stats 9

Question 1

Multivariate regression

Answer 1

a regression model with more than 2 variables, which allows researchers to control for the impact of potentially confounding variables on the dependent variable when examining the relationship between an independent variable of interest and a dependent variable

Question 2

population regression model

Answer 2

a theoretical formulation of the proposed linear relationship between at least one independent variable and a dependent variable

Question 3

the population regression model specifies

Answer 3

the relationship we theorize to exist between our variables in the real world

Question 4

The beta coefficients in multiple regression fit a

Answer 4

hyperplane to the data

Question 5

In three dimensions (with 2 ind and 1 dependent), a multivariate regression fits a

Answer 5

plane

Question 6

in higher dimensions (with more than two independent variables and a dependent variable), a multivariate regression fits a

Answer 6

hyperplane that exists in that multi-dimensionsal space

Question 7

Multiple regression only controls for the

Answer 7

variables that are measured and included in the equation

Question 8

MR uses statistical controls which are not as effective as

Answer 8

isolating the effects of X on Y as experimental controls

Question 9

you cannot compare beta coefficients from a regression table because they are

Answer 9

unstandardized

Question 10

Unstandardized coefficients:

Answer 10

regression coefficients such that the rise-over-run interpretation is expressed in the original metrics of each variable

Question 11

Substantive significance

Answer 11

a judgment call about whether or not statistically significant relationships are “large” or “small” in terms of their real-world impact.

Question 12

multivariate regression requires one more assumption

Answer 12

No perfect multicollinearity – there can be no exact linear relationship between two or more of the independent variables in the model