Deck 15: Understanding Regression Analysis Basics

Question 1

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 1

A

Question 2

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 2

C

Question 3

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 3

B

Question 4

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 5

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 6

Which of the following residuals shows an exact prediction?

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

Question 7

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 8

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 9

________ 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

Question 10

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

Question 11

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 12

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 13

________ 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

Question 14

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.

Question 15

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 16

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.

Question 17

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.

Question 18

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.

Question 19

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.

Question 20

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 21

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.

Question 22

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 23

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 24

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 25

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 26

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 27

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 28

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 29

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 30

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 31

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 32

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 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

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 35

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 36

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 37

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

Question 38

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 39

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 40

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 41

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

Question 42

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 43

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

Question 44

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

Question 45

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

Question 46

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 47

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 48

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 49

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

Question 50

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 51

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 52

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 53

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

Question 54

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

Question 55

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

Question 56

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.

Question 57

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 58

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

Question 59

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

Question 60

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 61

The VIF is useful for identifying multicollinearity.

Question 62

In dummy coding,the 0-versus-1 code is traditional,but any two adjacent numbers could be used,such as 1 versus 2.

Question 63

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

Question 64

In multiple regression we make a prediction,but we cannot put confidence intervals around our prediction as we can in bivariate regression.

Question 65

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.

Question 66

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.

Question 67

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 68

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.

Question 69

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 70

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

Question 71

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.

Question 72

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.

Question 73

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

Question 74

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.

Question 75

The SPSS command for running multiple regression is: ANALYZE;REGRESSION;LINEAR.

Question 76

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

Question 77

VIF is an acronym for "Very InFrequent."

Question 78

There is a type of multiple regression,called stepwise multiple regression,that does the trimming operation automatically.

Question 79

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

Question 80

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.