validateInput(o, "ordinal", "minimum"); + if (!1 === l) return; + let u = Infinity, + g = l.length; + for (; g--; ) l.data[g] < u && (u = l.data[g]); + return "string" == typeof o && this.updateStatistics(o, "minimum", u), u; + }), + (Statistics.prototype.maximum = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Maximum: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "maximum")) + return this.getStatistics(o, "maximum"); + let l = this.validateInput(o, "ordinal", "maximum"); + if (!1 === l) return; + let u = -Infinity, + g = l.length; + for (; g--; ) l.data[g] > u && (u = l.data[g]); + return "string" == typeof o && this.updateStatistics(o, "maximum", u), u; + }), + (Statistics.prototype.range = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Range: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "range")) + return this.getStatistics(o, "range"); + let l, u; + if ("string" == typeof o && "nominal" === this.getScale(o)) { + if (((l = this.validateInput(o, "nominal", "range")), !1 === l)) return; + u = this.getUniqueValues(l.data); + } else { + if (((l = this.validateInput(o, "ordinal", "range")), !1 === l)) return; + let g = this.sort(l.data); + u = g[l.length - 1] - g[0]; + } + return "string" == typeof o && this.updateStatistics(o, "range", u), u; + }), + (Statistics.prototype.variance = function (o, l = !0) { + if ("undefined" == typeof o) + return this.errorMessage("Variance: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "variance")) + return this.getStatistics(o, "variance"); + let u = this.validateInput(o, "interval", "variance"); + if (!1 === u) return; + if (2 > u.length) + return this.errorMessage( + "The data supplied to compute variance needs to contain at least two datasets." + ); + let g = 0; + if (this.isNumeric(l)) { + for (var h = 0; h < u.length; h++) g += _Mathpow(l - u.data[h], 2); + g /= u.length - 1; + } else { + let M = 0, + S = 0; + for (var h = 0; h < u.length; h++) { + M += 1; + let T = u.data[h] - S; + S += T / M; + let C = u.data[h] - S; + g += T * C; + } + l && 1 < M ? (g /= M - 1) : !l && 0 < M ? (g /= M) : (g = void 0); + } + return "string" == typeof o && this.updateStatistics(o, "variance", g), g; + }), + (Statistics.prototype.standardDeviation = function (o, l = !0) { + if ("undefined" == typeof o) + return this.errorMessage("Standard deviation: No data was supplied."); + if ( + "string" == typeof o && + !1 === this.checkLastUpdated(o, "standardDeviation") + ) + return this.getStatistics(o, "standardDeviation"); + let u = this.validateInput(o, "interval", "standard deviation"); + if (!1 !== u) { + if (2 > u.length) + return this.errorMessage( + "The data supplied to compute standardDeviation needs to contain at least two datasets." + ); + let g = _Mathsqrt(this.variance(o, l)); + return ( + "string" == typeof o && + this.updateStatistics(o, "standardDeviation", g), + g + ); + } + }), + (Statistics.prototype.coefficientOfVariation = function (o, l) { + if ("undefined" == typeof o) + return this.errorMessage( + "Coefficient of variation: No data was supplied." + ); + let u = this.validateInput(o, "interval", "coefficient of variation"); + if (!1 !== u) { + let g = this.standardDeviation(u.data), + h = "undefined" == typeof l ? this.mean(u.data) : l; + return "undefined" != typeof g && "undefined" != typeof h && 0 !== h + ? g / h + : void 0; + } + }), + (Statistics.prototype.indexOfDispersion = function (o, l) { + if ("undefined" == typeof o) + return this.errorMessage("Index of dispersion: No data was supplied."); + let u = this.validateInput(o, "interval", "coefficient of variation"); + if (!1 !== u) { + let g = this.variance(u.data), + h = "undefined" == typeof l ? this.mean(u.data) : l; + return "undefined" != typeof g && "undefined" != typeof h && 0 !== h + ? g / h + : void 0; + } + }), + (Statistics.prototype.geometricStandardDeviation = function (o) { + if ("undefined" == typeof o) + return this.errorMessage( + "Geomtric standard deviation: No data was supplied." + ); + if ( + "string" == typeof o && + !1 === this.checkLastUpdated(o, "geometricStandardDeviation") + ) + return this.getStatistics(o, "geometricStandardDeviation"); + let l = this.validateInput(o, "interval", "geometric standard deviation"); + if (!1 === l) return; + let u = l.length, + g = this.geometricMean(o), + h = 0, + M = !1; + for (var S = 0; S < u; S++) + 0 >= l.data[S] ? (M = !0) : (h += _Mathpow(_Mathlog(l.data[S] / g), 2)); + return ( + (h = M ? void 0 : _Mathexp(_Mathsqrt(h / u))), + "string" == typeof o && + this.updateStatistics(o, "geometricStandardDeviation", h), + h + ); + }), + (Statistics.prototype.medianAbsoluteDeviation = function (o) { + if ("undefined" == typeof o) + return this.errorMessage( + "Median absolute deviation: No data was supplied." + ); + let l = this.validateInput(o, "interval", "median absolute deviation"); + if (!1 === l) return; + let u = l.data, + g = this.median(u); + if ("undefined" != typeof g) + return ( + (u = u.map((h) => { + return _Mathabs(h - g); + })), + this.median(u) + ); + }), + (Statistics.prototype.cumulativeFrequency = function (o, l) { + if ("undefined" == typeof o) + return this.errorMessage("Cumulative frequency: No data was supplied."); + if ("undefined" == typeof l || !this.isNumeric(l)) + return void this.errorMessage( + "You need to specify a boundary for the cumulative frequency analysis that is either an integer or a floating point number." + ); + let u = this.validateInput(o, "ordinal", "cumulative frequency analysis"); + if (!1 !== u) { + let g = this.sort(u.data), + h = 0; + for (; l >= g[h]; ) h++; + return h; + } + }), + (Statistics.prototype.frequencies = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Frequencies: No data was supplied."); + let l = this.validateInput(o, "nominal", "frequencies"); + if (!1 === l) return; + let u = l.data, + g = {}, + h = []; + for (var M = 0; M < l.length; M++) { + let S = u[M]; + null == g[S] ? (h.push(S), (g[S] = 1)) : g[S]++; + } + return ( + (h = h.sort((S, T) => { + return g[T] - g[S]; + })), + (h = h.map((S) => { + return { value: S, absolute: g[S], relative: g[S] / l.length }; + })), + h + ); + }), + (Statistics.prototype.quantile = function (o, l = 0.5) { + if ("undefined" == typeof o) + return this.errorMessage("Quantile: No data was supplied."); + if (!this.isNumeric(l) || 0 > l || 1 < l) + return this.errorMessage( + "Quantiles should be called with a percentage value within the range of [0, 1]." + ); + let u = this.validateInput(o, "ordinal", "quantile"); + if (!1 !== u) { + let g = l * u.length, + h = this.sort(u.data); + return 0 == g % 1 ? 0.5 * (h[g] + h[g - 1]) : h[_Mathfloor(g)]; + } + }), + (Statistics.prototype.quartiles = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Quartiles: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "quartiles")) + return this.getStatistics(o, "quartiles"); + let l = [this.quantile(o, 0.25), this.quantile(o, 0.75)]; + return "undefined" != typeof l[0] && "undefined" != typeof l[1] + ? (this.updateStatistics(o, "quartiles", l), l) + : void 0; + }), + (Statistics.prototype.interQuartileRange = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Interquartile range: No data was supplied."); + let l = this.validateInput(o, "interval", "interquartile range"); + if (!1 !== l) { + let u = this.quartiles(l.data); + return "undefined" == typeof u ? void 0 : u[1] - u[0]; + } + }), + (Statistics.prototype.skewness = function (o, l = !1) { + if ("undefined" == typeof o) + return this.errorMessage("Skewness: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "skewness")) + return this.getStatistics(o, "skewness"); + let u = this.validateInput(o, "ordinal", "skewness"); + if (!1 === u) return; + if (2 > u.length) return; + let g = this.mean(o), + h = this.standardDeviation(o, !1), + M = 0; + for (var S = 0; S < u.length; S++) M += _Mathpow(u.data[S] - g, 3); + let T = M / _Mathpow(h, 3); + return ( + (T *= l ? u.length / ((u.length - 1) * (u.length - 2)) : 1 / u.length), + "string" == typeof o && this.updateStatistics(o, "skewness", T), + T + ); + }), + (Statistics.prototype.kurtosis = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Kurtosis: No data was supplied."); + if ("string" == typeof o && !1 === this.checkLastUpdated(o, "kurtosis")) + return this.getStatistics(o, "kurtosis"); + let l = this.validateInput(o, "ordinal", "kurtosis"); + if (!1 === l) return; + if (2 > l.length) return; + let u = this.mean(o), + g = this.standardDeviation(o), + h = 0; + for (var M = 0; M < l.length; M++) h += _Mathpow(l.data[M] - u, 4); + let S = h / (l.length * _Mathpow(g, 4)); + return "string" == typeof o && this.updateStatistics(o, "kurtosis", S), S; + }), + (Statistics.prototype.excessKurtosis = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Excess kurtosis: No data was supplied."); + let l = this.kurtosis(o); + return "undefined" == typeof l ? void 0 : l - 3; + }), + (Statistics.prototype.sum = function (o) { + return "undefined" == typeof o + ? this.errorMessage("Sum: No data given.") + : ("string" == typeof o && + this.has(this.columns, o) && + (o = this.getColumn(o)), + 0 == o.length + ? void 0 + : o.reduce((l, u) => { + return this.isNumeric(u) ? l + u : l; + }, 0)); + }), + (Statistics.prototype.sumExact = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Sum exact: No data given."); + if ( + ("string" == typeof o && + this.has(this.columns, o) && + (o = this.getColumn(o)), + 0 == o.length) + ) + return; + let l = 0, + u = 0; + for (var g = 0; g < o.length; g++) { + let h = this.isNumeric(o[g]) ? o[g] : 0; + h -= u; + let M = l + h; + (u = M - l - h), (l = M); + } + return l; + }), + (Statistics.prototype.product = function (o) { + return "undefined" == typeof o + ? this.errorMessage("Product: No data given.") + : ("string" == typeof o && + this.has(this.columns, o) && + (o = this.getColumn(o)), + 0 === o.length + ? 1 + : o.reduce((l, u) => { + return this.isNumeric(u) ? l * u : l; + }, 1)); + }), + (Statistics.prototype.factorial = function (o) { + return this.isNumeric(o) && !_NumberisInteger(o) + ? this.gamma(o) + : "undefined" == typeof this.factorials[o] + ? this.computeFactorial(o) + : this.factorials[o]; + }), + (Statistics.prototype.computeFactorial = function (o) { + if ("undefined" == typeof o || !this.isNumeric(o) || 0 > o) return; + if ("undefined" != typeof this.factorials[o]) return this.factorials[o]; + if (!_NumberisInteger(o)) return this.gamma(o); + let l = 1, + u = 1; + for (; o > u; ) + u++, + (l *= u), + "undefined" == typeof this.factorials[u] && (this.factorials[u] = l); + return l; + }), + (Statistics.prototype.binomialCoefficient = function (o = 1, l = 1) { + if (o < l || 0 > l) + return this.errorMessage( + "The binomial coefficient is only defined for n and k with n \u2265 k \u2265 0. N is " + + o + + " and k is " + + l + + "." + ); + if (!_NumberisInteger(o) || !_NumberisInteger(l)) + return this.errorMessage( + "The binomial coefficient is only defined for integers n and k." + ); + let u = []; + for (var g = 1; g <= l; g++) u.push((o + 1 - g) / g); + return this.product(u); + }), + (Statistics.prototype.gamma = function (o, l = !1) { + return "undefined" == typeof o || !this.isNumeric(o) || 0 > o + ? void 0 + : _NumberisInteger(o) && "undefined" != typeof this.factorials[o - 1] + ? this.factorials[o - 1] + : l + ? this.gammaSpouge(o) + : this.gammaStirling(o); + }), + (Statistics.prototype.gammaSpouge = function (o) { + if ("undefined" == typeof o || !this.isNumeric(o) || 0 > o) return; + if (_NumberisInteger(o) && "undefined" != typeof this.factorials[o - 1]) + return this.factorials[o - 1]; + const l = this.spougeConstant; + let u = _Mathpow(o + l, o + 0.5), + g = 1, + h = 0, + M = _Mathsqrt(2 * _MathPI); + (u *= _Mathexp(-o - l)), (u /= o); + for (var S = 1; S < l; S++) + o++, + (h = _Mathpow(l - S, S - 0.5)), + (h *= _Mathexp(l - S)), + (h /= g), + (M += h / o), + (g *= -S); + return M * u; + }), + (Statistics.prototype.gammaStirling = function (o) { + if ("undefined" == typeof o || !this.isNumeric(o) || 0 > o) return; + if (_NumberisInteger(o) && "undefined" != typeof this.factorials[o - 1]) + return this.factorials[o - 1]; + let g = 1 / (10 * o); + return ( + (g = 1 / (12 * o - g)), + (g = (g + o) * 0.36787944117144233), + (g = _Mathpow(g, o)), + (g *= _Mathsqrt(6.283185307179586 / o)), + g + ); + }), + (Statistics.prototype.incompleteGamma = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "The incomplete lower gamma function is only defined for two numeric variables s and x." + ); + if (!this.isNumeric(o) || !this.isNumeric(l)) + return this.errorMessage( + "The incomplete lower gamma function is only defined for numeric variables s and x." + ); + if (0 > l) + return this.errorMessage( + "The incomplete lower gamma function is defined for x > 0." + ); + let u = this.incompleteGammaIterations, + g = 1; + for (var h = 0; h < u; h++) { + let M = u - h; + g = l + (M - o) / (1 + M / g); + } + return this.gamma(o, !0) - (_Mathexp(-l) * _Mathpow(l, o)) / g; + }), + (Statistics.prototype.regularisedGamma = function (o, l) { + return "undefined" == typeof l + ? this.errorMessage( + "The regularised lower gamma function is only defined for two numeric variables s and x." + ) + : this.isNumeric(o) && this.isNumeric(l) + ? 0 > l + ? this.errorMessage( + "The regularised lower gamma function is defined for x > 0." + ) + : this.incompleteGamma(o, l) / this.gamma(o, !0) + : this.errorMessage( + "The regularised lower gamma function is only defined for numeric variables s and x." + ); + }), + (Statistics.prototype.beta = function (o, l) { + return "undefined" == typeof l + ? this.errorMessage( + "The beta function is only defined for two numeric variables a and b." + ) + : this.isNumeric(o) && this.isNumeric(l) + ? 0 >= o || 0 >= l + ? this.errorMessage( + "The beta function is defined for a and b with a > 0 and b > 0." + ) + : _NumberisInteger(o) && _NumberisInteger(l) && 0 < o && 0 < l + ? (this.factorial(o - 1) * this.factorial(l - 1)) / + this.factorial(o + l - 1) + : (this.gamma(o, !0) * this.gamma(l, !0)) / this.gamma(o + l, !0) + : this.errorMessage( + "The beta function is only defined for numeric variables a and b." + ); + }), + (Statistics.prototype.incompleteBeta = function (o, l, u) { + if ("undefined" == typeof u) + return this.errorMessage( + "The incomplete beta function is only defined for two numeric variables a and b." + ); + if (!this.isNumeric(o) || !this.isNumeric(l) || !this.isNumeric(u)) + return this.errorMessage( + "The incomplete beta function is only defined for numeric variables x, a and b." + ); + if (0 > o || 1 < o) + return this.errorMessage( + "The incomplete beta function is defined for x \u2265 0 and x \u2266 1." + ); + if (0 >= l || 0 >= u) + return this.errorMessage( + "The incomplete beta function is defined for a and b with a > 0 and b > 0." + ); + if (1 == o) return this.beta(l, u); + var g = function (N, I, V, D) { + if (0 == N % 2) { + let F = 0.5 * N; + return (F * (V - F) * D) / ((I + 2 * F - 1) * (I + 2 * F)); + } + let F = 0.5 * N - 0.5; + return (-(I + F) * (I + V + F) * D) / ((I + 2 * F) * (I + 2 * F + 1)); + }; + let h = (_Mathpow(o, l) * _Mathpow(1 - o, u)) / l, + M = this.incompleteBetaIterations, + S = 1; + for (var T = 0; T < M; T++) { + let N = M - T; + S = 1 + g(N, l, u, o) / S; + } + let C = h / S; + return C; + }), + (Statistics.prototype.regularisedBeta = function (o, l, u) { + if ("undefined" == typeof u) + return this.errorMessage( + "The regularised beta function is only defined for two numeric variables a and b." + ); + if (!this.isNumeric(o) || !this.isNumeric(l) || !this.isNumeric(u)) + return this.errorMessage( + "The regularised beta function is only defined for numeric variables x, a and b." + ); + if (1 < o || 0 > o) + return this.errorMessage( + "The regularised beta function is defined for x \u2265 0 and x \u2266 1." + ); + if (0 >= l || 0 >= u) + return this.errorMessage( + "The regularised beta function is defined for a and b with a > 0 and b > 0." + ); + if (!_NumberisInteger(l) || !_NumberisInteger(u)) + return this.incompleteBeta(o, l, u) / this.beta(l, u); + let g = this.epsilon + 1, + h = l, + M = 0; + for (; g >= this.epsilon; ) + (g = this.binomialCoefficient(u + h - 1, h) * _Mathpow(o, h)), + (M += g), + h++; + return M * _Mathpow(1 - o, u); + }), + (Statistics.prototype.covariance = function (o, l, u = !0) { + if ("undefined" == typeof l) + return this.errorMessage( + "Covariance requires two variables to be compared." + ); + let g = this.validateInput(o, "interval", "covariance"); + if (!1 === g) return; + let h = this.validateInput(l, "interval", "covariance"); + if (!1 === h) return; + let M = this.reduceToPairs(g.data, h.data); + if (0 === M.length) return; + let S = this.mean(M.valuesFirst), + T = this.mean(M.valuesSecond), + C = 0; + for (var N = 0; N < M.length; N++) + C += (M.valuesFirst[N] - S) * (M.valuesSecond[N] - T); + return ( + u && 1 < M.length + ? (C /= M.length - 1) + : !u && 0 < M.length + ? (C /= M.length) + : (C = void 0), + { covariance: C, missings: M.missings } + ); + }), + (Statistics.prototype.correlationCoefficient = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "Pearson correlation coefficient requires two variables to be compared." + ); + let u = this.validateInput( + o, + "interval", + "Pearson correlation coefficient" + ); + if (!1 === u) return; + let g = this.validateInput( + l, + "interval", + "Pearson correlation coefficient" + ); + if (!1 === g) return; + let h = u.length >= g.length ? u.length : g.length, + M = u.data, + S = g.data, + T = [], + C = 0, + N = 0; + for (var I = 0; I < h; I++) { + let U = M[I], + O = S[I]; + void 0 === typeof U || + void 0 === typeof O || + isNaN(U) || + isNaN(O) || + ((C += U), (N += O), T.push([U, O])); + } + let V = h - T.length; + if (((h = T.length), 0 === h)) return; + (C /= h), (N /= h); + let D = 0, + F = 0, + P = 0; + for (var I = 0; I < h; I++) { + let U = T[I][0] - C, + O = T[I][1] - N; + (D += U * O), (F += U * U), (P += O * O); + } + let B = 0 < F && 0 < P ? D / _Mathsqrt(F * P) : 0; + return { correlationCoefficient: B, missings: V }; + }), + (Statistics.prototype.spearmansRho = function (o, l, u = !1) { + if ("undefined" == typeof l) + return this.errorMessage( + "Spearman's Rho requires two variables to be compared." + ); + let g = this.validateInput(o, "ordinal", "Spearman's Rho"); + if (!1 === g) return; + let h = this.validateInput(l, "ordinal", "Spearman's Rho"); + if (!1 === h) return; + let M = this.reduceToPairs(o, l), + S = M.length; + if (0 === S) return; + let T = this.assignRanks(o, { + data: M.valuesCombined, + returnFrequencies: !0, + order: "desc", + }), + C = T.frequencies; + T = this.assignRanks(l, { + data: T.data, + returnFrequencies: !0, + order: "desc", + }); + let N = T.frequencies; + T = T.data; + let I = T.reduce((O, R) => { + return O + _Mathpow(R["rank-" + o] - R["rank-" + l], 2); + }, 0), + V = 6 * I; + if (u) { + let O = Object.values(C).reduce((L, E) => { + return L + _Mathpow(E, 3) - E; + }), + R = Object.values(N).reduce((L, E) => { + return L + _Mathpow(E, 3) - E; + }), + G = _Mathpow(S, 3) - S - 0.5 * O - 0.5 * R - V, + A = _Mathsqrt((_Mathpow(S, 3) - S - O) * (_Mathpow(S, 3) - S - R)); + V = G / A; + } else V = 1 - V / (_Mathpow(S, 3) - S); + let D = S - 2, + F = _Mathsqrt((D - 1) / 1.06) * this.fisherTransformation(V), + P = 1 - this.normalCumulativeValue(_Mathabs(F)), + B = V * _Mathsqrt(D / (1 - V * V)), + U = 1 - this.studentsTCumulativeValue(_Mathabs(B), D); + return { + rho: V, + significanceNormal: { zScore: F, pOneTailed: P, pTwoTailed: 2 * P }, + significanceStudent: { + degreesOfFreedom: D, + tStatistic: B, + pOneTailed: U, + pTwoTailed: 2 * U, + }, + missings: M.missings, + }; + }), + (Statistics.prototype.kendallsTau = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "Kendall's Tau requires two columns to analyze." + ); + let u = this.validateInput(o, "ordinal", "Kendall's Tau"); + if (!1 === u) return; + let g = this.validateInput(l, "ordinal", "Kendall's Tau"); + if (!1 === g) return; + let h = this.reduceToPairs(o, l), + M = h.length, + S = h.valuesCombined, + T = 0, + C = 0, + N = {}, + I = {}, + V = 0; + for (var D = 0; D < M - 1; D++) + for (var F = D + 1; F < M; F++) { + let X = S[D][o], + Q = S[F][o], + H = S[D][l], + J = S[F][l]; + X === Q + ? H === J + ? (V += 1) + : (N[H] = this.has(N, H) ? N[H] + 1 : 1) + : H === J + ? (I[H] = this.has(I, H) ? I[H] + 1 : 1) + : _Mathsign(X - Q) === _Mathsign(H - J) + ? (T += 1) + : (C += 1); + } + let P = T - C, + B = + 0 === Object.keys(N).length + Object.keys(I).length + ? (2 * P) / (M * (M - 1)) + : void 0, + U = (3 * P) / _Mathsqrt(0.5 * M * (M - 1) * (2 * M + 5)), + O = this.normalCumulativeValue(-_Mathabs(U)), + R = 2 < h.length ? 2 : h.length, + A = B, + L = U, + E = O; + if ("undefined" == typeof B) { + A = + P / + _Mathsqrt( + (T + C + Object.keys(N).length) * (T + C + Object.keys(I).length) + ); + let X = 0, + Q = 0, + H = 0, + J = 0, + $ = 0; + for (var W in N) + (X += W * (W - 1) * (2 * W + 5)), + (H += W * (W - 1)), + (J += W * (W - 1) * (W - 2)); + for (var W in I) + (Q += W * (W - 1) * (2 * W + 5)), + (J += (W * (W - 1)) / (2 * M * (M - 1))), + ($ += (W * (W - 1) * (W - 2)) / (9 * M * (M - 1) * (M - 2))); + (L = M * (M - 1) * (2 * M + 5)), + (L = (L - X - Q) / 18 + H * J + 0 * $), + (L = P / _Mathsqrt(L)), + (E = this.normalCumulativeValue(-_Mathabs(L))); + } + let K = + "undefined" == typeof B + ? void 0 + : { tauA: B, z: U, pOneTailed: O, pTwoTailed: 2 * O }, + Y = + "undefined" == typeof B + ? { tauB: A, z: L, pOneTailed: E, pTwoTailed: 2 * E } + : { tauB: B, z: U, pOneTailed: O, pTwoTailed: 2 * O }; + return { + a: K, + b: Y, + c: { tauC: (2 * R * P) / (M * M * (R - 1)) }, + missings: h.missings, + }; + }), + (Statistics.prototype.goodmanKruskalsGamma = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "Goodman and Kruskal's Gamma requires two columns to analyze." + ); + let u = this.validateInput(o, "ordinal", "Goodman and Kruskal's Gamma"); + if (!1 === u) return; + let g = this.validateInput(l, "ordinal", "Goodman and Kruskal's Gamma"); + if (!1 === g) return; + let h = this.reduceToPairs(o, l), + M = h.valuesCombined, + S = h.length, + T = 0, + C = 0; + for (var N = 0; N < S - 1; N++) + for (var I = N + 1; I < S; I++) { + let P = M[N][o], + B = M[I][o], + U = M[N][l], + O = M[I][l]; + P === B || + U === O || + (_Mathsign(P - B) === _Mathsign(U - O) ? (T += 1) : (C += 1)); + } + let V = (T - C) / (T + C), + D = V * _Mathsqrt((T + C) / (S * (1 - V * V))), + F = 1 - this.studentsTCumulativeValue(_Mathabs(D), S - 2); + return { + gamma: V, + tStatistic: D, + pOneTailed: F, + pTwoTailed: 2 * F, + missings: h.missings, + }; + }), + (Statistics.prototype.fisherTransformation = function (o) { + return "undefined" == typeof o || !this.isNumeric(o) || -1 > o || 1 < o + ? this.errorMessage( + "Fisher transformation is only defined for a Pearson correlation coefficient within the interval of [-1, 1]." + ) + : Math.atanh(o) || 0.5 * _Mathlog((1 + o) / (1 - o)); + }), + (Statistics.prototype.linearRegression = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "Linear regression requires two columns to compare." + ); + let u = this.validateInput(o, "interval", "linear regression"); + if (!1 === u) return; + let g = this.validateInput(l, "interval", "linear regression"); + if (!1 === g) return; + let h = this.reduceToPairs(u.data, g.data), + M = h.valuesFirst, + S = h.valuesSecond, + T = h.missings, + C = h.length; + if (0 === C) return; + let N = this.mean(M), + I = this.mean(S), + V = 0, + D = 0, + F = 0; + for (var P = 0; P < C; P++) { + let W = M[P] - N, + K = S[P] - I; + (F += W * K), (V += W * W), (D += K * K); + } + if (0 == V || 0 == D) return; + let B = F / V, + U = F / D, + G = F / _Mathsqrt(V * D), + A = G * G, + L = 2 < C ? 1 - ((1 - A) * (C - 1)) / (C - 2) : void 0, + E = (180 * Math.acos(G)) / _MathPI; + return ( + 90 < E && (E = 180 - E), + { + regressionFirst: { beta1: I - B * N, beta2: B }, + regressionSecond: { beta1: N - U * I, beta2: U }, + coefficientOfDetermination: A, + coefficientOfDeterminationCorrected: L, + correlationCoefficient: G, + phi: E, + } + ); + }), + (Statistics.prototype.normalProbabilityDensity = function (o, l = 0, u = 1) { + if ("undefined" == typeof o) + return this.errorMessage("Normal probability density: no x is given."); + if (0 >= u) + return this.errorMessage( + "Normal probability density: variance must be larger than 0." + ); + let g = -_Mathpow(o - l, 2) / (2 * u); + return _Mathexp(g) / _Mathsqrt(2 * _MathPI * u); + }), + (Statistics.prototype.normalDistribution = function (o = 0, l = 1) { + if (0 >= l) + return this.errorMessage( + "Normal distribution: variance must be larger than 0." + ); + let u = 0, + g = 1, + h = {}; + for ( + ; + g >= this.epsilon && + ((g = this.normalProbabilityDensity(o + u, o, l)), !(g < this.epsilon)); + + ) + (h[(o + u).toFixed(2)] = g), (h[(o - u).toFixed(2)] = g), (u += 0.01); + return h; + }), + (Statistics.prototype.normalCumulativeValue = function (o) { + if ("undefined" == typeof o) + return this.errorMessage("Normal cumulative value: no z is given."); + let l = o, + u = o; + for (var g = 1; g < this.zTableIterations; g++) + (u *= (o * o) / (2 * g + 1)), (l += u); + return ( + _Mathround( + 1e5 * (0.5 + (l / _Mathsqrt(2 * _MathPI)) * _Mathexp(-0.5 * o * o)) + ) / 1e5 + ); + }), + (Statistics.prototype.normalCumulativeDistribution = function () { + let o = this.zTable; + if ("undefined" == typeof o) { + o = {}; + for (var l = 0; 4.09 >= l; l += 0.01) { + let u = _Mathround(100 * l) / 100; + o[u.toFixed(2)] = this.normalCumulativeValue(l); + } + this.zTable = o; + } + return o; + }), + (Statistics.prototype.binomialProbabilityMass = function ( + o, + l = 10, + u = 0.5 + ) { + return "undefined" == typeof o + ? this.errorMessage( + "Binomial probability mass: the required argument k was not given." + ) + : 0 > o || !_NumberisInteger(o) + ? this.errorMessage( + "Binomial probability mass: k must be a non-negative integer." + ) + : 0 > l || !_NumberisInteger(l) + ? this.errorMessage( + "Binomial probability mass: n must be a non-negative integer." + ) + : 0 > u || 1 < u + ? this.errorMessage( + "Binomial probability mass: The probability must lie within the range of [0, 1]." + ) + : this.binomialCoefficient(l, o) * + _Mathpow(u, o) * + _Mathpow(1 - u, l - o); + }), + (Statistics.prototype.binomialDistribution = function (o = 10, l = 0.5) { + if (0 > o || !_NumberisInteger(o)) + return this.errorMessage( + "Binomial distribution: n must be a non-negative integer." + ); + if (0 > l || 1 < l) + return this.errorMessage( + "Binomial distribution: The probability must lie within the range of [0, 1]." + ); + let u = 0, + g = 0, + h = 0, + M = [], + S = 1; + for (; u <= o; ) + (g = S * _Mathpow(l, u) * _Mathpow(1 - l, o - u)), + M.push(g), + (h += g), + u++, + (S = (S * (o + 1 - u)) / u); + return M; + }), + (Statistics.prototype.binomialCumulativeValue = function ( + o, + l = 10, + u = 0.5 + ) { + return "undefined" == typeof o + ? this.errorMessage( + "Binomial cumulative distribution value: the required argument k was not given." + ) + : 0 > o || !_NumberisInteger(o) + ? this.errorMessage( + "Binomial cumulative distribution value: k must be a non-negative integer." + ) + : 0 > l || !_NumberisInteger(l) + ? this.errorMessage( + "Binomial cumulative distribution value: n must be a non-negative integer." + ) + : 0 > u || 1 < u + ? this.errorMessage( + "Binomial cumulative distribution value: The probability must lie within the range of [0, 1]." + ) + : this.regularisedBeta(1 - u, l - o, o + 1); + }), + (Statistics.prototype.binomialCumulativeDistribution = function ( + o = 10, + l = 0.5 + ) { + if (0 > o || !_NumberisInteger(o)) + return this.errorMessage( + "Binomial cumulative distribution: n must be a non-negative integer." + ); + if (0 > l || 1 < l) + return this.errorMessage( + "Binomial cumulative distribution: The probability must lie within the range of [0, 1]." + ); + let u = this.binomialDistribution(o, l), + g = 0; + return u.map(function (h) { + return (g = this.sumExact([g, h])), g; + }, this); + }), + (Statistics.prototype.poissonProbabilityMass = function (o, l = 1) { + if ("undefined" == typeof o || !_NumberisInteger(o)) + return this.errorMessage( + "Poisson probability mass: the required argument k must be an integer." + ); + if (0 > o || 0 >= l) + return this.errorMessage( + "Poisson probability mass: Both k and lambda must be larger than 0." + ); + if (10 < o) { + let g = 1; + for (var u = 1; u <= o; u++) g *= (l * _Mathexp(-l / o)) / u; + return g; + } + return (_Mathexp(-l) * _Mathpow(l, o)) / this.factorial(o); + }), + (Statistics.prototype.poissonDistribution = function (o = 1) { + if (0 >= o) + return this.errorMessage( + "Poisson distribution: Lambda must be larger than 0." + ); + let l = 0, + u = 0, + g = []; + for (; u < 1 - this.epsilon; ) { + let h = this.poissonProbabilityMass(l, o); + g.push(h), (u += h), l++; + } + return g; + }), + (Statistics.prototype.poissonCumulativeValue = function (o, l = 1) { + if ("undefined" == typeof o || !_NumberisInteger(o)) + return this.errorMessage( + "Poisson cumulative distribution: The number of cumulative events k must be supplied." + ); + if (0 > o || 0 >= l) + return this.errorMessage( + "Poisson distribution: Both k and lambda must be larger than 0." + ); + let u = this.poissonDistribution(l); + return o < u.length - 1 ? this.sumExact(u.slice(0, o + 1)) : 1; + }), + (Statistics.prototype.poissonCumulativeDistribution = function (o = 1) { + if (0 >= o) + return this.errorMessage( + "Poisson distribution: lambda must be larger than 0." + ); + let l = this.poissonDistribution(o), + u = 0; + return l.map(function (g) { + return (u = this.sumExact([u, g])), u; + }, this); + }), + (Statistics.prototype.studentsTProbabilityDensity = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "Student's t-distribution probability density: no value for degrees of freedom (df) given." + ); + if (0 >= l) + return this.errorMessage( + "Student's t-distribution probability density: degrees of freedom (df) must be larger than 0." + ); + let u = _Mathpow(1 + (o * o) / l, -0.5 * (l + 1)); + return (u /= _Mathsqrt(l) * this.beta(0.5, 0.5 * l)), u; + }), + (Statistics.prototype.studentsTDistribution = function (o) { + if ("undefined" == typeof o) + return this.errorMessage( + "Student's t-distribution: no value for degrees of freedom (df) given." + ); + if (0 >= o) + return this.errorMessage( + "Student's t-distribution: degrees of freedom (df) must be larger than 0." + ); + let l = 0, + u = 1, + g = {}; + for ( + ; + u >= this.epsilon && + ((u = this.studentsTProbabilityDensity(l, o)), !(u < this.epsilon)); + + ) + (g[l.toFixed(2)] = u), (g[(-l).toFixed(2)] = u), (l += 0.01); + return g; + }), + (Statistics.prototype.studentsTCumulativeValue = function (o, l) { + return "undefined" == typeof l + ? this.errorMessage( + "Student's cumulative t-distribution value: no value for degrees of freedom (df) given." + ) + : 0 >= l + ? this.errorMessage( + "Student's cumulative t-distribution value: degrees of freedom (df) must be larger than 0." + ) + : 0 >= o + ? 0.5 * this.regularisedBeta(l / (o * o + l), 0.5 * l, 0.5) + : 0.5 + 0.5 * this.regularisedBeta((o * o) / (o * o + l), 0.5, 0.5 * l); + }), + (Statistics.prototype.studentsTCumulativeDistribution = function (o) { + if ("undefined" == typeof o) + return this.errorMessage( + "Student's cumulative t-distribution: no value for degrees of freedom (df) given." + ); + if (0 >= o) + return this.errorMessage( + "Student's cumulative t-distribution: degrees of freedom (df) must be larger than 0." + ); + let l = 0, + u = 0, + g = -0.1, + h = {}; + for ( + ; + u <= 1 - this.epsilon && + ((u = this.studentsTCumulativeValue(l, o)), !(g >= u)); + + ) + (h[l.toFixed(2)] = u), (h[(-l).toFixed(2)] = u), (g = u), (l += 0.01); + return h; + }), + (Statistics.prototype.chiSquaredProbabilityDensity = function (o, l) { + return "undefined" == typeof l + ? this.errorMessage( + "Chi squared distribution probability density: no value for degrees of freedom (df) given." + ) + : 0 >= l + ? this.errorMessage( + "Chi squared distribution probability density: degrees of freedom (df) must be larger than 0." + ) + : 0 >= o + ? 0 + : (_Mathpow(o, 0.5 * l - 1) * _Mathexp(-0.5 * o)) / + (_Mathpow(2, 0.5 * l) * this.gamma(0.5 * l, !0)); + }), + (Statistics.prototype.chiSquaredDistribution = function (o) { + if ("undefined" == typeof o) + return this.errorMessage( + "Chi squared distribution: no value for degrees of freedom (df) given." + ); + if (0 >= o) + return this.errorMessage( + "Chi squared distribution: degrees of freedom (df) must be larger than 0." + ); + let l = 0.01, + u = 1, + g = { "0.00": 0 }; + for ( + ; + ((2 >= o && u >= this.epsilon) || + (2 < o && l <= o - 2) || + (2 < o && u >= this.epsilon)) && + ((u = this.chiSquaredProbabilityDensity(l, o)), + !(u < this.epsilon && l >= o - 2 && 2 < o)); + + ) + (g[l.toFixed(2)] = u), (l += 0.01); + return g; + }), + (Statistics.prototype.chiSquaredCumulativeValue = function (o, l) { + return "undefined" == typeof l + ? this.errorMessage( + "Chi squared cumulative distribution value: no value for degrees of freedom (df) given." + ) + : 0 >= l + ? this.errorMessage( + "Chi squared cumulative distribution value: degrees of freedom (df) must be larger than 0." + ) + : 0 >= o + ? 0 + : this.regularisedGamma(0.5 * l, 0.5 * o); + }), + (Statistics.prototype.chiSquaredCumulativeDistribution = function (o) { + if ("undefined" == typeof o) + return this.errorMessage( + "Chi squared cumulative distribution: no value for degrees of freedom (df) given." + ); + if (0 >= o) + return this.errorMessage( + "Chi squared cumulative distribution: degrees of freedom (df) must be larger than 0." + ); + let l = 0.01, + u = 0, + g = { "0.00": 0 }; + for ( + ; + u <= 1 - this.epsilon && + ((u = this.chiSquaredCumulativeValue(l, o)), !(u >= 1 - this.epsilon)); + + ) + 0 < u && (g[l.toFixed(2)] = u), (l += 0.01); + return g; + }), + (Statistics.prototype.barnardsTest = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage("Barnard's test: No data was supplied."); + let u = this.contingencyTable(o, l, "Barnard's test"); + if ("undefined" == typeof u) return; + let { a: g, b: h, c: M, d: S } = u, + T = g + h + M + S; + if (T > this.maxBarnardsN) + return this.errorMessage( + "Barnard's test is a resource-intensive method, relative to the total number of datasets to analyze. There are " + + T + + " datasets in the supplied data, exceeding the maxinum of " + + this.maxBarnardsN + + '. You can change this number by changing the "maxBarnardsN" option (be cautious).' + ); + var C = (g + h) / T; + (C = C * (1 - C) * (1 / (g + M) + 1 / (h + S))), + (C = (h / (h + S) - g / (g + M)) / _Mathsqrt(C)), + isNaN(C) && (C = 0); + let N = []; + for (var I = 0; 1 > I; I = this.sumExact([I, 1e-3])) { + let F = 0; + for (var V = 0; V <= g + M; V++) + for (var D = 0; D <= h + S; D++) { + let P = (V + D) / T; + if ( + ((P = P * (1 - P) * (1 / (g + M) + 1 / (h + S))), + (P = (V / (g + M) - D / (h + S)) / _Mathsqrt(P)), + !isNaN(P) && _Mathabs(P) >= _Mathabs(C)) + ) { + let B = + this.binomialCoefficient(g + M, V) * + this.binomialCoefficient(h + S, D); + (B *= _Mathpow(I, V + D) * _Mathpow(1 - I, T - V - D)), + (F += isNaN(B) ? 0 : B); + } + } + N.push({ nuisance: I, significance: F }); + } + return ( + (N = this.sortDataByColumn("significance", { data: N, order: "desc" })), + { + wald: C, + nuisance: N[0].nuisance, + pOneTailed: 0.5 * N[0].significance, + pTwoTailed: N[0].significance, + } + ); + }), + (Statistics.prototype.binomialTest = function (o, l, u = 0.5) { + if ("undefined" == typeof l) + return this.errorMessage( + "Binomial test requires the data to test and a value which is hypotethised to be observed with a probability of alpha." + ); + if (0 > u || 1 < u) + return this.errorMessage( + "Binomial test is only defined for probabilities alpha with alpha \u2265 0 and alpha \u2266 1." + ); + let g = this.getScale(o); + if ("interval" === g || "metric" === g) + return this.errorMessage( + "Binomial test is only defined for data of nominal or ordinal dichotomic scale." + ); + let h = this.validateInput(o, "nominal", "binomial test"); + if (!1 !== h) { + let M = this.getUniqueValues(h.data); + if (2 < M.length) + return this.errorMessage( + "Binomial test is only defined for dichotomic data. The supplied data has " + + M.length + + " unique values." + ); + if (2 === M.length && 0 > M.indexOf(l)) + return this.errorMessage( + 'The value "' + l + '" was not found in the supplied data.' + ); + let S = h.data.filter(function (I) { + return I === l; + }).length, + T = this.binomialProbabilityMass(S, h.length, u), + C = this.binomialCumulativeValue(S - 1, h.length, u), + N = 1 - C - T; + return { + pExactly: T, + pFewer: C, + pAtMost: C + T, + pMore: N, + pAtLeast: N + T, + }; + } + }), + (Statistics.prototype.signTest = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage("Sign test: No data was supplied."); + let u = this.validateInput(o, "ordinal", "sign test"), + g = this.validateInput(l, "ordinal", "sign test"); + if (!1 === u || !1 === g) return; + if (0 === u.length || 0 === g.length) return; + let h = this.reduceToPairs(u.data, g.data), + M = h.missings, + S = h.length, + T = h.valuesFirst, + C = h.valuesSecond, + N = 0; + for (var I = 0; I < S; I++) T[I] > C[I] && (N += 1); + let V = this.binomialProbabilityMass(N, S), + D = this.binomialCumulativeValue(N - 1, S), + F = 1 - D - V; + return { + positives: N, + pExactly: V, + pFewer: D, + pAtMost: D + V, + pMore: F, + pAtLeast: F + V, + }; + }), + (Statistics.prototype.fishersExactTest = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "Fisher's exact test requires two columns to analyze." + ); + let u = this.contingencyTable(o, l, "Fisher's exact test"); + if ("undefined" != typeof u) { + let { a: g, b: h, c: M, d: S } = u, + T = + (this.binomialCoefficient(g + h, g) * + this.binomialCoefficient(M + S, M)) / + this.binomialCoefficient(g + h + M + S, g + M); + return T; + } + }), + (Statistics.prototype.mannWhitneyU = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "Mann-Whitney-U test requires two columns to analyze." + ); + let u = this.validateInput(o, "nominal", "Mann-Whitney-U test"), + g = this.validateInput(l, "ordinal", "Mann-Whitney-U test"); + if (!1 === u || !1 === g) return; + if (0 === u.length || 0 === g.length) return; + let h = this.sort(g.data), + M = this.getUniqueValues(u.data); + if (2 !== M.length) + return this.errorMessage( + 'The Mann-Whitney-U test requires the independent variable to have exactly two unique values. Variable "' + + independentValue + + '" has ' + + M.length + + " different values: " + + u.data.join(", ") + ); + let S = this.assignRanks(l), + T = S.reduce((O, R) => { + return R[o] === M[0] ? O + R["rank-" + l] : O; + }, 0), + C = S.reduce((O, R) => { + return R[o] === M[0] ? O + 1 : O; + }, 0), + N = S.reduce((O, R) => { + return R[o] === M[1] ? O + R["rank-" + l] : O; + }, 0), + I = S.reduce((O, R) => { + return R[o] === M[1] ? O + 1 : O; + }, 0), + F = Math.min(C * (0.5 * C + I + 0.5) - T, I * (0.5 * I + C + 0.5) - N), + P = (F - 0.5 * C * I) / _Mathsqrt((C * I * (C + I + 1)) / 12), + B = 1 - this.normalCumulativeValue(_Mathabs(P)); + return { MannWhitneyU: F, zScore: P, pOneTailed: B, pTwoTailed: 2 * B }; + }), + (Statistics.prototype.chiSquaredTest = function (o, l) { + if ("undefined" == typeof l) + return this.errorMessage( + "Chi Squared Test: You need to specify two variables, either of nominal or ordinal scale." + ); + let u = this.getScale(o), + g = this.getScale(l); + if ( + ("ordinal" !== u && "nominal" !== u) || + ("ordinal" !== g && "nominal" !== g) + ) + return this.errorMessage( + "Chi Squared Test: Both variables need to be either of nominal or ordinal scale." + ); + let h = this.contingencyTable(o, l); + if ("undefined" == typeof h) + return this.errorMessage( + "Chi Squared Test: Failed to create a contingency table. Please make sure your data is prepared correctly." + ); + h = h.detailled; + let M = h.total.total, + S = 0, + T = Object.keys(h).length - 1; + for (var C in ((T = (T - 1) * (Object.keys(h.total).length - T - 2)), h)) + if (this.has(h, C) && "total" != C) { + let V = h[C].total; + for (var N in h[C]) + if (this.has(h[C], N) && "total" != N) { + let D = h.total[N], + F = (V * D) / M; + S += _Mathpow(h[C][N] - F, 2) / F; + } + } + let I; + return ( + (I = 0 > S || 1 > T ? 0 : 1 - this.chiSquaredCumulativeValue(S, T)), + { PearsonChiSquared: S, degreesOfFreedom: T, significance: I } + ); + }), + (Statistics.prototype.studentsTTestOneSample = function (o, l) { + if ("undefined" == typeof l || !this.isNumeric(l)) + return this.errorMessage( + "Student's t-test (one sample) requires data and the mean for which the null hypothesis should hold true." + ); + let u = this.validateInput(o, "interval", "student's t-test (one sample)"); + if (!1 === u) return; + if (0 === u.length) return; + let g = this.mean(u.data), + h = this.standardDeviation(u.data), + M = (_Mathsqrt(u.length) * (g - l)) / h, + S = u.length - 1, + T = this.studentsTCumulativeValue(_Mathabs(M), S); + return ( + 0.5 < T && (T = 1 - T), + { tStatistic: M, degreesOfFreedom: S, pOneSided: T, pTwoSided: 2 * T } + ); + }), + (Statistics.prototype.studentsTTestTwoSamples = function ( + o, + l, + { nullHypothesisDifference: u = 0, dependent: g = !1 } = {} + ) { + if ("undefined" == typeof l) + return this.errorMessage( + "Student's t-test (two sample) requires data for two columns and the difference of their means for which the null hypothesis should hold true." + ); + let h = this.validateInput(o, "interval", "student's t-test (two sample)"), + M = this.validateInput(l, "interval", "student's t-test (two sample)"); + if (!1 === h || !1 === M) return; + let N, + I, + S = h.length, + T = M.length, + C = {}; + if (0 === S || 0 === T) return; + if (g) { + let F = this.reduceToPairs(h.data, M.data), + P = F.valuesFirst, + B = F.valuesSecond, + U = 0, + O = 0; + for (var V = 0; V < F.length; V++) O += P[V] - B[V]; + O /= F.length; + for (var V = 0; V < F.length; V++) U += _Mathpow(P[V] - B[V] - O, 2); + (U = _Mathsqrt(U / (F.length - 1))), + (N = (_Mathsqrt(S) * (O - u)) / U), + (I = F.length - 1), + (C = { tStatistic: N, degreesOfFreedom: I, missings: F.missings }); + } else { + let F = this.mean(h.data), + P = this.mean(M.data), + B = this.variance(h.data), + U = this.variance(M.data), + O = _Mathsqrt(((S - 1) * B + (T - 1) * U) / (S + T - 2)); + (N = (F - P - u) / O), + (N *= _Mathsqrt((S * T) / (S + T))), + (I = S + T - 2), + (C = { tStatistic: N, degreesOfFreedom: I }); + } + let D = this.studentsTCumulativeValue(N, I); + return 0.5 < D && (D = 1 - D), (C.pOneSided = D), (C.pTwoSided = 2 * D), C; + }), + (Statistics.prototype.gaussianError = function (o) { + if ("undefined" == typeof o || !this.isNumeric(o)) + return this.errorMessage( + "Gaussian Error Function: No valid value for x supplied. X needs to be numeric." + ); + let l = 1 / (1 + 0.5 * _Mathabs(o)), + u = + -o * o - + 1.26551223 + + 1.00002368 * l + + 0.37409196 * l * l + + 0.09678418 * _Mathpow(l, 3) - + 0.18628806 * _Mathpow(l, 4) + + 0.27886807 * _Mathpow(l, 5) - + 1.13520398 * _Mathpow(l, 6) + + 1.48851587 * _Mathpow(l, 7) - + 0.82215223 * _Mathpow(l, 8) + + 0.17087277 * _Mathpow(l, 9); + return (u = _Mathexp(u) * l), 0 <= o ? 1 - u : u - 1; + }), + (Statistics.prototype.inverseGaussianError = function (o) { + if ("undefined" == typeof o || !this.isNumeric(o)) + return this.errorMessage( + "Inverse Gaussian Error Function: No valid value for x supplied. X needs to be numeric." + ); + if (1 < o) + return this.errorMessage( + "Inverse Gaussian Error Function: x can not be larger than 1." + ); + if (-1 > o) + return this.errorMessage( + "Inverse Gaussian Error Function: x can not be smaller than -1." + ); + var u, + l = -_Mathlog((1 - o) * (1 + o)); + return ( + 5 > l + ? ((l -= 2.5), + (u = +3.43273939e-7 + +2.81022636e-8 * l), + (u = +-3.5233877e-6 + u * l), + (u = +-4.39150654e-6 + u * l), + (u = 2.1858087e-4 + u * l), + (u = -0.00125372503 + u * l), + (u = -0.00417768164 + u * l), + (u = 0.246640727 + u * l), + (u = 1.50140941 + u * l)) + : ((l = _Mathsqrt(l) - 3), + (u = 1.00950558e-4 - 2.00214257e-4 * l), + (u = 0.00134934322 + u * l), + (u = -0.00367342844 + u * l), + (u = 0.00573950773 + u * l), + (u = -0.0076224613 + u * l), + (u = 0.00943887047 + u * l), + (u = 1.00167406 + u * l), + (u = 2.83297682 + u * l)), + (u * o).toFixed(7) + ); + }), + (Statistics.prototype.probit = function (o) { + return "undefined" != typeof o && this.isNumeric(o) + ? 0 > o || 1 < o + ? this.errorMessage( + "Probit is only defined for quantiles p with 1 \u2265 p \u2265 0." + ) + : 0 === o + ? -Infinity + : 1 === o + ? Infinity + : 1.4142135623730951 * this.inverseGaussianError(2 * o - 1) + : this.errorMessage( + "Probit: No valid value for quantile supplied. quantile needs to be numeric." + ); + }), + (Statistics.prototype.fisherYatesShuffle = function (o, l = Math.random) { + if ("undefined" == typeof o) + return this.errorMessage("Fisher-Yates shuffle: No data given."); + let u = this.validateInput(o, "nominal", ""); + if (!1 === u) return; + let M, + S, + g = u.data, + h = u.length; + for (; h; ) + (S = _Mathfloor(l() * h--)), (M = g[h]), (g[h] = g[S]), (g[S] = M); + return g; + }), + (Statistics.prototype.xorshift = function (o, l = 0) { + if ("undefined" == typeof o || o.constructor !== Array || 4 !== o.length) + return this.errorMessage( + "Xorshift needs to be seeded with an array consisting of four numbers." + ); + if (!_NumberisInteger(l) || 0 > l) + return this.errorMessage( + "Xorshift: startIndex must be a non-negative integer." + ); + const u = 4, + g = 64; + let h = 0, + M = u, + S = new Uint32Array(g); + (S[0] = o[0]), (S[1] = o[1]), (S[2] = o[2]), (S[3] = o[3]); + const T = function (I, V, D) { + for (var F = V; F < D; F++) { + let P = (I[0] ^ (I[0] << 11)) >>> 0; + (I[0] = I[1]), + (I[1] = I[2]), + (I[2] = I[3]), + (I[3] = + (((I[3] ^ (I[3] >>> 19)) >>> 0) ^ ((P ^ (P >>> 8)) >>> 0)) >>> 0), + (I[F] = I[3]); + } + return I; + }; + (this.next = function (I = !0) { + let V = S[M]; + return ( + (h += 1), M++ >= g && ((M = u), T(S, u, g)), I ? V / 4294967296 : V + ); + }), + (S = T(S, u, g)); + for (var C = 0; C < l; C++) this.next(); + }), + (Statistics.prototype.boxMuller = function ( + o = 0, + l = 1, + { randomSourceA: u = Math.random, randomSourceB: g = Math.random } = {} + ) { + let h = 0, + M = 0, + S = 0, + T = 0; + do (h = u()), (S += 1); + while ((0 >= h || 1 <= h) && 50 > S); + do (M = g()), (T += 1); + while ((0 >= M || 1 <= M) && 50 > T); + for (; 0 >= h || 1 <= h; ) h = Math.random(); + for (; 0 >= M || 1 <= M; ) M = Math.random(); + let C = _Mathsqrt(-2 * _Mathlog(h)) * Math.cos(2 * _MathPI * M); + return C * l + o; + }), + (Statistics.prototype.ziggurat = function (o = 0, l = 1) { + let u = 123456789, + g = [128], + h = [128], + M = [128]; + const S = function (I, V) { + let P, + B, + D = 3.442619855899, + F = 1 / D; + for (;;) { + if (((P = I * g[V]), 0 === V)) { + for (P = -_Mathlog(C()) * F, B = -_Mathlog(C()); B + B < P * P; ) + (P = -_Mathlog(C()) * F), (B = -_Mathlog(C())); + return 0 < I ? D + P : -D - P; + } + if (h[V] + C() * (h[V - 1] - h[V]) < _Mathexp(-0.5 * P * P)) return P; + if (((I = T()), (V = 127 & I), _Mathabs(I) < M[V])) return I * g[V]; + } + }, + T = function () { + let I = u, + V = u; + return ( + (V ^= V << 13), (V ^= V >>> 17), (V ^= V << 5), (u = V), 0 | (I + V) + ); + }, + C = function () { + return 0.5 * (1 + T() / -2147483648); + }; + (function () { + u ^= new Date().getTime(); + let I = 2147483648, + V = 3.442619855899, + D = V, + F = 0.00991256303526217, + P = F / _Mathexp(-0.5 * V * V); + (M[0] = _Mathfloor((V * I) / P)), + (M[1] = 0), + (g[0] = P / I), + (g[127] = V / I), + (h[0] = 1), + (h[127] = _Mathexp(-0.5 * V * V)); + for (var B = 126; 1 <= B; B--) + (V = _Mathsqrt(-2 * _Mathlog(F / V + _Mathexp(-0.5 * V * V)))), + (M[B + 1] = _Mathfloor((V * I) / D)), + (D = V), + (h[B] = _Mathexp(-0.5 * V * V)), + (g[B] = V / I); + })(), + (this.next = function () { + let I = T(), + V = 127 & I, + D = _Mathabs(I) < M[V] ? I * g[V] : S(I, V); + return D * l + o; + }); + }); + + + + +//--------------------------------------------------------------------- +// Start of function to clear field when 'Clear Data' button is pressed +function clearData(){ + document.getElementById("numbers").value = ""; + document.getElementById("frequencies").value = ""; + document.getElementById("data-result-area").innerHTML = ""; + document.getElementById("myChart").innerHTML =""; + sessionStorage.clear(); + } + // End of function to clear field when 'Clear Data' button is pressed + //--------------------------------------------------------------------- + + + + +//--------------------------------------------------------------------- +// Start of function to preserve field when 'Clear Data' button is pressed + +document.addEventListener('DOMContentLoaded', function() { + // your code here + document.getElementById("numbers").value = sessionStorage.getItem("dataState"); + document.getElementById("frequencies").value = sessionStorage.getItem("freqState"); + var numberObj = document.getElementById("numbers"); + var freqObj = document.getElementById("frequencies"); + + numberObj.onchange = function() { + let datastate = numberObj.value; + sessionStorage.setItem("dataState", datastate) + console.log(datastate) + }; + + freqObj.onchange = function() { + let freqstate = freqObj.value; + sessionStorage.setItem("freqState", freqstate) + } + +}, false); + + + // End of function to clear field when 'Clear Data' button is pressed + //--------------------------------------------------------------------- + + + + + +//--------------------------------------------------------------------- +// Start of function to sanitizeData taken from input fields +// this functions outputs a 2D array with inner array containing data-freqency pairs +function sanitizeData() +{ + // extacting data points into an array from the string of numbers + let dataString = document.getElementById("numbers").value; + let freqString = document.getElementById("frequencies").value; + + // removing extra space char at the bigginnig and end of inputed data + dataString = dataString.trim(); + freqString = freqString.trim(); + + + // removing extra space in-between the data values in the data strings + dataString = dataString.replace(/\s+/g, ' '); + freqString = freqString.replace(/\s+/g, ' '); + + // defining a test to check whether the data in class data or not + let classtest = /\d+[-]\d+/ + + // testing data string for classinput + //if is is class data data we will return classdatafreq array if not we will return normal freqdataAray + if(classtest.test(dataString)){ + let dataArrayString = ""; + dataArrayString = dataString.split(" "); + console.log(dataArrayString); + } + + // making an num array from the string of point data + let dataArray = dataString.split(" "); + for(let x=0; x < dataArray.length; x++) + { dataArray[x] = +dataArray[x]; }; + + // making an num array from the string of freq data + let freqArray = freqString.split(" "); + for(let x=0; x < freqArray.length; x++) + { freqArray[x] = +freqArray[x]; }; + +// the variable defied below will be returned + let freqDataArray = [] + + +// checking if user has not inputed same number of data and frequencies + if( freqArray.length !== dataArray.length){ + alert("number of data and freqencies don't match"); + } + + // check and alert if the inputed numbers is not a number + if(dataArray.some(i => isNaN(i)) || freqArray.some(i => isNaN(i))){ + alert("please input numbers only"); + } + + + else{ + for(let k = 0; k < dataArray.length ; k++) + { + let tempArray = [] + for(let i = freqArray[k]; i > 0; i--){ + tempArray.push(dataArray[k]); + } + // tempArray.push(freqArray[k]); + freqDataArray.push(tempArray) ; + } + } + + freqDataArray = freqDataArray.flat(); + // console.log(freqDataArray) + return freqDataArray; +} +// End of function to sanitizeData taken from input fields + +//--------------------------------------------------------------------- + +// var Statistics = require('/path/to/statistics.js'); + + +//--------------------------------------------------------------------- + +// for Mean +function calculateMean() + { + let data = []; + let arr = sanitizeData(); + + for(let i = 0; i <= arr.length - 1; i++){ + let tempob = {dataPoint: arr[i]}; + data.push(tempob) + } + + // console.log(data); + + var columns = { + dataPoint: 'metric' + } + + let stats = new Statistics( data , columns); + + + // Calculating Median + let mean = stats.arithmeticMean(arr) + + + //Code below is calculating Mean + + // Code Below is showing it in the Page + let result = document.getElementById("data-result-area"); + result.innerHTML = `

${mean.toFixed(2)}

`; + + } + +// For Median + function calculateMedian() + { + + let data = []; + let arr = sanitizeData(); + + for(let i = 0; i <= arr.length - 1; i++){ + let tempob = {dataPoint: arr[i]}; + data.push(tempob) + } + + // console.log(data); + + let columns = { + dataPoint: 'metric' + } + + let stats = new Statistics( data , columns); + + + // Calculating Median + let median = stats.median(arr) + + // Code Below is showing it in the Page + let result = document.getElementById("data-result-area"); + + result.innerHTML = `

${median.toFixed(2)}

`; + + } + + +// For Mode + function calculateMode() + { + + let data = []; + let arr = sanitizeData(); + + for(let i = 0; i <= arr.length - 1; i++){ + let tempob = {dataPoint: arr[i]}; + data.push(tempob) + } + + // console.log(data); + + let columns = { + dataPoint: 'metric' + } + + let stats = new Statistics( data , columns); + + + // Calculating Median + let mode = stats.mode(arr) + + + //Code above is calculating Mode + + // Code Below is showing it in the Page + let result = document.getElementById("data-result-area"); + result.innerHTML = `

${mode.toFixed(2)}

`; + + } + +// For Range + function calculateRange() + { + + let data = []; + let arr = sanitizeData(); + + for(let i = 0; i <= arr.length - 1; i++){ + let tempob = {dataPoint: arr[i]}; + data.push(tempob) + } + + // console.log(data); + + let columns = { + dataPoint: 'metric' + } + + let stats = new Statistics( data , columns); + + + // Calculating range + let range = stats.range(arr) + + + //Code above is calculating Mode + + // Code Below is showing it in the Page + let result = document.getElementById("data-result-area"); + result.innerHTML = `

${range.toFixed(2)}

`; + + } + +// For Kutosis + function calculateKurtosis() + { + + let data = []; + let arr = sanitizeData(); + + for(let i = 0; i <= arr.length - 1; i++){ + let tempob = {dataPoint: arr[i]}; + data.push(tempob) + } + + // console.log(data); + + let columns = { + dataPoint: 'metric' + } + + let stats = new Statistics( data , columns); + + + // Calculating Median + let kurtosis = stats.kurtosis(arr) + + + //Code above is calculating Mode + + // Code Below is showing it in the Page + let result = document.getElementById("data-result-area"); + result.innerHTML = `

${kurtosis.toFixed(2)}

`; + + } + +// For Skewness + function calculateSkewness() + { + + let data = []; + let arr = sanitizeData(); + + for(let i = 0; i <= arr.length - 1; i++){ + let tempob = {dataPoint: arr[i]}; + data.push(tempob) + } + + // console.log(data); + + let columns = { + dataPoint: 'metric' + } + + let stats = new Statistics( data , columns); + + + // Calculating Median + let skewness = stats.skewness(arr) + + + //Code above is calculating Mode + + // Code Below is showing it in the Page + let result = document.getElementById("data-result-area"); + result.innerHTML = `

${skewness.toFixed(2)}

`; + + } + +// For Quantiles + function calculateQuantiles() + { + + let data = []; + let arr = sanitizeData(); + + for(let i = 0; i <= arr.length - 1; i++){ + let tempob = {dataPoint: arr[i]}; + data.push(tempob) + } + + // console.log(data); + + let columns = { + dataPoint: 'metric' + } + + let stats = new Statistics( data , columns); + + + // Calculating Median + let quantile1 = stats.quantile(arr, 0.25) + let quantile2 = stats.quantile(arr, 0.50) + let quantile3 = stats.quantile(arr, 0.75) + let quantile4 = stats.quantile(arr, 0.100) + + + //Code above is calculating Mode + + // Code Below is showing it in the Page + let result = document.getElementById("data-result-area"); + result.innerHTML = `

Q1 = ${quantile1.toFixed(2)}, Q2 = ${quantile2.toFixed(2)}, Q3 = ${quantile3.toFixed(2)}

`; + + } + + function generateTableRows() + { + +let table = document.getElementById("inputTable"); + +let row = table.insertRow(4); + +let cell1 = row.insertCell(0); +let cell2 = row.insertCell(1); + + +let x = table.rows.length; +console.log(x) +cell1.innerHTML = ` `; +cell2.innerHTML = ``; + } + diff --git a/experiment/simulation/css/main.css b/experiment/simulation/css/main.css deleted file mode 100644 index 20bf42b..0000000 --- a/experiment/simulation/css/main.css +++ /dev/null @@ -1 +0,0 @@ -/* You CSS goes in here */ \ No newline at end of file diff --git a/experiment/simulation/index.html b/experiment/simulation/index.html index ee9be23..7c1e715 100644 --- a/experiment/simulation/index.html +++ b/experiment/simulation/index.html @@ -1,13 +1,62 @@ - + - - + + + + + + + + + + + + + + Stats Virtual Lab + + + - + +
+ + +
+

+ + Open Descriptive Statistics Simulations + +

+
+ + +
+ - - - - + + + + \ No newline at end of file diff --git a/experiment/simulation/js/main.js b/experiment/simulation/js/main.js deleted file mode 100644 index 4dbe1cf..0000000 --- a/experiment/simulation/js/main.js +++ /dev/null @@ -1 +0,0 @@ -//Your JavaScript goes in here diff --git a/experiment/simulation/css/README.md b/experiment/simulation/styles/README.md similarity index 100% rename from experiment/simulation/css/README.md rename to experiment/simulation/styles/README.md diff --git a/experiment/simulation/styles/animation.css b/experiment/simulation/styles/animation.css new file mode 100644 index 0000000..f86d833 --- /dev/null +++ b/experiment/simulation/styles/animation.css @@ -0,0 +1,35 @@ +/* This file contians global animations */ + + + + + + +/* Start of Underline animation for links */ +.animation-underline a{ + position: relative; + /* color: #000; */ + text-decoration: none; + } + + .animation-underline a::before { + content: ""; + position: absolute; + display: block; + width: 100%; + height: 2px; + bottom: 0; + left: 0; + background-color: var(--main-secondary); + transform: scaleX(0); + transition: transform 0.3s ease; + } + + .animation-underline a:hover::before { + transform: scaleX(1); + /* Scale from center */ + } + + +/* Start of Underline animation for links */ + \ No newline at end of file diff --git a/experiment/simulation/styles/base.css b/experiment/simulation/styles/base.css new file mode 100644 index 0000000..731e32d --- /dev/null +++ b/experiment/simulation/styles/base.css @@ -0,0 +1,358 @@ + +/* this file should contain base reset, header and footer styles, theme colors */ + + + + + + + + +/* Start of theme variables ---------------------------------------------------------------------------*/ + +:root { + + --main-white: #f0f0f0; + --main-red: #ee9329; + --main-blue: #0951a5; + --main-gray: #303841; + + --main-background: #f6efef; + --main-Paragraph: #2e2e2e; + --main-Headline: #181818; + --main-Button-text: #181818; + --main-button: #4fc4cf; + + --main-stroke: #181818; + --main-highlight: #4fc4cf; + --main-primary: #ffffff; + --main-secondary: #37474F; + --main-tertiary: #fbdd74; + + } + +/* End of theme colors --------------------------------------------------------------------------------*/ + + + + + + + + + + + /*Start of Base styles reset ----------------------------------------------------------------------------*/ + + * { + margin: 0; + padding: 0; + } + + /* box-sizing and font sizing */ + *, + *::before, + *::after { + box-sizing: inherit; + } + + html { + box-sizing: border-box; + + /* Set font size for easy rem calculations + * default document font size = 16px, 1rem = 16px, 100% = 16px + * (100% / 16px) * 10 = 62.5%, 1rem = 10px, 62.5% = 10px + */ + font-size: 62.5%; + scroll-behavior: smooth; + } + + + /* 1200px / 16px = 75em */ + @media (max-width: 75em) { + html { + font-size: 60%; + } + } + + /* 980px / 16px = 61.25em */ + @media (max-width: 61.25em) { + html { + font-size: 58%; + } + } + + /* 460px / 16px = 28.75em */ + @media (max-width: 28.75em) { + html { + font-size: 55%; + } + } + + /*End of Base styles reset----------------------------------------------------------------------------*/ + + + + + + + + + + + + +/* Start of Default element styles ---------------------------------------------------------------------*/ + + ul { + list-style: none; + } + + a { + text-decoration: none; + color: var(--main-Paragraph); + } + + img { + display: block; + width: 100%; + } + +button{ + background-color: var(--main-button); + color: var(--main-Button-text); + border-radius: 10%; + padding: 0 0.5rem; + font-weight: 600; + margin: 1rem; +} + +h1, +h2 { + font-family: 'Raleway', sans-serif; + font-weight: 700; + text-align: center; +} + + + + + body { + font-family: 'Poppins', sans-serif; + font-size: 1.8rem; /* 18px */ + font-weight: 400; + height: 100%; + line-height: 1.4; + color: var(--main-stroke); + background-color: var(--main-background); + padding-bottom: 10vh; + padding-top: 10vh; + + } + +/* End of Default element styles ---------------------------------------------------------------------*/ + + + + + + + + + + + + + +/* Start of Header Styles-------------------------------------------------------------------------------- */ + /* nav */ + +header { + position: fixed; + display: grid; + grid-template-columns:100fr auto; + grid-auto-flow: column; + justify-content: end; + padding: 0 2vw; + align-items: center; + top: 0; + width: 100vw; + min-height: 8vh; + color: var(--main-primary); + background-color: var(--main-secondary); + box-shadow: 0 2px 0 rgba(0, 0, 0, 0.4); + z-index: 10; + margin-bottom: 10vh; +} + +#home-button{ + /* position: absolute; */ + /* right: 2rem; */ + margin-left: 2rem; + margin-right: 2rem +} + +header * { + color: var(--main-primary); +} + +/* header a{ + position: absolute; + display: inline; + right: 1rem; +} */ + +header h4{ + margin: 1rem; + left: 1rem; +} + + + +/* End of Header Styles-------------------------------------------------------------------------------- */ + + + + + + + + + + +/* Start of Footer Styles-------------------------------------------------------------------------------- */ + +footer{ + position: fixed; + width:100vw; + height: 5vh; + padding: 0 1vw; + margin-top: 6vw; + display: inline-flex; + align-items: center; + bottom: 0 ; + color: var(--main-primary); + background-color: var(--main-secondary); +} + +footer a{ + color: var(--main-primary); +} + +/* Start of Footer Styles-------------------------------------------------------------------------------- */ + + + + + + + + + + +/* Start of basic containter */ + +.inline-grid-container{ + display: grid; + padding: 0 1rem; + grid-auto-flow: row; + + /* grid-template-columns: repeat(6,auto); */ +} + +.inline-flex-container{ + display: inline-flex; + padding: 0 1rem; + flex-direction: row; + flex-wrap: wrap; + /* gap: 0; */ + /* grid-template-columns: repeat(6,auto); */ +} + +/* End of basic containter */ + + + + + + + +.welcome-section > h3 { + margin: 0.7rem 0; + } + + +.content-section { + display: flex; + flex-direction: column; + justify-content: center; + align-items: center; + width: 90vw; + /* height: vh; */ + top: 50px; + margin: auto; +} + +.welcome-section > p { + font-size: 3rem; + font-weight: 200; + font-style: italic; + color: var(--main-stroke); +} + + +#result{ + padding: 1em; +} + +.result-groups{ + display: flex; + flex-direction: column; + margin-top: 2rem; + width: 100%; + justify-content: center; + align-items: center; + +} + +.result-box{ + overflow-wrap: anywhere; + border: 1px solid var(--main-stroke); + background-color: var(--main-primary); + min-height: 100px; + max-width: 60%; + min-width: 60%; + + +} + + +#numbers,#frequencies { + min-height: 120px; + width: 100%; + padding: 0.625rem; + resize: vertical; + border: 1px solid var(--main-stroke); + font-size: 2.5rem; +} + +#result-area{ + display: flex; + width:100%; + justify-content: space-between; + margin: auto; +} + +#frequencies{ + margin-top: 2rem; +} + +td input{ + width: 40px; +} + + +td{ + padding: 0 2rem +} + diff --git a/experiment/theory.md b/experiment/theory.md index ef57af9..8e92aad 100644 --- a/experiment/theory.md +++ b/experiment/theory.md @@ -1 +1,28 @@ -### Link your theory in here \ No newline at end of file + + + + + + + + + + +## **Association between the two variables:** + +1. ## **Scatter Plot:** +• Scatter Plots are used to identify underlying relationships among pairs of data sets. +• The plot consists of many scattered points, each point representing an observation. +Based on the distribution in the dataset and the points in the scatter plot, we can infer the outliers in the dataset and the relationship between 2 variable + + +## **Covariance:** +Covariance measures how two variables change together. If one variable tends to increase when the other variable increases, and decrease when the other decreases, they have a positive covariance. If one tends to decrease when the other increases, they have a negative covariance. If there's no consistent pattern, their covariance is close to zero. + +## **Correlation Coefficient:** +The correlation coefficient measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1. +- If it's close to 1, it means there's a strong positive linear relationship (when one variable increases, the other tends to increase). +- If it's close to -1, it indicates a strong negative linear relationship (when one variable increases, the other tends to decrease). +- If it's close to 0, there's little to no linear relationship between the variables. + +Remember, correlation doesn't imply causation, and it only captures linear relationships.