Chapter 5 Multiple Choice Quiz

. The identification problem refers to the difficulties that a researcher encounters when trying to

A. determine which independent variables influence quantity demanded.

B. find accurate data on the price of a commodity and on the quantity demanded of a commodity.

C. estimate a demand function from data on commodity price and quantity demanded.

D. measure the impact of extraneous variables on experimental market data.

. The estimation of consumer demand by questioning a sample of consumers is referred to as the

A. consumer survey approach.

B. observational research approach.

C. consumer clinic approach.

D. market experiment approach.

. The estimation of consumer demand by setting up simulated stores, providing a sample of consumers with money, and then allowing them to purchase and keep the commodities they select in the stores is called the

A. consumer survey approach.

B. observational research approach.

C. consumer clinic approach.

D. market experiment approach.

. The estimation of consumer demand by monitoring actual purchasing and consumption behavior by a sample of consumers is called the

A. consumer survey approach.

B. observational research approach.

C. consumer clinic approach.

D. market experiment approach.

. If the t ratio for the slope of a simple linear regression equation is -2.48 and the critical values of the t distribution at the 1% and 5% levels, respectively, are 3.499 and 2.365, then the slope is

A. not significantly different from zero.

B. significantly different from zero at both the 1% and the 5% levels.

C. significantly different from zero at the 1% level but not at the 5% level.

D. significantly different from zero at the 5% level but not at the 1% level.

. Ordinary least squares is used to estimate a linear relationship between a firm's quantity sold per month and its total promotional expenditures and the slope of the linear function is found to be positive and significantly different from zero. Assuming that all other variables, including product price, were constant during the period covered by the data set, this result implies that

A. the firm should spend more on promotional expenditures.

B. the firm should spend less on promotional expenditures.

C. promotional expenditures influence demand.

D. promotional expenditures have no influence on demand.

. Ordinary least squares is used to estimate a linear relationship between a firm's total revenue per week (in $1,000s) and the average percentage discount from list price allowed to customers by salespersons. A 95% confidence interval on the slope is calculated from the regression output. The interval ranges from 1.05 to 2.38. Based on this result, the researcher

A. can conclude that the slope is significantly different from zero at the 5% level of significance.

B. can be 95% confident that the effect of a 1% increase in the average price discount will increase weekly total revenue by between $1,050 and $2,380.

C. has one chance in twenty of incorrectly concluding that the slope is within the estimated confidence interval.

D. All of the above are correct.

. The coefficient of determination

A. is maximized by ordinary least squares.

B. has a value between zero and one.

C. will generally increase if additional independent variables are added to a regression analysis.

D. All of the above are correct.

. The coefficient of correlation is

A. a measure of the strength and direction of the linear relationship between two variables.

B. equal to the size of the change in the Y variable that is caused by a change in the X variable.

C. is equal to the proportion of the variation in the Y variable that is due to variations in the X variable.

D. All of the above are correct.

. Multiple regression analysis is used when

A. there is not enough data to carry out simple linear regression analysis.

B. the dependent variable depends on more than one independent variable.

C. one or more of the assumptions of simple linear regression are not correct.

D. the relationship between the dependent variable and the independent variables cannot be described by a linear function.

. The adjusted value of the coefficient of determination

A. will always increase if additional independent variables are added to the regression model.

B. is equal to the proportion of the sum of the squared deviations of the dependent variable from its mean that is explained by the regression model.

C. is always greater than the proportion of the sum of the squared deviations of the dependent variable from its mean that is explained by the regression model.

D. is always less than the proportion of the sum of the squared deviations of the dependent variable from its mean that is explained by the regression model.

. If the F test statistic for a regression is greater than the critical value from the F distribution, it implies that

A. none of the independent variables in the regression model have a significant effect on the dependent variable.

B. all of the independent variables in the regression model have significant effects on the dependent variable.

C. one or more of the independent variables in the regression model have a significant effect on the dependent variable.

D. None of the above is correct.

. The standard error of the regression measures the

A. variability of the independent variable(s) relative to its (their) mean.

B. variability of the dependent variable relative to its mean.

C. variability of the dependent variable relative to the regression line.

D. average error that will result if the regression line is used to predict.

. Multicollinearity refers to a situation in which

A. successive error terms derived from the application of regression analysis to time series data are correlated.

B. there is a high degree of correlation between the independent variables included in a multiple regression model.

C. the dependent variable is highly correlated with the independent variable(s) in a regression analysis.

D. the application of a multiple regression model yields estimates that are nonlinear in form.

. Autocorrelation refers to a situation in which

A. successive error terms derived from the application of regression analysis to time series data are correlated.

B. there is a high degree of correlation between two or more of the independent variables included in a multiple regression model.

C. the dependent variable is highly correlated with the independent variable(s) in a regression analysis.

D. the application of a multiple regression model yields estimates that are nonlinear in form.

. Heteroskedasticity refers to a situation in which the error terms from a regression analysis

A. do not have equal variance.

B. are not normally distributed.

C. do not have a mean of zero.

D. All of the above are correct.

. The Durbin-Watson statistic is used to test for

A. multicollinearity.

B. autocorrelation.

C. heteroskedasticity.

D. All of the above are correct.

. Autocorrelation may be the result of

A. the omission of an important explanatory variable.

B. the presence of a trend in the independent variable.

C. nonlinearities in the relationship between the dependent and independent variables.

D. All of the above are correct.

. One advantage of estimating a function in which all variables have been transformed into their natural logarithms is that

A. problems with multicollinearity will be eliminated.

B. problems with heteroskedasticity will be eliminated.

C. the estimated slope coefficients are all elasticities.

D. None of the above is correct.

. One difference between foreign and domestic demand for a commodity exported by the U.S. is that

A. foreign demand is unrelated to the dollar price of the commodity.

B. foreign demand depends on the exchange rate between domestic and foreign currencies.

C. the domestic price elasticity of demand depends on the availability of substitute commodities.

D. foreign-made commodities are not good substitutes for U.S. made commodities.

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