Question 1

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).

Accepted Answer

These are commonly used dummy variables in research, marketing, and statistics.

Question 2

Researchers must keep in mind that it is not necessary to run trimmed regressions iteratively until all betas are significant.

Accepted Answer

The answer of Researchers must keep in mind that it...

Question 3

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.

Accepted Answer

It is necessary to test for statistical significance to determine if the estimated population multiple regression equation is significant or not.

Question 4

Used as a screening device,multiple regression analysis identifies dependent variables that qualify to be part of the final equation.

Accepted Answer

Multiple regression analysis is used to understand the relationship between one dependent variable and two or more independent variables, not to identify dependent variables for the equation.

Question 5

It is possible to inspect the strength of the linear relationship between the independent variables and the dependent variable with multiple regression.

Accepted Answer

Multiple regression allows us to inspect the strength of the linear relationship between the dependent variable and multiple independent variables simultaneously. It does this by calculating the coefficient of determination (R-squared), which represents the percentage of variation in the dependent variable that can be explained by the independent variables. The R-squared value ranges from 0 to 1, with higher values indicating a stronger linear relationship. Therefore, we can use multiple regression to inspect the strength of the linear relationship between the independent variables and the dependent variable.

Question 6

With multiple regression,it is not necessary to look at the significance level of each calculated beta.

Accepted Answer

In multiple regression, it is important to examine the significance level (p-value) of each calculated beta coefficient to determine whether a particular predictor variable is significantly related to the outcome variable, holding all other predictors constant. Beta coefficients with high significance levels (low p-values) indicate that the predictor variable is contributing significantly to the model, while beta coefficients with low significance levels (high p-values) suggest that the variable may not be important or may be redundant with other predictors. Therefore, it is necessary to look at the significance level of each calculated beta in multiple regression.

Question 7

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.

Accepted Answer

This statement is true. Stepwise regression is a statistical method used to identify a subset of independent variables that are correlated with a dependent variable. It involves sequentially adding or removing variables based on their statistical significance. This can help a researcher to identify the most important variables for predicting the outcome of interest, and to avoid overfitting the model by including too many variables.

Question 8

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.

Accepted Answer

Multicollinearity refers to the presence of moderate or stronger correlations among independent variables, not dependent variables, in a regression model.

Question 9

It is common practice to directly compare the beta coefficient for family size to another for money spent per month on personal grooming.

Accepted Answer

It is not appropriate to directly compare beta coefficients of different independent variables as they may have different scales and units of measurement. It is more appropriate to compare the standardized beta coefficients or to use effect sizes such as Cohen's d or partial eta squared.

Question 10

A trimmed regression means that the researcher eliminates the nonsignificant independent variables and rerun the regression.

Accepted Answer

This statement is true as a trimmed regression involves removing independent variables that are not statistically significant and running the regression again to see the relationship between the remaining independent variables and the dependent variable.

Question 11

Standardized betas indicate the relative importance of alternative predictor variables.

Accepted Answer

Standardized betas are regression coefficients that have been transformed to have a mean of zero and a standard deviation of one. This transformation allows researchers to compare the relative importance of different predictor variables in a multiple regression model. Therefore, the statement is true.

Question 12

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.

Accepted Answer

The answer of Generally,during the trimming process,successive iterations sometimes cause...

Question 13

The researcher can compare standardized beta coefficients' sizes directly,but comparing unstandardized betas is like comparing apples and oranges.

Accepted Answer

Standardized beta coefficients are standardized on a common scale, allowing for comparisons of their sizes. Unstandardized betas, on the other hand, are based on different units of measurement, making direct comparisons inappropriate. Therefore, comparing unstandardized betas is like comparing apples and oranges because they are not on the same scale.

Question 14

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.

Accepted Answer

Adding independent variables can make the model more complex, but it also allows for more realistic predictions by accounting for multiple factors that may influence the outcome.

Question 15

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.

Accepted Answer

VIF (Variance Inflation Factor) measures the extent to which the variance of the estimated regression coefficient is increased due to multicollinearity in the data. Hence, it is important to inspect the VIF number associated with each independent variable in the final multiple regression equation to assess the presence of multicollinearity. A high VIF value (greater than 5 or 10) indicates a possible issue with multicollinearity, which can affect the interpretation and reliability of the regression results.

Question 16

The greater the explanatory power of the multiple regression finding,the better and more useful it is for the researcher.

Accepted Answer

The greater the explanatory power of the multiple regression finding, the more accurately the model can explain the relationship between the independent and dependent variables. This makes it more useful for the researcher as it can provide more insight and information for further analysis and decision making.

Question 17

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.

Accepted Answer

Most concepts in multiple regression are analogous to those in the simple bivariate case, with terminology and statistics adjusted to accommodate the inclusion of more than one independent variable, but the foundational concepts remain similar.

Question 18

Many researchers avoid mentally converting the multiple R value into a percentage,but rather insist on using a decimal value.

Accepted Answer

Many researchers convert the multiple R value into a percentage to interpret the strength of the relationship more intuitively.

Question 19

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.

Accepted Answer

During the trimming process, the beta values and intercept value may shift slightly, which can affect the significance levels of the betas. Therefore, it is necessary to inspect all significance levels of the betas again to ensure their validity.

Question 20

There is no need to perform a great many bivariate regressions,as there is a much better tool called multiple regression analysis.

Accepted Answer

Multiple regression analysis allows for the analysis of the relationship between multiple independent variables and a single dependent variable, making it a more efficient and comprehensive tool compared to performing many individual bivariate regressions.

Quiz 15: Understanding Regression Analysis Basics