Test Bank Chapter 4
Chapter 4—Estimating Demand
MULTIPLE CHOICE
1. Using a sample of 100 consumers, a double-log regression model was used to estimate demand for
gasoline. Standard errors of the coefficients appear in the parentheses below the coefficients.
Ln Q = 2.45 -0.67 Ln P + . 45 Ln Y – .34 Ln Pcars
(.20) (.10) (.25)
Where Q is gallons demanded, P is price per gallon, Y is disposable income, and Pcars is a price index
for cars. Based on this information, which is NOT correct?
a. Gasoline is inelastic.
b. Gasoline is a normal good.
c. Cars and gasoline appear to be mild complements.
d. The coefficient on the price of cars (Pcars) is insignificant.
e. All of the coefficients are insignificant.
2. In a cross section regression of 48 states, the following linear demand for per-capita cans of soda was
found: Cans = 159.17 – 102.56 Price + 1.00 Income + 3.94Temp
Coefficients
Standard
Error
t Stat
Intercept
159.17
94.16
1.69
Price
-102.56
33.25
-3.08
Income
1.00
1.77
0.57
Temperature
3.94
0.82
4.83
R-Sq = 54.1% R-Sq(adj) = 51.0%
From the linear regression results in the cans case above, we know that:
a. Price is insignificant
b. Income is significant
c. Temp is significant
d. As price rises for soda, people tend to drink less of it
e. All of the coefficients are significant
3. A study of expenditures on food in cities resulting in the following equation:
Log E = 0.693 Log Y + 0.224 Log N
where E is Food Expenditures; Y is total expenditures on goods and services; and N is the size of the
family. This evidence implies:
a. that as total expenditures on goods and services rises, food expenditures falls.
b. that a one-percent increase in family size increases food expenditures .693%.
c. that a one-percent increase in family size increases food expenditures .224%.
d. that a one-percent increase in total expenditures increases food expenditures 1%.
e. that as family size increases, food expenditures go down.
4. All of the following are reasons why an association relationship may not imply a causal relationship
except:
a.
b.
c.
d.
e.
5. In regression analysis, the existence of a significant pattern in successive values of the error term
constitutes:
a.
b.
c.
d.
e.
6. In regression analysis, the existence of a high degree of intercorrelation among some or all of the
explanatory variables in the regression equation constitutes:
a.
b.
c.
d.
e.
7. When using a multiplicative power function (Y = a X1b1 X2b2 X3b3) to represent an economic
relationship, estimates of the parameters (a, and the b’s) using linear regression analysis can be
obtained by first applying a ____ transformation to convert the function to a linear relationship.
a.
b.
c.
d.
e.
8. The correlation coefficient ranges in value between 0.0 and 1.0.
a.
b.
9. The coefficient of determination ranges in value between 0.0 and 1.0.
a.
b.
10. The coefficient of determination measures the proportion of the variation in the independent variable
that is “explained” by the regression line.
a.
b.
11. The presence of association between two variables does not necessarily imply causation for the
following reason(s):
a.
b.
c.
d.
e.
12. The estimated slope coefficient (b) of the regression equation (Ln Y = a + b Ln X) measures the ____
change in Y for a one ____ change in X.
a.
b.
c.
d.
e.
13. The standard deviation of the error terms in an estimated regression equation is known as:
a.
b.
c.
d.
e.
14. In testing whether each individual independent variables (Xs) in a multiple regression equation is
statistically significant in explaining the dependent variable (Y), one uses the:
a.
b.
c.
d.
e.
15. One commonly used test in checking for the presence of autocorrelation when working with time
series data is the ____.
a.
b.
c.
d.
e.
16. The method which can give some information in estimating demand of a product that hasn’t yet come
to market is:
a.
b.
c.
d.
e.
17. Demand functions in the multiplicative form are most common for all of the following reasons except:
a.
b.
c.
d.
e.
18. The Identification Problem in the development of a demand function is a result of:
a.
b.
c.
d.
e.
19. Consider the following linear demand function where QD = quantity demanded, P = selling price, and
Y = disposable income:
QD = −36 −2.1P + .24Y
The coefficient of P (i.e., −2.1) indicates that (all other things being held constant):
a.
b.
c.
d.
e.
20. Consider the following multiplicative demand function where QD = quantity demanded, P = selling
price, and Y = disposable income:
The coefficient of Y (i.e., .2) indicates that (all other things being held constant):
a.
b.
c.
d.
e.
21. One shortcoming of the use of ____ in demand analysis is that the participants are generally aware that
their actions are being observed and hence they may seek to act in a manner somewhat different than
normal.
a.
b.
c.
d.
e.
22. The constant or intercept term in a statistical demand study represents the quantity demanded when all
independent variables are equal to:
a.
b.
c.
d.
e.
23. Novo Nordisk A/S, a Danish firm, sells insulin and other drugs worldwide. Activella, an estrogen and
progestin hormone replacement therapy sold by Novo-Nordisk, is examined using 33 quarters of data
Y = -204 + . 34X1 – .17X2
(17.0) (-1.71)
Where Y is quarterly sales of Activella, X1 is the Novo’s advertising of the hormone therapy, and X2 is
advertising of a similar product by Eli Lilly and Company, Novo-Nordisk’s chief competitor. The pa-
rentheses contain t-values. Addition information is: Durbin-Watson = 1.9 and R2 = .89.
Using the data for Novo-Nordisk, which is correct?
a. Both X1 and X2 are statistically significant.
b. Neither X1 nor X2 are statistically significant.
c. X1 is statistically significant but X2 is not statistically significant.
d. X1 is not statistically significant but X2 is statistically significant.
e. The Durbin-Watson statistic shows significant problems with autocorrelation
24. In which of the following econometric problems do we find Durbin-Watson statistic being far away
from 2.0?
a. the identification problem
b. autocorrelation
c. multicollinearity
d. heteroscedasticity
e. agency problems
25. When there is multicollinearity in an estimated regression equation,
a. the coefficients are likely to be small.
b. the t-statistics are likely to be small even though the R2 is large.
c. the coefficient of determination is likely to be small.
d. the problem of omitted variables is likely.
e. the error terms will tend to have a cyclical pattern.
26. When two or more “independent” variables are highly correlated, then we have:
a. the identification problem
b. multicollinearity
c. autocorrelation
d. heteroscedasticity
e. complementary products
27. Which is NOT true about the coefficient of determination?
a. As you add more variables, the R-square generally rises.
b. As you add more variables, the adjusted R-square can fall.
c. If the R-square is above 50%, the regression is considered significant.
d. The R-square gives the percent of the variation in the dependent variable that is explained by the
independent variables.
e. The higher is the R-square, the better is the fit.
28. Even though insignificant explanatory variables can raise the adjusted R2 of a demand function, one
should not interpret their effects on the regression when
a. testing marketing hypotheses about the determinants of demand
b. analyzing inventory relative to capacity requirements
c. forecasting unit sales for operations planning
d. sales revenue reaches its peak
e. planning for capital budgets
PROBLEMS
1. Phoenix Lumber Company uses the number of construction permits issued to help estimate demand
(sales). The firm collected the following data on annual sales and number of construction permits
issued in its market area:
No. of Construction
Sales
Year
Permits Issued (000)
(1,000,000)
2003
6.50
10.30
2004
6.20
10.10
2005
6.60
10.50
2006
7.30
10.80
2007
7.80
11.20
2008
8.20
11.40
2009
8.30
11.30
(a)
Which variable is the dependent variable and which is the independent variable?
(b)
Determine the estimated regression line.
(c)
Test the hypothesis (at the .05 significance level) that there is no relationship between
the variables.
(d)
Calculate the coefficient of determination. Give an economic interpretation to the value
obtained.
(e)
Perform an analysis of variance on the regression including an F-test (at the .05
significance level) of the overall significance of the results.
(f)
Suppose that 8,000 construction permits are expected to be issued in 2010. What would
be the point estimate of Phoenix Lumber Company’s sales for 2010?
ANS:
Test Bank Chapter 4
Test Bank Chapter 4
(d)
Explained
10.80
11.20
11.30
2. Lenny’s, a national restaurant chain, conducted a study of the factors affecting demand (sales). The
following variables were defined and measured for a random sample of 30 of its restaurants:
Y
= Annual restaurant sales ($000)
X1
= Disposable personal income (per capita) of residents within 5 mile radius
X2
= License to sell beer/wine (0 = No, 1 = Yes)
X3
= Location (within one-half mile of interstate highway—0 = No, 1 = Yes)
X4
= Population (within 5 mile radius)
X5
= Number of competing restaurants within 2 mile radius
The data were entered into a computerized regression program and the following results were
obtained:
(e)
Source of
Variation
Regression
Residual
Total
*Difference is due to round-off error
Test Bank Chapter 4
MULTIPLE R
.889
R-SQUARE
.79
STD. ERROR OF EST.
.40
ANALYSIS OF VARIANCE
DF
Sum Squares
Mean Sqr.
F-Stat
Regression
5
326.13
65.226
18.17
Error
24
86.17
3.590
Total
29
412.30
Variable
Coefficient
Std. Error
T-Value
Constant
.363
.196
1.852
X-1
.00275
.00104
2.644
X-2
76.65
93.70
.818
X-3
164.3
235.4
.698
X-4
.00331
.00126
2.627
X-5
46.2
12.1
−3.818
Questions:
(a)
Give the regression equation for predicting restaurant sales.
(b)
Give an interpretation of each of the estimated regression coefficients.
(c)
Which of the independent variables (if any) are statistically significant at the .05 level in
“explaining” restaurant sales?
(d)
What proportion of the variation in restaurant sales is “explained” by the regression
equation?
(e)
Perform an F-test (at the .05 significance level) of the overall explanatory power of the
regression model.
(a)
Y = .363 + .00275X1 + 76.65X2 + 164.3X3 + .00331X4 − 46.2X5
are equal to zero.
restaurant sales will increase by .00275( $1000) = $2.7
a license.
Expected annual restaurant sales are 164.3( $1000) = $164,300 higher
for a restaurant located within one-half mile of an interstate highway.
increase by .00331( $1000) = $3.31.
radius, expected annual restaurant sales decrease by 46.2( $1000) =
$46,200.
(c)
H1: i 0
Reject H0 if t > t.025, 24 = 2.064 or t < −2.064.
“explaining” restaurant sales.
(d)
According to the R-SQUARE statistic, 79 percent of the variation in restaurant sales is
“explained” by the regression equation.
(e)
H0: All bi = 0 (no relationship)
H1: At least one bi 0
Reject H0 if F > F .05, 5, 24 = 2.62
hypothesis (at the .05 significance level) and concludes that the five independent
variables “explain” a significant proportion of the variation in restaurant sales.
3. The following demand function has been estimated for Fantasy pinball machines:
QD = 3,500 − 40P + 17.5Px + 670U + .0090A + 6,500N
where
P = monthly rental price of Fantasy pinball machines
Px = monthly rental price of Old Chicago pinball machines (their largest competitor)
U = current unemployment rate in the 10 largest metropolitan areas
A = advertising expenditures for Fantasy pinball machines
N = fraction of the U.S. population between ages 10 and 30
(a)
What is the point price elasticity of demand for Fantasy pinball machines when P =
$150, Px = $100, U = .12, A = $200,000 and N = .35?
(b)
What is the point cross elasticity of demand with respect to Old Chicago pinball
machines for the values of the independent variables given in part (a)?
4. Given the following demand function:
Q = 2.0 P−1.33 Y2.0 A.50
where
Q = quantity demanded (thousands of units)
P = price ($/unit)
Y = disposable income per capita ($ thousands)
A = advertising expenditures ($ thousands)
determine the following when P = $2/unit, Y = $8 (i.e., $8000), and A = $25 (i.e., $25,000)
(a)
Price elasticity of demand
(b)
The approximate percentage increase in demand if disposable income percentage
increases by 3%.
(c)
The approximate percentage increase in demand if advertising expenditures are
increased by 5 percent.
Since EY = −2.0, a 3% increase in disposable income would yield an approximate
(c)
Since EA = 0.50, a 5% increase in advertising expenditures would yield an approximate
or a 2.5% increase in demand