Introduction to Linear Regression
Completion requirements
This section defines simple linear regression, uses scatter plots to reveal linear patterns, and talks about prediction error. It also discusses how to compute regression line by minimizing squared errors.
Answers
- Plug \( X = 4 \) into the equation to find \( Y'= 3(4) - 2 = 10 \)
- Plug \( A = 40 \) into the equation to find \( B'= 2.3(40) + 9.5 = 101.5 \)
- Predicted value, \( B' = 2.3(32.5) + 9.5 = 84.25 \); Error of prediction = \( B - B' = 95.25 - 84.25 = 11 \)
- Plug \( A = 40 \) into the equation to find \( B'= 2.3(40) + 9.5 = 101.5 \)
- Someone who scored the mean on \( X \) would be predicted to score the mean on \( Y \).
- \( b = r(sY/sX) = .5(1/2) = .25 \)
- \( b = 0.9143 \)