# Econ

**Topics:**Regression analysis, Linear regression, Econometrics

**Pages:**10 (1509 words)

**Published:**December 11, 2013

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.

the association may be due to pure chance

b.

the association may be the result of the influence of a third common factor c.

both variables may be the cause and the effect at the same time d.

the association may be hypothetical

e.

both c and d

5.In regression analysis, the existence of a significant pattern in successive values of the error term constitutes: a.

heteroscedasticity

b.

autocorrelation

c.

multicollinearity

d.

nonlinearities

e.

a simultaneous equation relationship

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.

autocorrelation

b.

a simultaneous equation relationship

c.

nonlinearities

d.

heteroscedasticity

e.

multicollinearity

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.

semilogarithmic

b.

double-logarithmic

c.

reciprocal

d.

polynomial

e.

cubic

8.The correlation coefficient ranges in value between 0.0 and 1.0. a.

true

b.

false

9.The coefficient of determination ranges in value between 0.0 and 1.0. a.

true

b.

false

10.The coefficient of determination measures the proportion of the variation in the independent variable that is "explained" by the regression line. a.

true

b.

false

11.The presence of association between two variables does not necessarily imply causation for the following reason(s): a.

the association between two variables may result simply from pure chance b.

the association between two variables may be the result of the influence of a third...

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