Question 1

Which statement is not true about the constant b in the bivariate regression equation Y^ i = a + bXi?

Accepted Answer

A)  It is the slope of the regression line and it indicates the expected change in Y when X is changed by one unit. 
B)  It may be computed as b = COVxy/Sx2. 
C)  It is the intercept of the regression line and it indicates the value of Y when X is zero. 
D)  It is usually referred to as the non- standardized regression coefficient. 
A)  It is the slope of the regression line and it indicates the expected change in Y when X is changed by one unit. 
B)  It may be computed as b = COVxy/Sx2. 
C)  It is the intercept of the regression line and it indicates the value of Y when X is zero. 
D)  It is usually referred to as the non- standardized regression coefficient. C

Question 2

Both r and r2 are symmetric measures of association. In other words, the correlation of X and Y is the same as the correlation of Y and X.

Accepted Answer

A) True 
 B)False  True

Question 3

is a procedure for deriving a mathematical relationship, in the form of an equation, between a single metric dependent variable and a single metric independent variable.

Accepted Answer

A)  Multiple regression 
B)  Chi- square 
C)  Part correlation 
D)  Bivariate regression 
A)  Multiple regression 
B)  Chi- square 
C)  Part correlation 
D)  Bivariate regression D

Question 4

In regression with dummy variables, the predicted Y^  for each category is the mean of Y for each category.

Accepted Answer

A) True 
 B)False

Question 5

In multiple regression, if the overall null hypothesis is rejected, we know which specific coefficients (þis) are nonzero.

Accepted Answer

A) True 
 B)False

Question 6

The formula for the coefficient of determination is r2 = SSreg/ SSy.

Accepted Answer

A) True 
 B)False

Question 7

variables may be used as predictors or independent variables by coding them as dummy variables.

Accepted Answer

A)  Categorical 
B)  Ratio 
C)  Interval 
D)  All of the above 
A)  Categorical 
B)  Ratio 
C)  Interval 
D)  All of the above

Question 8

The vertical distance from a point to the regression line is the squared error, e2.

Accepted Answer

A) True 
 B)False

Question 9

When determining the correlation coefficient, r, it does matter which variable is considered to be the dependent variable and which is the independent.

Accepted Answer

A) True 
 B)False

Question 10

If an examination of the residuals indicates that the assumptions underlying linear regression are not met, the researcher can transform the variables in an attempt to satisfy the assumptions.

Accepted Answer

A) True 
 B)False

Question 11

The is a measure of the association between two variables after controlling or adjusting for the effects of one or more additional variables.

Accepted Answer

A)  partial correlation coefficient 
B)  regression analysis 
C)  product moment correlation 
D)  ANOVA 
A)  partial correlation coefficient 
B)  regression analysis 
C)  product moment correlation 
D)  ANOVA

Question 12

The product moment correlation, r2, is an index used to determine whether a linear, or
straight- line, relationship exists between X and Y. It indicates the degree to which the variation in one variable, X, is related to the variation in another variable, Y.

Accepted Answer

A) True 
 B)False

Question 13

The equation for r involves dividing the by .

Accepted Answer

A)  product of the variances of X and Y (Sx2Sy2); COVxy 
B)  COVxy; the product of the variance of X and Y (Sx2Sy2) 
C)  COVxy; the product of the standard deviation of X and Y (SxSy) 
D)  product of the standard deviation of X and Y (SxSy); COVxy 
A)  product of the variances of X and Y (Sx2Sy2); COVxy 
B)  COVxy; the product of the variance of X and Y (Sx2Sy2) 
C)  COVxy; the product of the standard deviation of X and Y (SxSy) 
D)  product of the standard deviation of X and Y (SxSy); COVxy

Question 14

The statistical significance of the linear relationship between X and Y may be tested by examining the hypotheses: H0: fi1 × 0; H1: fi1 = 0.

Accepted Answer

A) True 
 B)False

Question 15

The product moment correlation helps us determine the strength of the association between two metric variables. Regression analysis helps us determine which variables cause a change in other variables.

Accepted Answer

A) True 
 B)False

Question 16

Standardized variables have a mean of 1 and a variance of zero.

Accepted Answer

A) True 
 B)False

Question 17

A partial correlation coefficient measures the association between two variables after controlling for or adjusting for the effects of one or more additional variables.

Accepted Answer

A) True 
 B)False

Question 18

r2 measures .

Accepted Answer

A)  the proportion of error variation 
B)  the proportion of variation in Y related to the variation of the categories of X 
C)  the proportion of variation in one variable that is explained by the other 
D)  the proportion of variation in Y due to the variation within each of the categories of X 
A)  the proportion of error variation 
B)  the proportion of variation in Y related to the variation of the categories of X 
C)  the proportion of variation in one variable that is explained by the other 
D)  the proportion of variation in Y due to the variation within each of the categories of X

Question 19

A technique for fitting a straight line to a scattergram by minimizing the square of the vertical distances of all the points from the line is known as the _ .

Accepted Answer

A)  scatter diagram plot 
B)  maximum residual procedure 
C)  sum of square errors procedure 
D)  least- square procedure 
A)  scatter diagram plot 
B)  maximum residual procedure 
C)  sum of square errors procedure 
D)  least- square procedure

Question 20

If a variable explains a significant proportion of the residual variation, it should be considered for inclusion in the regression equation.

Accepted Answer

A) True 
 B)False

Exam 17: Correlation and Regression

Which statement is not true about the constant b in the bivariate regression equation Y^ i = a + bXi?

Both r and r2 are symmetric measures of association. In other words, the correlation of X and Y is the same as the correlation of Y and X.

is a procedure for deriving a mathematical relationship, in the form of an equation, between a single metric dependent variable and a single metric independent variable.

In regression with dummy variables, the predicted Y^ for each category is the mean of Y for each category.

In multiple regression, if the overall null hypothesis is rejected, we know which specific coefficients (þis) are nonzero.

The formula for the coefficient of determination is r2 = SSreg/ SSy.

variables may be used as predictors or independent variables by coding them as dummy variables.

The vertical distance from a point to the regression line is the squared error, e2.

When determining the correlation coefficient, r, it does matter which variable is considered to be the dependent variable and which is the independent.

If an examination of the residuals indicates that the assumptions underlying linear regression are not met, the researcher can transform the variables in an attempt to satisfy the assumptions.

The is a measure of the association between two variables after controlling or adjusting for the effects of one or more additional variables.

The product moment correlation, r2, is an index used to determine whether a linear, or straight- line, relationship exists between X and Y. It indicates the degree to which the variation in one variable, X, is related to the variation in another variable, Y.

The equation for r involves dividing the by .

The statistical significance of the linear relationship between X and Y may be tested by examining the hypotheses: H0: fi1 × 0; H1: fi1 = 0.

The product moment correlation helps us determine the strength of the association between two metric variables. Regression analysis helps us determine which variables cause a change in other variables.

Standardized variables have a mean of 1 and a variance of zero.

A partial correlation coefficient measures the association between two variables after controlling for or adjusting for the effects of one or more additional variables.

r2 measures .

A technique for fitting a straight line to a scattergram by minimizing the square of the vertical distances of all the points from the line is known as the _ .

If a variable explains a significant proportion of the residual variation, it should be considered for inclusion in the regression equation.

Introduction to Marketing

Defining the Marketing Research

Research Design

Exploratory Research Design

Exploratory Research Design

Descriptive Research Design

Causal Research Design

Measurement and Scaling

Measurement and Scaling

Questionnaire and Form

Sampling: Design and

Sampling: Final and Initial

Fieldwork

Data Preparation

Frequency Distribution, Crosstabulation, and Hypothesis

Analysis of Variance and

Discriminant and Logit

Factor Analysis

Cluster Analysis

Multidimensional Scaling and

Structural Equation Modeling

Report Preparation and

International Marketing

Filters

Which statement is not true about the constant b in the bivariate regression equation Y^{^}_i= a + bX_i?

Both r and r²are symmetric measures of association. In other words, the correlation of X and Y is the same as the correlation of Y and X.

In regression with dummy variables, the predicted Y^{^}for each category is the mean of Y for each category.

In multiple regression, if the overall null hypothesis is rejected, we know which specific coefficients (þ_is) are nonzero.

The formula for the coefficient of determination is r²= SS_reg/SS_y.

The vertical distance from a point to the regression line is the squared error, e².

The product moment correlation, r², is an index used to determine whether a linear, or straight- line, relationship exists between X and Y. It indicates the degree to which the variation in one variable, X, is related to the variation in another variable, Y.

The statistical significance of the linear relationship between X and Y may be tested by examining the hypotheses: H₀: fi₁× 0; H₁: fi₁= 0.

r²measures .