Deck 13: Regression Analysis

Question 1

Linear regression analysis goes beyond Pearson's product moment correlation because of its ability to _________________ the value of one variable from another variable.

A) predict
B) lower
C) raise
D) correlate

Answer 1

A

Question 2

_________________ uses correlation as a basis to predict the value of one variable from the value of a second variable or combination of several variables.

A) linear regression analysis
B) multilinear regression analysis
C) curvilinear regression analysis
D) correlation

Answer 2

A

Question 3

A ___________________ can be used to estimate what the dependent variable would be if you know the value of the independent variable.

A) X-Y relationship line
B) regression line
C) correlation line
D) fitting line

Answer 3

B

Question 4

_______________________ occurs when there is a strong relationship among the independent variables in a multiple regression.

A) multicollinearity
B) variance of inflation
C) normality
D) homoscedasticity

Question 5

The _______________________ is a line of the plot which best represents the pattern of the relationship.

A) X-Y relationship line
B) correlation line
C) line of best fit
D) fitting line

Question 6

In the regression equation, when X = 0 , what is the value of Y?

A) a
B) b
C) X
D) Y

Question 7

What element of the regression equation provides information about the direction of the relationship?

A) X
B) Y
C) a
D) b

Question 8

Simple linear regression assumes that the relationship between in the independent and dependent variables is linear. This assumption is known as what?

A) Linear requirement
B) Linearity
C) Bivariate linearity
D) Linear development

Question 9

Jennifer is performing an analysis in which the outside temperature and days until next holiday are used to predict the number of employees who take the day off. This is an example of what type of regression?

A) multiple regression
B) simple linear regression
C) bivariate regression
D) curvilinear regression

Question 10

The differences between the regression line and the observed values are known as ___________________.

A) residual sum of squares
B) total sum of squares
C) line of best fit
D) residuals

Question 11

Typically, X is assigned to represent the ______________________ variable and Y is assigned to represent the ___________________ variable.

A) outcome; predictor
B) predictor; outcome
C) outcome; independent
D) dependent; independent

Question 12

___________________ refers to the assumption that the degree of random noise in the dependent variable remains the same regardless of the values of the independent variables.

A) normality
B) linearity
C) homoscedasticity
D) regression

Question 13

In linear regression, the variable being used to predict the outcome variable is the __________________ variable.

A) independent
B) dependent
C) extraneous
D) control

Question 14

In the regression equation, 'a' represents the _______________________.

A) dependent variable
B) independent variable
C) intercept
D) slope

Question 15

You are trying to predict students test scores based on their hours spent studying. This is an example of what type of regression analysis?

A) multiple regression
B) simple linear regression
C) complex linear regression
D) curvilinear regression

Question 16

What is the basic formula for a bivariate regression?

A) Y = b + aX
B) X = b + aY
C) Y = a + bX
D) X = a + bY

Question 17

The ____________________ is used to measure the amount of variance in one variable explained by another variable.

A) intercept
B) slope
C) critical coefficient
D) coefficient of determination

Question 18

Simple linear regression uses what level of measurement?

A) categorical
B) nominal
C) ordinal
D) continuous

Question 19

________________________ are scores that have a mean of 0 and a standard deviation of 1.

A) b-scores
B) z-scores
C) residuals
D) coefficients

Question 20

When dummy coding, the category designated at the dummy variable with a value of 0 is called the ________________.

A) zero group
B) dummy group
C) independent group
D) reference group

Question 21

Identify a research question for which you would like to use a regression analysis. Why is regression your choice for such a question? Is your question suited to a bivariate or multiple linear regression?

Question 22

Normality is the assumption in linear regression that the dependent variable is continuous and normally distributed.

Question 23

By finding relationship between cultural competency and number of diversity trainings attended, Emily hopes to gain support for more diversity trainings.

Question 24

What are the assumptions of a linear regression? How do they vary or seem similar to other statistical tests?

Question 25

The coefficient of determination can take a value ranging from 0 to 1.

Question 26

In the regression equation, the slope is represented by what symbol?

A) Y
B) X
C) a
D) b

Question 27

R² is calculated as _____________________.

A) 1 - (total sum of squares/residual sum of squares)
B) 1 + (total sum of squares/residual sum of squares)
C) 1 - (residual sum of squares/total sum of squares)
D) 1 + (residual sum of squares/total sum of squares)

Question 28

In Mary's case she wants to predict the volunteer hours based on the volunteers' level of income. In this example, volunteer hours is the independent variable.

Question 29

In linear regression the independent variable is often referred to as the outcome variable.

Question 30

Describe how the regression line is related to the regression equation. How can one predict values based on the regression equation?

Question 31

The regression line determines the prediction of the dependent variable from the independent variable.

Question 32

Normality is assumed with a linear regression.

Question 33

Residual sum of squares is abbreviated as RSS

Question 34

A common method of visually representing the data in a regression analysis is through the use of a __________________.

A) boxplot
B) frequency polygon
C) scatterplot
D) histogram

Question 35

Multiple regression analysis has two variables of interest, one dependent and one independent.

Question 36

In a linear regression the variable that is being predicted, is the __________________ variable.

A) independent
B) dependent
C) extraneous
D) control

Question 37

Correlation, chi square and linear regression are all interested in looking at the _________________ between variables.

A) relationships
B) differences
C) frequency distributions
D) normal distributions
True/False

Question 38

It is possible to integrate categorical variables into the regression equation as dummy variables.