Question 1

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.

Accepted Answer

Standardized beta weights are used to compare the size of beta weights in multiple regression because the independent variables they represent are often measured with different units. The standardization process removes the influence of the scale of measurement and makes it possible to compare the magnitude of the effect of different predictors on the outcome variable.

Question 2

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."

Accepted Answer

This type of regression is called "stepwise regression."

Question 3

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.

Accepted Answer

When a variable is not significant in multiple regression analysis, it is appropriate to consider removing it from the model and rerunning the analysis. The resulting model, which includes only the significant variables, is often referred to as a trimmed model.

Question 4

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) when 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.

Accepted Answer

If the ANOVA results indicate a significant F-value at the alpha level of .05, then we reject the null hypothesis that the slope of the regression line is zero, which means that there is a linear relationship between the two variables. In this case, we look at the Coefficients output for further information. The intercept and slope values are considered significant when their Sig. values are equal to or below .05, which is the same level of significance as the F-value in the ANOVA. Therefore, the correct answer is C.

Question 5

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)

Accepted Answer

The regression result formula is: y = intercept + slope*x
Substituting the values, we get y = 2 + 5x, which is option B.

Question 6

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.

Accepted Answer

Just because there is a statistical relationship between certain independent variables and a dependent variable, it does not necessarily mean there is a cause and effect relationship. Other variables or factors could be influencing the relationship or there may be reverse causality at play. Additional research is needed to establish causality.

Question 7

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

Accepted Answer

In multiple regression, we can put confidence intervals around our prediction. These intervals are generally wider than those in bivariate regression due to the increased complexity of the model.

Question 8

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.

Accepted Answer

Multiple regression can be used to identify the most important independent variables that are related to the dependent variable, allowing researchers to focus on those variables in subsequent analyses. This can help reduce the number of variables that need to be considered, making it easier to interpret the results of the analysis.

Question 9

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

Accepted Answer

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

Question 10

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

Accepted Answer

Stepwise multiple regression is a type of regression that automatically selects the most significant independent variables to include in the model, effectively performing the trimming operation automatically.

Question 11

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.

Accepted Answer

The answer of Stepwise multiple regression is useful if a...

Question 12

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

Accepted Answer

The dependent variable is the variable being predicted or explained in a model. In this case, the model is focused on Advanced Automotive Concepts, so the most likely dependent variable would be the probability of purchasing a particular auto model. The other variables listed (customer income, gender, education, and occupation) are all potential independent variables that could be used in a model to predict the dependent variable.

Question 13

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

Accepted Answer

The answer of In dummy coding, the 0-versus-1 code is...

Question 14

The weather forecast has called for an 80 percent chance of rain for the last five days and it rained each day. On the sixth day, the forecast again is for an 80 percent chance of rain. Therefore, you forecast that it will rain today. Which method of prediction have you used?
A) guessing
B) statistics
C) extrapolation
D) predictive model
E) forecasting

Accepted Answer

Extrapolation involves predicting future events based on past trends or data, which is what is being done by assuming it will rain again based on the previous days' patterns.

Question 15

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

Accepted Answer

The model built using data from the late 1940s and early 1950s may not accurately predict attendance for the upcoming season due to the changes that may have occurred in population, economy, and other factors since that time. The variable levels of today may be out of range of those used to build the regression model, making it less accurate.

Question 16

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.

Accepted Answer

When regression is used as a screening device, the important items to report are the dependent variable, statistically significant independent variables, signs of beta coefficients, and standardized beta coefficients for the significant variables.

Question 17

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.

Accepted Answer

This statement is true. If a beta is insignificant, it means that its respective independent variable is not contributing significantly to the prediction of the dependent variable and should be removed from the model.

Question 18

If you have a good regression model, apply regression analysis to predict outside of the boundaries of the data used to develop your regression model.

Accepted Answer

Extrapolating predictions beyond the range of the data used to develop a regression model can lead to unreliable and inaccurate results, as the model may not capture the behavior of the data outside the observed range.

Question 19

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

Accepted Answer

The straight-line formula is y = mx + b, where y is the predicted variable, x is the independent variable, m is the slope, and b is the y-intercept. 
In this case, we are given b = 4, m = 2, and x = 6. 
Plugging in these values, we get y = 2(6) + 4 = 16. 
Therefore, the predicted variable's level is 16, option C.

Question 20

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

Accepted Answer

The forecaster is using a predictive model, which uses knowledge of the relationships between variables to make a prediction about future weather conditions. This is different from guessing, statistics, extrapolation, or simply forecasting based on past weather patterns.

Quiz 19: Regression Analysis in Marketing Research