Deck 19: Regression Analysis in Marketing Research

Question 1

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 1

C

Question 2

Which of the following residuals shows an exact prediction?

A) 0
B) +1.0
C) -25
D) +25
E) 100.0

Answer 2

A

Question 3

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 3

D

Question 4

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

Question 5

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.

Question 6

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

Question 7

When you compare how far the predicted values are from the actual or observed values, you are performing:

A) analysis of prediction
B) control
C) analysis of residuals
D) analysis of variance
E) analysis of values

Question 8

A good way to assess a predictive model's predictive accuracy is through:

A) measuring its predictive accuracy
B) measuring its analysis of residuals
C) measuring its analysis of variance
D) measuring its reliability
E) measuring its consistency

Question 9

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

Question 10

What are two ways of making a prediction?

A) guessing and using statistics
B) extrapolation and guessing
C) building a predictive model and guessing
D) extrapolation and building a predictive model
E) guessing and hypothesizing

Question 11

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

Question 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

Question 13

Extrapolation is a process of making predictions by:

A) using surveys as a means of predicting the future
B) using external factors as a means of predicting the future
C) using past experience as a means of predicting the future
D) identifying correlations
E) taking the mean of several possible scenarios

Question 14

In bivariate regression analysis, the higher the Adjusted R Square value, the:

A) 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) closer to 0 it will be
E) None of the above; there is no Adjusted R Square value in regression analysis.

Question 15

Whose paper entitled "Regression toward mediocrity in hereditary stature" began the work that gave us linear regression?

A) Sir Francis Galton
B) Sir Isaac Newton
C) Adam Smith
D) John Law
E) Edward Bernays

Question 16

The "goodness" of predictions refers to:

A) accuracy
B) reliability
C) the way the prediction is written
D) identification
E) consistency

Question 17

You might be using extrapolation as an approach for stating that:

A) since your professor's exam was easy today, the last one must have been easy too
B) you will have to study more for your exams
C) your professor's exam will have to be more difficult than the last two easy ones
D) since your professor's last two exams were easy, the next should be easy as well
E) your professor will not give another exam this semester

Question 18

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

Question 19

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

Question 20

A predictive model is defined as 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

Question 21

That a scatter diagram plot will be spread uniformly and in accord with the normal curve assumptions over the regression line is:

A) one of the assumptions of regression analysis
B) one of the assumptions of time series analysis
C) the only assumption of exponential smoothing
D) the only assumption of scatter diagram plots
E) an understood fact

Question 22

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 non-linear beta weight for each independent variable
E) a significant R for each independent variable

Question 23

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

Question 24

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.

Question 25

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

Question 26

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

Question 27

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

Question 28

A measure of the accuracy of the predictions of the regression equation is referred to as:

A) standard error of the mean
B) standard deviation
C) residuals deviation
D) standard error of the estimate
E) regression deviation

Question 29

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

Question 30

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

Question 31

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

Question 32

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

Question 33

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

Question 34

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

Question 35

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

Question 36

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) check for outliers
E) rerun the regression

Question 37

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

Question 38

When using regression analysis, confidence intervals may be used to:

A) determine the x and y variables
B) allow the researcher to use the knowledge of the normal curve to specify the range in which the dependent variable may fall
C) allow the researcher to use the knowledge of the normal curve to specify the range in which the independent variable may fall
D) determine the slope
E) satisfy assumptions

Question 39

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

Question 40

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

Question 41

The two ways of making a prediction are extension analysis and astrological modeling.

Question 42

In using extrapolation, the forecaster goes beyond what happened "yesterday" and identifies relationships between a number of variables such as the relationship between winds and barometric pressure.

Question 43

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

Question 44

All predictions should be judged for their "goodness."

Question 45

A predictive model simply examines what has happened in the past and predicts the future.

Question 46

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.

Question 47

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

Question 48

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

Question 49

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 you should not apply regression to predict data outside the boundaries of the data used to develop the regression model.
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.

Question 50

A prediction is a statement of what is believed will happen in the future made on the basis of past experience or prior observation.

Question 51

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.

Question 52

In regression the variable being predicted, b, is known as the dependent variable.

Question 53

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.

Question 54

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

Question 55

In the following straight line formula, y = a + bx, the variable being predicted is the beta weight, b.

Question 56

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

Question 57

The "goodness" of a prediction means its reliability.

Question 58

In regression the variable being predicted, y, is known as the dependent variable.

Question 59

In regression the variable used to predict the dependent variable is known as x, the independent variable.

Question 60

In the formula for bivariate regression analysis, the change in y for each one-unit change in x is known as the slope.

Question 61

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."

Question 62

Multiple regression requires specification of a general conceptual model that identifies independent and dependent variables and shows their expected relationships.

Question 63

The VIF is useful for identifying multicollinearity.

Question 64

A regression line using the "least squares criterion" will result in high residuals.

Question 65

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).

Question 66

If the ANOVA F test is not significant in bivariate regression analysis, we must trim the model by eliminating the insignificant dependent variable(s).

Question 67

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.

Question 68

The standard error of the estimate is used to help predict a range within which we would expect an actual result to fall.

Question 69

One of the assumptions of regression analysis is that the plots on a scatter diagram will be spread uniformly and in accord with the normal curve assumptions over the regression line. This means that the points will be diffused and spread out near the line and become closer together as you move away from the line.

Question 70

The standard error of the estimate is used as a measure of the accuracy of the predictions in regression; it is analogous to the standard error of the mean used in estimating a population mean from a sample.

Question 71

We can sometimes improve a regression analysis finding by removing outliers and rerunning the regression analysis.

Question 72

In multiple regression analysis, we are trying to predict an independent variable using more than two dependent variables.

Question 73

In bivariate regression analysis, t tests are used to test the significance of the slope and the intercept of the multiple dependent variables.

Question 74

A regression plane is the shape of the independent variable in multiple regression analysis.

Question 75

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.

Question 76

VIF is an acronym for "Very InFrequent."

Question 77

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.

Question 78

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.

Question 79

Immediately in bivariate analysis the researcher must find out whether or not a linear relationship
exists in the population.

Question 80

An outlier refers to Multiple Rs that are above expected norms such as above 95 or 100.