- Instructor: Gaston Sanchez
- Lecture: MWF 11:00-12:00pm 60 Evans
- Tentative calendar (weekly topics), subject to change depending on the pace of the course.
- Notes (:file_folder:) involves material discussed in class.
- Reading (:book:) involves related chapters from SticiGui text.
- 📇 Dates: Jan 22-25
- 📎 Topics: Understanding the concept of "data" for statistical analysis, the concept of variables, and the difference between qualitative and quantitative variables. Also, we'll discuss how to summarize information with frequency tables, and visual displays with bar-charts.
- 📁 Notes:
- Welcome to Stat 131A (chalk and talk)
- What is data?
- Frequency tables and barcharts
- 📖 Reading:
- 🔬 Lab: No lab
- 🔈 To Do: Please spend some time outside class to review the course policies, and piazza etiquette rules.
- 📇 Dates: Jan 28-Feb 01
- 📎 Topics: Understanding histograms, how to read them, how to graph them, and what makes them different from bar-charts. Likewise, we'll talk about measures of center, their meanings, properties, and how to use them in practice. In addition, we'll discuss other summaries for describing distributions.
- 📁 Notes:
- 📖 Reading:
- 🔬 Lab:
- Intro to R and RStudio (Tu; due Jan-30)
- Histograms (Th; due Feb-01)
- 💡 Cheatsheet: RStudio cheat sheet and R markdown cheat sheet
- 🎯 HW 01: Markdown and Histograms (due Feb-03)
- 📇 Dates: Feb 04-08
- 📎 Topics: Having looked at measures of center, we now turn our attention to measures of spread, their rationales, meanings, and properties. Also, we'll get a first contact with the normal curve, and its approximation for symmetric bell-shaped distributions.
- 📁 Notes:
- Measures of spread: Standard Deviation
- Normal Curve
- Normal curve (demo)
- 📖 Reading:
- 🔬 Lab:
- Measures of center (Tu; due Feb-06)
- Measures of Spread (Th; due Feb-08)
- 💡 Cheatsheet: Base R
- 🎯 HW 02: Descriptive Statistics (due Feb-10)
- 📇 Dates: Feb 11-15
- 📎 Topics: Describing one variable at a time can be too limiting. However, we can enrich our analysis by studying whether two (quantitative) variables tend to be associated. Like in the univariate case, this can be done with pictures and numeric summaries: e.g. scatter plots and correlation coefficient.
- 📁 Notes:
- Scatter diagrams and correlation
- More about correlation
- corr-coeff-diagrams (demo)
- 📖 Reading:
- 🔬 Lab:
- Spreads and Normal Curve (Tu; due Feb-13)
- Scatterplots (Th; due Feb-15)
- 💡 Cheatsheet: Data import cheat sheet
- 🎯 HW 03: Correlation (due Feb-17)
- 📇 Dates: Feb 18-22 (Holiday Feb-18)
- 📎 Topics: When the association between two variables meets certain requirements (e.g. linear association, homoscedasticity, football-shaped scatterplot) such a relationship can be further summarized with the so called Regression Line. Consequently, we'll spend time studying the basics of regression, the most (mis)used tool in statistics.
- 📁 Notes:
- Intro to Regression
- Regression Line
- heights-data (demo)
- 📖 Reading:
- 🔬 Lab:
- Correlation (Tu; due Feb-20)
- More Correlation (Th; due Feb-22)
- 🎯 HW 04: Regression (due Feb-24)
- 📇 Dates: Feb 25-Mar 01
- 📎 Topics: We'll continue the discussion of Regression, looking at diagnostics tools, RMS of residuals, the regression effect, and the famous regression fallacy.
- 📁 Notes:
- regression-residuals (demo)
- regression-strips (demo)
- 📖 Reading:
- 🔬 Lab:
- Regression (Tu; due Feb-27)
- 5b: Review session (Th)
- 🎯 HW 05: More Regression (due Mar-03)
- 🎓 MIDTERM 1: Friday Mar-01
- 📇 Dates: Mar 04-08
- 📎 Topics: Statistics can be used for making decisions when we are faced with uncertainties. To do so, you need to learn some of the basic tools provided by Probability, the branch of Math that studies randomness. This part of the course gives you a general introduction to probability.
- 📁 Notes:
- Probability: what is it?
- Probability concepts (part 1)
- Probability concepts (part 2)
- 📖 Reading:
- 🔬 Lab:
- Probability Basics (Tu; due Mar-06)
- Probability Rules 1 (Th; due Mar-08)
- 🎯 HW 06: Probability Basics (due Mar-10)
- 📇 Dates: Mar 11-15
- 📎 Topics: We turn our attention to categorical variables. The objective is to study the relationship between two categorical variables while linking the concept of probability to relative frequencies. This is done via Two-way tables and its associated frequencies (e.g. marginal, conditional, and joint frequencies). We also use introduce the notion of Random Variables, and Box Models.
- 📁 Notes:
- 📖 Reading:
- Chapter 19: Probability Meets Data (Binomial Distribution)
- Chapter 20: Random Variables and Discrete Distributions
- 🔬 Lab:
- Probability Rules 2 (Tu; due Mar-13)
- Box Models (Th; due Mar-15)
- 🎯 HW 07: More Probability (due Mar-17)
- 📇 Dates: Mar 18-22
- 📎 Topics: We dive deep into two of the most famous probability distributions: the Binomial distribution, and the Normal distribution. We also introduce the concepts of expected value and standard deviation/error of tickets randomly sampled from box models.
- 📁 Notes:
- 📖 Reading:
- 🔬 Lab:
- Binomial Probability (Tu; due Mar-22)
- Expected Value (Th; due Mar-24)
- 🎯 HW 08: Binomial Probability (due Mar-24)
- 📇 Dates: Mar 25-29
- 📎 Topics: Recharge your batteries
- 📇 Dates: Apr 01-05
- 📎 Topics: A central topic in statistics has to do with Inference: the problem of obtaining information about a population without examining every unit in the population—--only a random sample from the population. Obviously, random samples vary, so we need to understand how much they vary and how they relate to the population.
- 📁 Notes:
- 📖 Reading:
- 🔬 Lab:
- Exp. Value and SE (Tu; due Apr-03)
- Sampling (Th; due Apr-05)
- 🎯 HW 09: TBA (due Apr-07)
- 📇 Dates: Apr 08-12
- 📎 Topics: Recall that one of the goals of inference is to draw a conclusion about a population on the basis of a random sample from the population. This involves using a probability model that describes the long-run behavior of sample measurements. In this part of the course we continue to develop the probability machinery that underlies inference (i.e. drawing conclusions from sample data).
- 📁 Notes:
- Sample Distributions
- More Sampling distributions (chalk and talk)
- Tentative Review
- 📖 Reading:
- 🔬 Lab:
- Sampling Distributions (Tu; due Apr-10)
- 10b: Review Session
- 🎯 HW 10: No HW
- 🎓 MIDTERM 2: Friday Apr-12
- 📇 Dates: Apr 15-19
- 📎 Topics: In inference, we use a sample to draw a conclusion about a population. Two types of inference are the focus of our work in this course: 1) estimate a population parameter with a confidence interval; 2) test a claim about a population parameter with a hypothesis test. The purpose of a confidence interval is to use a sample statistic (e.g. proportion, mean) to construct an interval of values that we can be reasonably confident contains the population parameter.
- 📁 Notes:
- 📖 Reading:
- 🔬 Lab:
- TBA (Tu; due Apr-17)
- TBA (Th; due Apr-19)
- 🎯 HW 11: TBA (due Apr-21)
- 📇 Dates: Apr 22-26
- 📎 Topics: Now we look more carefully at the second type of inferential task: testing a claim about a population parameter. We begin our discussion of hypothesis tests with research questions that require us to test a claim. Later we look at how a claim becomes a hypothesis.
- 📁 Notes:
- One sample Z-test
- Hypothesis test for one Proportion
- Hypothesis test for one Mean
- 📖 Reading:
- 🔬 Lab:
- TBA (Tu; due Apr-24)
- TBA (Th; due Apr-26)
- 🎯 HW 12: TBA (due Apr-28)
- 📇 Dates: Apr 29-May 03
- 📎 Topics: More about tests of significance.
- 📁 Notes:
- 📖 Reading:
- 🔬 Lab:
- TBA (Tu; due May-01)
- TBA (Th; due May-03)
- 📇 Dates: May 06-10
- 📎 Topics: Prepare for final examination
- 📁 Notes:
- No lecture. Instructor will hold OH (in 309 Evans)
- 🎓 FINAL: May Tu 14, 7-10pm, in Mulford 159
- See announcement about the final test on bCourses