Chapter 15 Understanding Regression Analysis Basics
1) Which of the following is NOT true of prediction?
A) It is a statement of what is believed will happen in the future.
B) It may be based on prior observation.
C) We are seldom confronted with the need to make predictions.
D) It may be based on past experience.
E) Marketing managers are constantly faced with the need to make predictions.
Answer: C
2) One of the best tools for unraveling prediction complexity is:
A) probability sampling.
B) association analysis.
C) differences testing.
D) linear regression.
E) causal research.
Answer: D
3) What is the best way to make a prediction?
A) using simple statistical analysis
B) making a best guess based on past experience
C) employing residual analysis
D) building a predictive model
E) hypothesizing
Answer: D
4) A predictive model is an approach to prediction that:
A) relates the conditions expected to be in place influencing the factor that is being predicted.
B) observes a consistent pattern over time.
C) identifies a pattern and projects it into the future.
D) uses past experience to predict the future.
E) uses current experience to explain the past.
Answer: A
5) ________ helps the researcher to understand whether observed data is truly linear and
whether the data is a good fit to the model being used.
A) Analysis of prediction
B) Control data
C) Analysis of residuals
D) Analysis of variance
E) Values comparison
Answer: C
6) Which of the following residuals shows an exact prediction?
A) 0
B) +1.0
C) -25
D) +25
E) 100.0
Answer: A
7) Bivariate regression analysis is defined as a predictive analysis technique in which:
A) a pattern is identified over time and projected into the future.
B) a relationship that exists across time is observed to make a prediction.
C) one variable is used to predict the level of another by use of the straight-line formula.
D) one variable is used to predict the level of another by use of a scatter diagram.
E) a relationship that exists at one point in time is observed to make a prediction.
Answer: C
8) ________ is a simple technique for analyzing two variables to predict behavior or activity
in the marketplace.
A) Regression analysis
B) Variance analysis
C) Bivariate regression
D) Multiple regression
E) Stepwise regression
Answer: C
9) In the formula for a straight line, the intercept is known as:
A) the dependent variable.
B) the variable used to predict the dependent variable.
C) the change in y for any unit change in x.
D) the point where the line cuts the y axis when x = 0.
E) b.
Answer: D
10) In the formula for a straight line, the slope is defined as:
A) the change in y for any 1-unit change in x.
B) where the line cuts the y axis when x = 0.
C) the variable used to predict the dependent variable.
D) the dependent variable.
E) the predicted variable.
Answer: A
11) In bivariate regression analysis, the dependent variable is one that is:
A) used to predict the independent variable, and it is the x in the regression formula.
B) used to predict the independent variable, and it is the y in the regression formula.
C) predicted, and it is usually termed x in the regression formula.
D) predicted, and it is usually termed y in the regression formula.
E) predicted, and it is termed b in the regression formula.
Answer: D
12) In bivariate regression analysis, the independent variable is one that is:
A) used to predict the dependent variable, and it is the x in the regression formula.
B) used to predict the dependent variable, and it is the y in the regression formula.
C) predicted, and it is the x in the regression formula.
D) predicted, and it is the y in the regression formula.
E) used to predict the dependent variable, and it is the b in the regression formula.
Answer: A
13) What criterion is used to establish the best "fit" of a straight line through the points on a
scatter diagram?
A) the plum line criterion
B) the least squares criterion
C) the bearing line criterion
D) the b slope criterion
E) the right angle criterion
Answer: B
14) In evaluating your bivariate regression analysis findings, you first determine whether or
not a linear relationship between the independent and dependent variable exists in the
population. Which of the following best describes what you are doing in this step?
A) determining if the two variables have any covariation
B) determining if the two variables vary together
C) determining if the two variables belong in the same regression matrix
D) determining if the two variables are isotonic
E) determining if there is statistical significance
Answer: E
15) In evaluating your bivariate regression analysis findings, you first determine whether or
not a linear relationship between the independent and dependent variable exists in the
population and secondly you:
A) determine the significance of the intercept and the slope.
B) determine the significance of the covariation.
C) determine if the two variables vary together.
D) determine if the two variables belong in the same regression matrix.
E) determine if the two variables predict the intercept and the slope.
Answer: A
16) Which of the following SPSS commands allows you to run bivariate regression?
A) ANALYZE; BIVARIATE; REGRESSION
B) ANALYZE; REGRESSION; BIVARIATE
C) REGRESSION; BIVARIATE
D) ANALYZE; REGRESSION; LINEAR
E) REGRESSION; BIVARIATE; LINEAR
Answer: D
17) In bivariate regression analysis, the higher the Adjusted R Square value:
A) the lower the predictive power of the analysis.
B) the better the straight line's fit to the scatter points.
C) the worse the straight line's fit to the scatter points.
D) the closer to 0 it will be.
E) None of the above; there is no Adjusted R Square value in regression analysis.
Answer: B
18) The main purpose of ANOVA in bivariate regression is to:
A) tell us if there are significant differences between three or more means.
B) tell us if ANOVA is an issue.
C) tell us if the straight-line model fits the data we are analyzing.
D) provide a frequency table for further analysis.
E) None of the above; ANOVA is not used in regression.
Answer: C
19) In bivariate regression, if the F value is significant (say .05 or less), then:
A) we accept the null hypothesis that a straight-line model fits our data.
B) we reject the null hypothesis that a straight-line model does not fit our data.
C) we abandon our efforts to analyze the two variables.
D) we check for outliers.
E) we rerun the regression.
Answer: B
20) In bivariate regression, if the F value is not significant (say .051), then:
A) we accept the null hypothesis that a straight-line model fits our data.
B) we reject the null hypothesis that a straight-line model does not fit our data.
C) we abandon our efforts to analyze the two variables.
D) we check for outliers.
E) we rerun the regression.
Answer: C
21) Sometimes a researcher will find that the ANOVA F is not significant in regression
analysis or if the F is significant, the R square is lower than desired. It is appropriate in these
cases to:
A) examine the data using another stat package other than SPSS.
B) change the scaling assumptions from ratio or interval to ordinal and rerun the analysis.
C) run a confidence interval around the predicted values and then make the interval narrower.
D) run a confidence interval around the predicted values and then make the interval wider.
E) run a scatter diagram, search for outliers, and remove them and rerun the regression.
Answer: E
22) A form of regression analysis where more than one independent variable is used in the
regression equation is known as:
A) regression planes.
B) additivity.
C) multiple regression analysis.
D) independence assumption.
E) MANOVA.
Answer: C
23) If Maxwell House Coffee was considering a line of gourmet iced coffee, it would want to
know how coffee drinkers feel about gourmet iced coffee; that is, their attitudes toward
buying, preparing, and drinking it would be the dependent variables. Maxwell House might
consider developing:
A) a general conceptual model.
B) a general conceptual model that identifies the independent and dependent variables.
C) a specific conceptual model that specifies the variables that will produce residuals
analysis.
D) a specific conceptual model that will require additional modification to be used in
residuals analysis.
E) a conceptual model that identifies the residuals that are associated with the dependent, or
slope, variable.
Answer: B
24) A graph of the dependent variable in multiple regression analysis is referred to as:
A) confidence intervals.
B) multiple regression.
C) multiple scatter plots.
D) regression plane.
E) a multi-scatter plot.
Answer: D
25) A multiple regression equation is best described by which of the following forms?
A) The independent variable is predicted by the intercept plus a series of values of the slope
times each dependent variable.
B) The independent variable is predicted by the slope plus a series of values of the intercept
times each dependent variable.
C) The dependent variable is predicted by the intercept plus a series of values of the slope
times each independent variable.
D) The dependent variable to be predicted is equal to the intercept plus a series of values of
the slope times each independent variable.
E) y = a + bx
Answer: D
26) Which of the following in multiple regression is a handy measure of the strength of the
overall relationship?
A) Adjusted R
B) Multiple R
C) multicollinearity
D) VIF
E) Adjusted B
Answer: B
27) Which of the following stipulates that independent multiple regression variables must be
statistically independent and uncorrelated with one another?
A) independence assumption
B) multicollinearity
C) additivity requirement
D) regression plane
E) uncorrelation
Answer: A
28) In multiple regression, the presence of correlations among the independent variables is
termed:
A) independence assumption.
B) multicollinearity.
C) additivity.
D) regression plane.
E) multicorrelation.
Answer: B
29) Which statistic is used to determine whether or not multicollinearity is a concern in
multiple regression?
A) coefficient of determination
B) multicol Z
C) multicol R
D) VIF (variance inflation factor)
E) Q
Answer: D
30) When the statistic used to determine whether or not multicollinearity is a concern in
multiple regression is greater than ________, it is prudent to remove that variable and rerun
the regression.
A) .05
B) .10
C) .95
D) 1.00
E) 10
Answer: E
31) What is the proper SPSS command sequence to run multiple regression analysis?
A) ANALYZE; REGRESSION; MULTIPLE; GO
B) ANALYZE; REGRESSION; MULTIPLE
C) ANALYZE; REGRESSION; LINEAR
D) ANALYZE; REGRESSION; MLINEAR
E) ANALYZE; REGRESSION; MR
Answer: C
32) In multiple regression, you must test for the significance of the betas for each of the
independent variables. You would do this by looking for:
A) a significant t test for each independent variable.
B) a significant ANOVA for each independent variable.
C) a significant alpha level for each independent variable.
D) a significant nonlinear beta weight for each independent variable.
E) a significant R for each independent variable.
Answer: A
33) When you find "mixed" results in multiple regression (i.e., some betas are significant,
others are not), you:
A) eliminate, or "trim," the insignificant variables.
B) adjust the insignificant variables by applying a standardized weight.
C) accept the null hypothesis.
D) choose the result that fits your hypothesis.
E) none of the above
Answer: A
34) When we make a prediction using multiple regression, we can apply a 95 percent
confidence interval around the predicted dependent variable by multiplying:
A) 1.96 times the standard error of the predictor.
B) 1.96 times the standard error of the estimate.
C) 2.58 times the standard error of the predictor.
D) 2.58 times the standard error of the estimate.
E) 1.96 times .95.
Answer: B
35) While the scaling assumptions of multiple regression require that both the independent
and dependent variables be at least interval-scaled, we may use nominal independent
variables by using:
A) ratio-scaled variables.
B) standardized beta coefficients.
C) dummy variables.
D) temporary variables.
E) semi-ratio variables.
Answer: C
36) A standardized beta coefficient is defined as:
A) the result of adding the difference between each independent variable value and its mean
and the standard deviation of that independent variable.
B) the result of multiplying the difference between each independent variable value and its
mean by the standard deviation of that independent variable.
C) the result of dividing the standard deviation of an independent variable by the difference
between that independent variable value and its mean.
D) the result of dividing the difference between each independent variable value and its mean
by the standard deviation of that independent variable.
E) the result of subtracting the difference between each independent variable value and its
mean by the standard deviation of that independent variable.
Answer: D
37) Independent variables are normally measured in different units, so to determine the
relative importance of the beta weights between independent variables we would use:
A) a screening variable.
B) a trimmed model.
C) standardized beta coefficients.
D) betas measured in "like-units."
E) weighted beta coefficients.
Answer: C
38) Researchers applied multiple regression analysis to study mobile phone service in
Thailand, using overall satisfaction as the dependent variable. Standardized betas for
independent variables were calculated. Of those listed below, which is the most important
factor for a Thai mobile phone company trying to increase its competitiveness?
A) quality of service, standardized beta = .139
B) promotions by the company, standardized beta = .158
C) innovativeness by the company, standardized beta = .060
D) social status of the company brand, standardized beta = -.013
E) customer service quality, standardized beta = .155
Answer: B
39) Which form of regression is useful when the researcher has many independent variables
and wants to narrow the set down to a smaller number?
A) multiple component reduction
B) stepwise multiple regression
C) variance deflation regression
D) variance inflation regression
E) narrow regression
Answer: B
40) Which sequence of SPSS commands would you select in order to run stepwise multiple
regression?
A) ANALYZE; REGRESSION; LINEARSTEPS
B) ANALYZE; REGRESSION; LINEAR; METHOD; STEPWISE
C) STEPWISE; LINEAR REGRESSION; GO
D) STEPWISE; LINEAR REGRESSION
E) ANALYZE; REGRESSION; METHOD; STEP
Answer: B
41) Which of the following are warnings that the textbook authors give regarding regression
analysis?
A) It is complicated and requires large computer memory.
B) It does not give you cause-and-effect statements, and it is expensive to run.
C) It does not give you cause-and-effect statements, and the text's coverage of regression
analysis only scratches the surface of this topic.
D) It is expensive, and you should not apply regression to predict data outside the boundaries
of the data used to develop the regression model.
E) No warnings are given.
Answer: C
42) A prediction is a statement of what is believed will happen in the future made on the basis
of past experience or prior observation.
Answer: True
43) The two ways of making a prediction are extension analysis and baseline predictive
modeling.
Answer: False
44) A predictive model simply examines what has happened in the past and predicts the
future.
Answer: False
45) When we make predictions and compare the differences between our predictions and the
actual results, we are performing what is known as analysis of residuals.
Answer: True
46) When we want to use one variable to predict another and use the equation: y = a + bx, we
use the technique known as multiple regression.
Answer: False
47) In the following straight-line formula, y = a + bx, the variable being predicted is the beta
weight, b.
Answer: False
48) In the formula for bivariate regression analysis, the change in y for each one-unit change
in x is known as the slope.
Answer: True
49) In the formula for bivariate regression analysis, the point where the line cuts the y axis
when x = 0 is known as b, the beta.
Answer: False
50) In regression, the variable being predicted, y, is known as the dependent variable.
Answer: True
51) In regression, the variable being predicted, b, is known as the dependent variable.
Answer: False
52) In regression, the variable used to predict the dependent variable is known as x, the
independent variable.
Answer: True
53) In regression, the line that runs through the points on a scatter diagram is positioned to
minimize the vertical distances away from the line of the various points because of the least
squares criterion.
Answer: True
54) A regression line using the least squares criterion will result in high residuals.
Answer: False
55) Immediately in bivariate analysis, the researcher must find out whether or not a linear
relationship exists in the population.
Answer: True
56) The R Square value is very important because it tells us how well our regression line fits
the scatter of data points. It may range from 0 to +1.00 because it is the square of the
correlation coefficient, which may range from -1.00 to +1.00.
Answer: True
57) If the ANOVA F test is not significant in bivariate regression analysis, we must trim the
model by eliminating the insignificant dependent variable(s).
Answer: False
58) In bivariate regression analysis, t tests are used to test the significance of the slope and
the intercept of the multiple dependent variables.
Answer: False
59) If the tests of the significance of the slope and the intercept are significant, this means
that the straight-line relationship depicted by the slope and the intercept actually exists in the
population and, therefore, the regression equation may be used as a prediction device.
Answer: True
60) In bivariate regression, we can calculate an upper and lower range within which we could
expect the values of the independent values to fall if they were calculated.
Answer: False
61) We can sometimes improve a regression analysis finding by removing outliers and
rerunning the regression analysis.
Answer: True
62) An outlier refers to Multiple Rs that are above expected norms such as above 95 or 100.
Answer: False
63) In multiple regression analysis, we are trying to predict an independent variable using
more than two dependent variables.
Answer: False
64) Multiple regression requires specification of a general conceptual model that identifies
independent and dependent variables and shows their expected relationships.
Answer: True
65) A regression plane is the shape of the independent variable in multiple regression
analysis.
Answer: False
66) The multiple R, also called the coefficient of determination, in multiple regression ranges
from 0 to +1.00 and represents the amount of the dependent variable "explained" by the
combined independent variables.
Answer: True
67) Multicollinearity refers to correlations among the dependent variables and makes
predictions much more accurate because predicting one variable also allows you to predict
the correlated variable(s).
Answer: False
68) The VIF is useful for identifying multicollinearity.
Answer: True
69) VIF is an acronym for "Very InFrequent."
Answer: False
70) The SPSS command for running multiple regression is: ANALYZE; REGRESSION;
LINEAR.
Answer: True
71) In multiple regression analysis, t tests are used to test for the statistical significance of
betas. If a beta is insignificant, it means that its respective independent variable plays no
meaningful role in predicting the dependent variable, and the independent variable should be
"trimmed" from the model.
Answer: True
72) When you have independent variables that are not significant in multiple regression
analysis, it is appropriate to take them out and rerun the regression. The new model is
referred to as a "trimmed" model.
Answer: True
73) In multiple regression we make a prediction, but we cannot put confidence intervals
around our prediction as we can in bivariate regression.
Answer: False
74) In dummy coding, the 0-versus-1 code is traditional, but any two adjacent numbers could
be used, such as 1 versus 2.
Answer: False
75) We must use standardized beta weights to compare the size of beta weights in multiple
regression because the independent variables they represent are often measured with different
units.
Answer: True
76) Multiple regression may be used as a screening device in the sense that it may be used to
reduce large numbers of potential independent variables in order to spot those that are most
salient for the dependent variable.
Answer: True
77) There is a type of multiple regression, called stepwise multiple regression, that does the
trimming operation automatically.
Answer: True
78) If we wanted to use a type of regression that first enters the variable that explains the
most variance, then the variable that explains the second highest level of variance and so on,
we would use ordinal regression.
Answer: False
79) Stepwise multiple regression is useful if a researcher has many dependent variables but
needs additional dependent variables in order to obtain a good predictive model.
Answer: False
80) Once we establish, through multiple regression analysis, that certain independent
variables are statistically significant in predicting a dependent variable, we may assume this
relationship to be one of cause and effect.
Answer: False
81) When a researcher uses gender as a dummy variable in a study for a client, it is important
not to distort the findings by highlighting this fact in the final report and presentation.
Answer: False
82) When regression is used as a screening device, the items to report are (1) dependent
variable, (2) statistically significant independent variables, (3) signs of beta coefficients, and
(4) standardized beta coefficients for the significant variables.
Answer: True
83) A weather forecaster studies relationships among phenomena such as wind direction,
barometric pressure, humidity, jet stream flow, and temperature. Based upon his or her
knowledge of the relationships between these variables and the weather, the forecaster
predicts there is an 80 percent chance of rain tomorrow. Which method of prediction have
you used?
A) guessing
B) statistics
C) extrapolation
D) predictive model
E) forecasting
Answer: D
84) If the intercept is found to be 2 and the slope is found to be 5 in a regression result
formula, then:
A) y = 2 + 3x.
B) y = 2 + 5x.
C) x = 2 + 5y.
D) x = 2 + 3y.
E) y = a + 2(5).
Answer: B
85) In a straight-line formula, the intercept is 4, the slope is 2, and the independent variable is
6. The predicted variable's level is:
A) 12.
B) 14.
C) 16.
D) 18.
E) 26.
Answer: C
86) You run bivariate regression analysis and you find that the ANOVA results indicate that
you have Sig. value for your F of .05. Now, looking under your Coefficients output, you have
an intercept value and a slope value. You should use these values only when:
A) they are both significant, i.e., have Sig. values above 1.00.
B) they are both significant, i.e., have Sig. values below 1.00.
C) they are both significant, i.e., have Sig. values equal to or below .05.
D) at least one is significant, i.e., has a Sig. value above .80.
E) None of the above; because the F value in the ANOVA was not significant, you should not
have even looked at the Coefficients output.
Answer: C
87) Assume you are developing a general conceptual model for the Advanced Automotive
Concepts dataset. Which of the following variables would most likely be your dependent
variable?
A) probability of purchasing a particular auto model
B) customer income
C) customer gender
D) customer education
E) customer occupation
Answer: A
88) The National Football League office discovered data covering attendance at professional
football games in the late 1940s and early 1950s. The game with the highest attendance was
between the St. Louis Cardinals and the New York Giants. The office also found considerable
information that someone had collected on each game day, such as the level of GDP, the
DOW, numbers of persons employed, number of new businesses formed during the week
preceding the game, and the population. A student intern took the information and built a
regression model to predict game attendance for the upcoming season. The model:
A) should accurately predict game attendance.
B) should not predict game attendance accurately because the variable levels of today (i.e.,
population, DOW, etc.) are out of range of those used to build the regression model.
C) should predict game attendance accurately because the variable levels of today (i.e.,
population, DOW, etc.) are out of range of those used to build the regression model.
D) should predict game attendance accurately because the variable levels (i.e., population,
DOW, etc.) are within range of those used to build the regression model.
E) does not have enough information.
Answer: B
Test Bank for Marketing Research
Alvin C. Burns, Ronald F. Bush
9780133074673, 9780134895406, 9780134167404
