Deck 5: Demand Estimation

Question 1

The identification problem would not prevent estimation of a demand curve from price and quantity data if, over the time period sampled, the only thing that varied was the

A) technology of production.
B) level of consumer income.
C) price(s) of substitutes and complements.
D) level of advertising expenditures.

Answer 1

technology of production.

Question 2

The identification problem would prevent estimation of a demand curve from price and quantity data if, over the time period sampled, the only thing that varied was the

A) technology of production.
B) price(s) of raw materials.
C) level of consumer income.
D) rental cost of capital.

Answer 2

level of consumer income.

Question 3

If the t ratio for the slope of a simple linear regression equation is equal to 3.614 and the critical values of the t distribution at the 1 percent and 5 percent levels of significance, 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 percent and the 5 percent levels.
C) significantly different from zero at the 1 percent level but not at the 5 percent level.
D) significantly different from zero at the 5 percent level but not at the 1 percent level.

Answer 3

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

Question 4

Application of simple linear regression analysis to the estimation of a demand equation has yielded the following:
Q = 24 - 2P
If the current product price is P = $6 and the quantity sold per time period is Q = 10, then the error (e) for the current time period is equal to

A) 1.
B) -1.
C) 2.
D) -2.

Question 5

If the t ratio for the slope of a simple linear regression equation is equal to 1.614 and the critical values of the t distribution at the 1 percent and 5 percent levels of significance, 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 percent and the 5 percent levels.
C) significantly different from zero at the 1 percent level but not at the 5 percent level.
D) significantly different from zero at the 5 percent level but not at the 1 percent level.

Question 6

A multiple regression analysis based on a data set that consists of 30 observations yielded the following estimated equation:
Q = 120 - 1.1P + 3.7I + 0.90A
Where P is price, I is income, and A is advertising. If the coefficient of determination is 0.80, then the adjusted coefficient of determination is

A) 0.84.
B) 0.78.
C) 0.62.
D) 1.04.

Question 7

A multiple regression analysis based on a data set that consists of 30 observations yielded the following estimated equation:
Q = 120 - 1.1P + 3.7I + 0.09A
Where P is price, I is income, and A is advertising. If the coefficient of determination is 0.80, then the F statistic is

A) 34.67.
B) 29.35.
C) 21.23.
D) 4.67.

Question 8

A simple linear regression analysis based on a data set that consists of 20 observations yielded the following estimated equation:
Q = 120 - 3.6P
If the coefficient of determination is 0.81, then the correlation coefficient is equal to

A) 0.81.
B) -0.81.
C) -0.90.
D) 0.90.

Question 9

A multiple regression analysis based on a data set that consists of 30 observations yielded the following estimated demand equation:
Q = 120 - 1.1P + 0.04I + 0.90A
Where P is price, I is income, and A is advertising. If price is equal to $1,000, income is equal to $20,000, and advertising expenditures are equal to $5,000, then the predicted quantity demanded (Q) is

A) 6,520.
B) 4,320.
C) 8,015.
D) None of these answers is correct.

Question 10

A multiple regression analysis based on a data set that consists of 500 observations yielded the following estimated demand equation:
Q = 120 - 1.1P + 0.04I + 0.90A - 0.04P_Z
Where P is price, I is income, A is advertising, and P_Z is the price of a related good. If the standard errors of the independent variables are 0.25, 0.5, 0.3, and 0.01, respectively, which of the four variables should be dropped from the equation?

A) P
B) I
C) A
D) P_Z

Question 11

The F statistic calculated from a multiple regression analysis is equal to 1.96. If the critical values of the F distribution are 2.42 and 3.47 at the 5 percent and 1 percent levels of significance, respectively, then

A) at least one of the slope coefficients is significantly different from zero.
B) none of the slope coefficients is significantly different from zero.
C) all of the slope coefficients are significantly different from zero.
D) no more than 5 percent (1 out of 20) of the slope coefficients are different from zero.

Question 12

The F statistic calculated from a multiple regression analysis is equal to 5.96. If the critical values of the F distribution are 2.42 and 3.47 at the 5 percent and 1 percent levels of significance, respectively, then

A) at least one of the slope coefficients is significantly different from zero.
B) none of the slope coefficients is significantly different from zero.
C) all of the slope coefficients are significantly different from zero.
D) no more than 5 percent (1 out of 20) of the slope coefficients are different from zero.

Question 13

The application of multiple regression analysis to a time-series data set yields a calculated Durbin-Watson statistic that is equal to 2.00. From this result, it is clear that

A) multicollinearity is present.
B) multicollinearity is absent.
C) autocorrelation is present.
D) autocorrelation is absent.

Question 14

The application of multiple regression analysis to a time-series data set yields a calculated Durbin-Watson statistic that is equal to 1.00. If the lower test value at the 1 percent level is 1.28 and the upper value is 1.51, then

A) there is no evidence of a problem.
B) there is some evidence of a problem, but it is not significant.
C) there is evidence of a significant problem.
D) there is not sufficient information to determine whether or not there is a problem.

Question 15

The application of multiple regression analysis to a time-series data set yields a calculated Durbin-Watson statistic that is equal to 1.00. If the lower test value at the 1 percent level is 1.28 and the upper value is 1.51, then there is evidence that

A) multicollinearity is present.
B) multicollinearity is absent.
C) autocorrelation is present.
D) autocorrelation is absent.

Question 16

The application of multiple regression analysis to a time-series data set yields a calculated Durbin-Watson statistic that is equal to 1.49. If the lower test value at the 1 percent level is 1.28 and the upper value is 1.51, then

A) there is no evidence of a problem.
B) there is some evidence of a problem, but it is not significant.
C) there is evidence of a significant problem.
D) there is not sufficient information given to determine whether or not there is a problem.

Question 17

The application of multiple regression analysis to a data set yields an F statistic that is highly significant and t ratios that are not significant. This is an indication that

A) autocorrelation is present.
B) multicollinearity is present.
C) homoscedasticity is present.
D) heteroscedasticity is present.

Question 18

Autocorrelation can be the result of

A) the omission of an important explanatory variable.
B) using cross-sectional data.
C) multicollinearity.
D) All of the above are correct.

Question 19

The scatter diagram represents a data set and a plot of the simple linear regression equation estimated from the data. The diagram shows evidence of

A) homoscedasticity.
B) heteroscedasticity.
C) positive autocorrelation.
D) negative autocorrelation.

Question 20

The scatter diagram represents the error terms from a simple linear regression estimated from time-series data. The error terms are plotted against time. The diagram shows evidence of

A) homoscedasticity.
B) heteroscedasticity.
C) positive autocorrelation.
D) negative autocorrelation.

Question 21

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.

Question 22

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.

Question 23

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.

Question 24

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.

Question 25

If the t ratio for the slope of a simple coefficient of a simple linear regression equation is -2.48 and the critical values of the t distribution at the 1 percent and 5 percent 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 percent and the 5 percent levels.
C) significantly different from zero at the 1 percent level but not at the 5 percent level.
D) significantly different from zero at the 5 percent level but not at the 1 percent level.

Question 26

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.

Question 27

Ordinary least squares is used to estimate a linear relationship between a firm's total revenue per week (in thousands of dollars) and the average percentage discount from list price allowed to customers by salespeople. A 95 percent 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 percent level of significance.
B) can be 95 percent confident that the effect of a 1 percent increase in the average price discount will increase weekly total revenue by between $1,050 and $2,380.
C) has a 1-in-20 chance of incorrectly concluding that the slope is within the estimated confidence interval.
D) All of the above are correct.

Question 28

The coefficient of determination

A) is maximized by ordinary least squares.
B) has a value between zero and 1.
C) will generally increase if additional independent variables are added to a regression analysis.
D) All of the above are correct.

Question 29

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.

Question 30

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.

Question 31

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.

Question 32

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 has 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.

Question 33

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.

Question 34

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.

Question 35

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.

Question 36

Heteroscedasticity 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.

Question 37

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.

Question 38

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 heteroscedasticity will be eliminated.
C) the estimated slope coefficients are all elasticities.
D) None of the above is correct.

Question 39

One difference between foreign and domestic demand for a commodity exported by the United States 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.

Question 40

Consider the following scenario: A real estate firm decides to estimate the demand for housing as a function of the location's population density and presence of shopping centers (among other things). However, the company also observes that there is a statistically significant positive correlation between population density and the presence of shopping centers. Would you recommend including both of these variables into the regression estimation? If your recommendation is no, why?

A) Yes, as both of these variables seem logical and interpreting their coefficients is important
B) No, because this would lead to multicollinearity
C) No, because this would lead to heteroscedasticity
D) No, because this would lead to autocorrelation

Question 41

Assume that the following is the result of a demand estimation:
,
$$\ln Q = \ln 20 + 5 \ln P + 2 \ln I$$ Where I represents consumer income, P is the price, and Q is quantity demanded. What is the price elasticity of demand?

A) 20
B) 5
C) 2
D) None of the above

Question 42

Assume that the following is the result of a demand estimation:
$$Q _ { X } = 1000 - 20 P _ { X } + 4 \text { mame } + 10 P _ { Y } - 15 P _ { Z }$$ Assuming that all coefficients are statistically significant and the model does not suffer from any estimation issues, which of the following statements is wrong:

A) X is a normal good
B) X and Y are competing products
C) X and Z are competing products
D) A rise in the price of X reduces the quantity of X demanded

Question 43

Which of the following is a marketing research approach to demand estimation?

A) Consumer surveys
B) Observational research
C) Market Experiments
D) All of the above

Question 44

Using consumer survey approach, data is obtained from

A) questioning a sample of consumers.
B) laboratory experiments.
C) observing consumers purchasing and consuming products.
D) real market tests in actual marketplace.

Question 45

Using observational research approach, data is obtained from

A) questioning a sample of consumers.
B) laboratory experiments.
C) observing consumers purchasing and consuming products.
D) real market tests in actual marketplace.

Question 46

Using consumer clinics approach, data is obtained from

A) questioning a sample of consumers.
B) laboratory experiments.
C) observing consumers purchasing and consuming products.
D) real market tests in actual marketplace.

Question 47

Using market experiment approach, data is obtained from

A) questioning a sample of consumers.
B) laboratory experiments.
C) observing consumers purchasing and consuming products.
D) real market tests in actual marketplace.

Question 48

Regression line can be best described as

A) the line that goes through all the data points.
B) line obtained by minimizing the sum of the distance between the horizontal axis and the data points.
C) line obtained by minimizing the sum of squared vertical deviations of each point from the regression line.
D) line obtained by minimizing the sum of squared horizontal deviations of each point from the regression line.

Question 49

OLS (ordinary least squares) is used to estimate a linear relationship between a firm's quantity of laptops sold per year and its total promotional expenditures, and the slope of the linear function is found to be positive but not 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 due to positive slope.
B) the firm should spend less on promotional expenditures due to positive slope.
C) promotional expenditures influence demand.
D) more research is needed, or promotional expenditures have no influence on demand.

Question 50

Application of simple linear regression analysis to the estimation of a demand equation has yielded the following:
Q = 44 - 11P
If the current product price is P = $1 and the quantity sold per time period is Q = 30, then the error (e) for the current time period is equal to

A) 3
B) 11
C) 29
D) 33

Question 51

$$\ln Q _ { t } = a + b \ln P _ { t } + c \ln G D P _ { t }$$
One of the advantages of using the following function in estimation is

A) problems with multicollinearity will be eliminated.
B) problems with heteroscedasticity will be eliminated.
C) the estimated slope coefficients are all elasticities.
D) None of the above is correct.

Question 52

Which of the following countries is the top exporter in dollar value of exports in the US in 2015?

A) Harley-Davidson
B) United Technologies
C) JP Morgan Chase
D) Apple

Question 53

Which of the following companies did NOT experience a decline in a smartphone market since the 2005?

A) Blackberry
B) Huawei
C) Nokia
D) Motorola

Question 54

Behavioral economics extends the understanding of economic behavior by considering which of the following factors?

A) Cognitive
B) Emotional
C) Rational
D) Physiological

Question 55

Which of the following is NOT an advantage of the virtual shopping against the simulated physical store in marketing research?

A) It is cheaper to conduct virtual shopping
B) More people can participate in a shorter period of time
C) It is easier to make changes and repeat simulations
D) The results are more accurate

Question 56

Assume that a firm wants to use multiple regression analysis on Excel to estimate how the demand for its product X depends on price of its product, consumer's income (i)and a price of a substitute Y. Which of the following explicit linear form should the firm use?

A) $Q _ { X } = a P _ { X } + b \left( I + P _ { Y } \right) + e$
B) $Q _ { X } = a P _ { x } + b _ { 1 } I + b _ { 2 } P _ { Y }$
C) $Q _ { X } = a + b _ { 1 } P _ { X } + b _ { 2 } I + b _ { 3 } P _ { Y } + e$
D) $Q _ { X } = a + b _ { 1 } P _ { X } + b _ { 2 } I + b _ { 3 } Q _ { Y } P _ { Y } + e$

Question 57

A simple linear regression analysis based on a data set that consists of 3,000 observations yielded the following estimated equation:
Q = 100 -1.25P
If the coefficient of determination is 0.64, then the correlation coefficient is equal to

A) 0.64
B) 0.72
C) 0.80
D) 0.88

Question 58

A multiple regression analysis based on a data set that consists of 505 observations yielded the following estimated demand equation:
Q = 50 - 2P + 0.01I + 0.01A
Where P is price, I is income, and A is advertising. If price is equal to $20, income is equal to $2,000, and advertising expenditures are equal to $5,000, then the predicted quantity demanded (Q) is

A) 20.
B) 40.
C) 60.
D) 80.

Question 59

If one finds estimate of b to be equal with standard error , using a critical value of 1.96, what is the confidence interval for the true b coefficient?

A) (0.52, 2.48)
B) (-0.98, 0.98)
C) (-0.46, 3.46)
D) (1.00, 2,00)

Question 60

Cross-sectional data are made up of observations that are collected across a period of time.

Question 61

The demand curve for a commodity can generally be approximated by drawing a graph with price on the horizontal axis and quantity on the vertical axis, plotting a series of points that represent observed combinations of price and quantity, and then drawing lines that connect the points.

Question 62

If the price of a commodity rises and the quantity sold increases, it does not prove that the demand curve for the commodity slopes upward.

Question 63

If the supply curve for a commodity shifts while the demand curve does not shift, then the demand identification problem will not be encountered.

Question 64

The identification problem is dealt with in practice by including all of the determinants of demand in the estimated demand function.

Question 65

Observational research involves questioning a sample of consumers about their responses to actual and potential market conditions.

Question 66

One advantage of consumer clinics over market experiments is the ability to control the environment and screen out the effects of external events.

Question 67

A market experiment is carried out by providing consumers with a sum of money that must be spent in a simulated store.

Question 68

The use of electronic devices designed to gather information about which television stations people are watching is an example of observational research.

Question 69

A scatter diagram is a graph of a linear function.

Question 70

In the linear function Y = a + bX, Y is the intercept and X is the slope of the function.

Question 71

The slope of a linear function is equal to the change in the dependent variable divided by the corresponding change in the independent variable.

Question 72

The Y intercept of a linear function is equal to the value of X when Y is equal to zero.

Question 73

If a 1-unit increase in the value of X results in a 2-unit decrease in the value of Y, then b = -2.

Question 74

If a linear function that is plotted on a graph passes through the origin of a graph, then b = 0.

Question 75

If a regression line that was calculated by ordinary least squares is plotted on a scatter diagram, all of the points in the data set will be on the line.

Question 76

A regression line that is calculated by ordinary least squares will have an intercept and slope that minimize the sum of the squared differences between the observed value of the Y variable and the regression line.

Question 77

Unexplained variation in the Y variable is denoted e_t.

Question 78

If ordinary least squares is used to estimate a linear function, then the sum of the e_t will always be equal to zero.

Question 79

One of the crucial assumptions of regression analysis is that the error term has a normal probability distribution.

Question 80

A significance test on the slope coefficient using the t ratio tests the hypothesis that the slope is equal to zero.