Multiple Linear Regression

Question 1

a statistical technique that uses several explanatory/independent variables to predict the outcome of a response/dependent variable

Answer 1

multiple linear regression

Question 2

is an extension of simple linear regression that uses just one explanatory variable.

Answer 2

multiple linear regression

Question 3

Method for studying the relationship between a dependent variable and two or more independent variables.

Answer 3

multiple linear regression analysis

Question 4

purpose of multiple linear regression analysis

Answer 4

prediction
explanation
theory building

Question 5

multiple linear regression analysis requirement

___ dependent variable (criterion)

Answer 5

one

Question 6

multiple linear regression analysis requirement

___ or more independent variables

Answer 6

two

Question 7

multiple linear regression analysis requirement

sample size is

Answer 7

> =50 (at least 10 times as many cases as independent variables)

Question 8

assumptions about MLR

the scores of any particular subject are independent of the scores of all other subjects

what assumptions

Answer 8

independence

Question 9

assumptions about MLR

: in the population, the scores on the dependent variable are normally distributed for each of the possible combinations of the level of the X variables; each of the variables is normally distributed

Answer 9

normality

Question 10

in the population, the variances of the dependent variable for each of the possible combinations of the levels of the X variables are equal.

what MLR assumption

Answer 10

homoscedascity

Question 11

what we are trying to predict

Answer 11

criterion variable

Question 12

: In the population, the relation between the dependent variable and the independent variable is linear when all the other independent variables are held constant.

what MLR assumtion

Answer 12

linearity

Question 13

One dependent variable Y predicted from one independent variable X

Answer 13

simple regression

Question 14

One dependent variable Y predicted from a set of independent variables (X1, X2 ….Xk)

Answer 14

multiple regression

Question 15

One regression coefficient

Answer 15

simple regression

Question 16

One regression coefficient for each independent variable

Answer 16

multiple regression

Question 17

r^2: proportion of variation in dependent variable Y predictable from X

Answer 17

simple linear regression

Question 18

r^2: : proportion of variation in dependent variable Y predictable by set of independent variables (X’s)

Answer 18

multiple regression

Question 19

Examines the appropriateness of the model for the data

Answer 19

diagnostic checking

Question 20

techniques for diagnostic checking

Answer 20

graphical analysis of residuals
statistical tests

Question 21

1:___ratio for the number of samples and variables

Answer 21

20

Question 22

Variables are ____

Answer 22

continuous

Question 23

If there are ___or ___, then less than full rank model will be applied.

Answer 23

ordinal
categorical

Question 24

r^2 explanation

r^2: 0.936

Answer 24

93.6% of the variation of the annoyance was explained by the model (specifically the roughness and the sharpness of the sound). Only 5.4% was not explained by the model

Question 25

omnibus ANOVA is <0.05

interpret

Answer 25

The model is significant in explaining the annoyance