Chapter 15

Question 1

Describe the independent and dependent variable?

Answer 1

Independent variable: used to predict the independent
variable (x in the regression straight-line equation)

Dependent variable: that which is predicted (y in the
regression straight-line equation

Question 2

What is the least squares criterion?

Answer 2

Used in regression analysis and guarantees that the “best” straight-line slope and intercept will be calculated

Question 3

What is a multiple regression analysis?

Answer 3

Uses the same concepts as
bivariate regression analysis, but uses more than one independent variable.

Regression plane is the shape of the dependent variables.

y=a+ bx+b1x1+b2x2+b3x3….

Question 4

What is Multiple R?

Answer 4

Multiple R: also called coefficient of determination,
is a measure of the strength of the overall linear relationship in multiple regression.

It indicates how well the independent variables can
predict the dependent variable.

Ranges from 0 to +1 and represents the amount of dependent variable that is “explained” or accounted for

Question 5

What are basic assumptions in multiple regression?

Answer 5

Additivity: each new independent variable is added to the regression equation

Independence: independent variables must be independent and uncorrelated from one another (strong correlations between independent variables called multicollinearity)

Variance Inflation Factor (VIF): used to assess and eliminate multicollinearity

• VIF is statistical value that identifies what independent variables contribute to multicolinearity and should be removed
• VIF > 10, removed

Question 6

What is a stepwise regression?

Answer 6

Stepwise regression is useful when there are many
independent variables, and a researcher wants to narrow the set down to a smaller number of statistically significant variables

Question 7

What does a trimmed regression mean?

Answer 7

You eliminate the nonsignificant independent variables and rerun the regression