Regression Basics

This post is on basics of Regression and Steps Involved. Linear Regression defines relationships between variables involved. We use it to identify relationships between variables.

Steps Involved

• Plot line between Independent Variable in X Axis, Dependent Variable Y Axis
• Identify if their positive or negative relationship (When X increases with respective to Y it is positive)
• Plot a line that minimizes errors between estimates / actuals

Y = B0 + B1X (B0, B1 Derived Mathematically)
where B0 is Y Intercept, B1 is Slope

R Squared 

R Squared Verification 

• How well regression line predicts actual values
• Take Actual values (compute mean of them). Distance between actual value of mean will sum up to zero
• Perfect fit R square equals 1

Standard Error of Estimates

• Compare estimated values vs Actual Values
• Distance between estimated and actual values

Correlation Coefficient

• Fit the line
• Remember slope +ve or -ve
• Scatter along Y and X Axis
• High Correlation means good fit

In next post we will look @ R Examples

Happy Learning!!!