Merge pull request #16 from virtual-labs/dev

Dev

experiment/theory.md

@@ -6,6 +6,23 @@
 <img src="images/Image 27-03-23 at 12.40 PM.jpg"/>
 <img src="images/Image 27-03-23 at 12.40 PM (1).jpg"/>
 <img src="images/Image 27-03-23 at 12.43 PM.jpg"/>
-<img src="images/Image 27-03-23 at 12.44 PM.jpg"/>
-<img src="images/Image 27-03-23 at 12.41 PM.jpg"/> 
-<img src="images/Image 27-03-23 at 12.42 PM.jpg"/>
+
+
+## **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.