Statistic Exam

Question 1

The multiple R-squared value for a regression represent the proportion of the variation in the Y variable that can explained by its regression on the X variables.

True or False?

Answer 1

True

Question 2

The assumptions which we need to check when we perform a multiple linear regression are (3):

Answer 2

Normality of the errors
Common variance of the errors
Independence of the errors

Question 3

For the Kolmogorov-Smirnov and Shapiro-Wilk tests of Normality, if p < 0.05 then we conclude that the Normality assumption has been satisfied.
True or False?

Multiple linear regression

Answer 3

False

Question 4

Residual calculation for multiple linear regression

Answer 4

Residual = Observations - Fitted Value (using equation)

Question 5

Test signifiance of each predictor, test null and alternate hypothesis that:

Multiple linear regression

Answer 5

H0: b = 0 vs H1 : b≠ 0 (for each particular X variable)

Question 6

If R^2 less than 60 in multiple linear regression then you say

Answer 6

Most of the variation remains unexplained

Question 7

When histogram of residuals does not follow a normal disturbition curve:

Answer 7

Question 8

When scatterplot has no random scatter

Answer 8