a) Means, sums of squares and cross products, standard deviations, and the correlation between X and Y.
b) Regression equation of Y on X.
c) Regression and residual sum of squares.
d) F ratio for the test of significance of the regression of Y on X, using the sums of squares (i.e., SSreg and SSres) and r_xy^2.
e) Variance of estimate and the standard error of estimate.
f) Standard error of the regression coefficient.
g) T ratio for the test of the regression coefficient. What should the square of the t equal? (In other words, what statistical calculated above should it equal?)
Using the regression equations, calculate the following:
h) Each person's predicted scores, Y', on the basis of the X's (Report the first 3 subjects).
i) The sum of the predicted scores and their mean.
j) The residuals between the observed and predicted scores (y-y') for each person and their sum, ?(y-y^' ), and the sum of the squared residuals, ?(y-y')^2.
k) Plot the data, the regression line, and the standardized residuals against the predicted scores.
Step by step method for computing Regression analysis in SPSS is given in the answer.