Answer in Statistics and Probability for Willy #86958
“a=frac{(Sigma(X))( Sigma(Y^2))-(Sigma(Y))( Sigma(XY))}{n (Sigma(Y^2))-(Sigma(Y))^2}”
“b=frac{n(Sigma(XY))-(Sigma(Y))( Sigma(X))}{n (Sigma(Y^2))-(Sigma(Y))^2}”
“a={15(151)-25(88) over 5(151)-(25)^2}=0.5”
“b={5(88)-15(25) over 5(151)-(25)^2}=0.5”
The equation is
“X=0.5Y+0.5”
CLASSICAL LINEAR REGRESSION MODEL ASSUMPTIONS
1. A linear regression exists between the dependent variable and the independent variable.
2. The independent variable is not random.
3. The expected value of the error term is 0.
This assumption says that, on average, we expect the impact of all left-out factors in our model to be zero.
4. The variance for the error term is the same for all observations. (homoscedasticity; the complementary concept is called heteroscedasticity).
5. The error term is normally distributed.
6. The error term is uncorrelated across observations.
