4 - Linear Regression

Question 1

Assumptions the model makes

Answer 1

1. There exists some linear relationship between Y & X
2. 3.
   4.

Question 2

What is used to find the “best” line

Answer 2

Residual Sum of Squares (RSS)

Question 3

What does the RSE do?

Answer 3

serves as a measure of the amount of training error in the model

Question 4

What is r^2?

Answer 4

The coefficient of determination. Is it the proportion of the total variation that is explained by the regression line

It is a measure of how close to the fitted line is to the observed data

Question 5

What what the Correlation Coefficient (r) tell us?

Answer 5

tells us if there is a negative or positive linear relationship between X and Y(and measures the strength of that relationship)

• its a value between -1 and 1