44) Even though two variables, such as sales and advertising, are logically connected, a
regression analysis does not permit the marketing researcher to make cause-and-effect statements
because:
A) Other independent variables are not controlled.
B) Other dependent variables are not controlled.
C) Other independent variables are controlled.
D) Other dependent variables are controlled.
45) The objective of a screening mechanism is to identify the relevant or meaningful variables as
they relate to some ________ of interest.
A) independent variable
B) dependent variable
C) co-dependent variable
D) descriptive variable
46) When regression is used as a screening device, there are several items to report. Which of the
following is NOT one of those items?
A) The dependent variable
B) Statistically significant independent variables
C) All non-statistically significant data
D) Signs of beta coefficients
47) With bivariate regression, one variable is used to predict another variable using the formula
for a straight line.
48) A straight-line relationship underlies regression, but is not a powerful predictive model.
49) Bivariate regression means only two variables are being analyzed, and researchers
sometimes refer to this a “simple regression.”
50) There is a direct cause-and-effect relationship or true dependence between the dependent and
independent variables in regression analysis.
51) In regression analysis, it is strictly a statistical relationship between dependent and
independent variables, not causal.
52) When SPSS or any other statistical analysis program computes the intercept and the slope in
a correlation analysis, it does so on the basis of the least squares criterion.
53) An outlier is a data point that is substantially outside the normal range of the data points
being analyzed.
54) When using a Venn diagram to identify outliers, draw a circle that encompasses most of the
points that appear to be in a circular pattern, then eliminate outliers from the data and rerun the
regression analysis.
55) It is beneficial for the marketing manager and the market researcher to have some sort of
model in mind when designing a research plan.
56) The independent variables that interest market researchers are typically sales, potential sales,
or some attitude held by those who make up the market.
57) A general conceptual model identifies independent and dependent variables and shows their
expected basic relationships to one another.
58) There is an underlying general conceptual model in multiple regression analysis.
59) In the research design determination stage, the researcher and manager reduce the myriad of
independent variables down to a manageable number to be included on the questionnaire.
60) The researcher and the manager identify, measure, and analyze specific variables that pertain
to the general conceptual model in mind.
61) There is no need to perform a great many bivariate regressions, as there is a much better tool
called multiple regression analysis.
62) The addition of independent variables complicates the model conceptualization by adding
more dimensions or axes to the regression situation, but it makes the regression model more
realistic because predictions normally depend on multiple factors, not just one.
63) The terminology is slightly different in places, and some statistics are modified to take into
account the multiple aspects, but for the most part, concepts in multiple regression are not
analogous to those in the simple bivariate case.
64) It is possible to inspect the strength of the linear relationship between the independent
variables and the dependent variable with multiple regression.
65) A multiple regression result is an estimate of the population multiple regression equation
and, just as is the case with other estimated population parameters, it is necessary to test for
statistical significance.
66) Many researchers avoid mentally converting the multiple R value into a percentage, but
rather insist on using a decimal value.
67) The greater the explanatory power of the multiple regression finding, the better and more
useful it is for the researcher.
68) The presence of moderate or stronger correlations among the dependent variables is termed
multicollinearity and will violate the dependence assumption of multiple regression analysis
results when it occurs.
69) When examining the output of any multiple regression, the researcher should inspect the VIF
number associated with each independent variable that is retained in the final multiple regression
equation by the procedure.
70) With multiple regression, it is not necessary to look at the significance level of each
calculated beta.
71) A trimmed regression means that the researcher eliminates the nonsignificant independent
variables and rerun the regression.
72) Generally, during the trimming process, successive iterations sometimes cause the multiple R
to change somewhat. However, it is not advisable to scrutinize this value after each run since the
software automatically performs this action.
73) Iterations during the trimming process will also cause the beta values and the intercept value
to shift slightly; consequently, it is necessary to inspect all significance levels of the betas once
again.
74) Researchers must keep in mind that it is not necessary to run trimmed regressions iteratively
until all betas are significant.
75) Some commonly used dummy variables are gender (males versus female), purchasing
behavior (buyer versus nonbuyer), advertising exposure (recalled versus not recalled), and
purchase history (first time buyer versus repeat buyer).
76) It is common practice to directly compare the beta coefficient for family size to another for
money spent per month on personal grooming.
77) The researcher can compare standardized beta coefficients’ sizes directly, but comparing
unstandardized betas is like comparing apples and oranges.
78) Used as a screening device, multiple regression analysis identifies dependent variables that
qualify to be part of the final equation.
79) Standardized betas indicate the relative importance of alternative predictor variables.
80) Stepwise regression is useful if a researcher has many independent variables and wants to
narrow the set down to a smaller number of statistically significant variables.
81) At the end of the stepwise multiple regression process, all of the significant independent
variables are eliminated from the final multiple regression equation based on the level of
significance stipulated by the researcher in the multiple regression options.
82) With stepwise multiple regression, there is no need to trim and rerun the regression analysis
because SPSS does the trimming automatically based on the stepwise method selected by the
researcher.
83) With stepwise multiple regression output, information on independent variables is taken out
of the multiple regression equation based on nonsignificance. However, researchers must
remember that SPSS stepwise multiple regression will not take into account the VIF statistic.
84) Regression analysis invites us to think in terms of a dependent variable resulting from or
being caused by an independent variable’s actions.
85) Researchers should think about regression analysis in terms of cause and effect.
86) Regression analysis is nothing more than a statistical tool that assumes a linear relationship
between two variables.
87) Since two variables, such as sales and advertising, are logically connected, a regression
analysis permits the marketing researcher to make cause-and-effect statements.
88) The objective of a screening mechanism is to identify the relevant or meaningful variables as
they relate to some dependent variable of interest.
89) For most clients, the independent variable of interest is sales, purchases, intentions to
purchase, satisfaction, or some other variable that translates in some way to how customers
regard or behave toward the company or brand.
90) When regression is used as a screening device, some of the items to report include the
dependent variable and statistically significant independent variables.
91) When regression is used as a screening device, signs of beta coefficients are not reported.
92) When regression is used as a screening device, standardized beta coefficients for the
significant variables are reported.
93) What is regression analysis? How do market researchers use regression analysis?
94) What is the basic concept behind bivariate regression? Describe the basic relationship of
dependent and independent variables.
95) How does a researcher improve regression analysis?
96) What is the underlying general conceptual model in multiple regression analysis? Why are
the roles of the market researcher and the marketing manager important in this conceptual
model?
97) Define multiple regression. What are the basic assumptions behind multiple regression?
98) What do researchers do with mixed significance results that they might find in multiple
regression analysis? Describe the process to address this issue.
99) An important application of multiple regression analysis is as an identifying or screening
device. How is multiple regression analysis used as an identifying or screening device?
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100) What is stepwise multiple regression? When is it useful for a researcher to use this method?