Please help with these questions:

QUESTION 7:

Use the following equations for the questions below:

=75200

=900 =20+2.420.013 =15850+0.42+22

Answer the following: / =

a. 751

b. 75

c. 125

d. 75

Question 8

Use the above equations: / =

a. 900

b. 0

c. 1

d. 900

Question 9

Use the equations above:

/ = ?

a. 20+4.8+0.033

b. 20+2.40.012

c. 20

d. 20+4.80.032

Question 10

Use the equations above: / =

a. 15850+0.8+22

b. 15+0.42

c. 850+22

d. 15+0.8

Question 11

Which of the following statements about regression analysis is NOT correct? Select one:

a. It provides estimates of the relationships between dependent and independent variables, as well as measures of statistical fit or confidence.

b. It attempts to find parameter values such that the difference between observed and predicted (or estimated) values is minimized.

c. It can be used to predict values of a dependent variable using one or more independent variables.

d. With the same set of data and same choice of dependent and independent variables, it will

yield different values for parameter estimates and test statistics each time a regression is run.

Question 12

You believe that variable directly (positively) affects the value of variable . After running a regression, the parameter estimate of is +0.49 and the t-statistic is 0.03. You would thus

conclude that Select one:

a. Since the t-statistic is less than the parameter value, the relationship between and is actually negative or inversely related.

b. The parameter has the correct sign and is statistically significant.

c. While statistically significant, the parameter has an unexpected sign. The variable should be discarded from the regression model and re-run.

d. The parameter has the expected sign but is statistically insignificant; therefore you cannot rule out the possibility that has no discernible effect on .

Question 13

A low p-value for an independent variable (say, 0.04) indicates that the parameter estimate is not statistically significant; the variable should be discarded from future regression models. Select one:

True

False

Question 14

Assume you run a regression on two different models (sets of independent variables). The first model results in an 2R2 of 0.60 and the second model results in an 2 of 0.64. This would mean that Select one:

a. The second model has higher overall explanatory power, but whether it is a "better" model than the first model depends on other factors too (expected signs of the coefficients, significant t-

stats/p-values, etc.).

b. The second model has better overall explanatory power, but a better 2 is only gained at the expense of other factors. The first model is better at explaining individual coefficient estimates than the second.

c. The second model is unambiguously better; since 2 signifies the explanatory power of the entire model, the second model's other results (expected signs, t-stats/p-values) are not as important. The first model should be seen as inferior.

d. The independent variables in the first model are not statistically significant, despite what the t-

stats and p-values suggest.

Question 15

If you ran a regression using data on many different used car sales, and had selling price as the dependent variable, the expected sign on the variable "mileage" (how many miles the car had been driven) would be negative.

True

False

Question 16

If you ran a regression using data on many different used car sales, and had selling price as the dependent variable, the expected sign on the variable "sound" (a dummy variable representing whether the car has upgraded speakers, 1 = upgraded and 0 = standard) would be negative.

True

False

The following are regression results where Car Price is the dependent variable: Regression Statistics

2=0.446 Adjusted 2=0.441

Observations = 804

Independent Variables

Coefficients Standard Error t Stat P-value

Intercept 6758.76 1876.967 3.601 0.000

Mileage -0.17 0.032 -5.326 0.000

Liter -787.22 867.062 -0.908 0.364

Cruise 6289.00 657.992 9.558 0.000

Leather 3349.36 597.681 5.604 0.000

Cylinder 3792.38 683.180 5.551 0.000

Doors -1542.75 320.456 -4.814 0.000

Sound -1993.80 571.776 -3.487 0.001

Car Price is measured in dollars. The independent variables are:

Mileage: number of miles the car has been driven

Cylinder: number of cylinders in the engine

Liter: a more specific measure of engine size

Doors: number of doors

Cruise: dummy variable representing whether the car has cruise control (1 = cruise, 0 = no cruise)

Sound: dummy variable representing whether the car has upgraded speakers (1 = upgraded, 0 = standard)

Leather: dummy variable representing whether the car has leather seats (1 = leather, 0 =

cloth)

Question 17

This model (set of independent variables) explains approximately how much of the variation in car prices in this dataset?

a. 10.441=55.9

b. 44.6

c. 44.1

d. 80.4

Question 18

What is true about the estimated coefficients?

a. The negative "Door" coefficient indicates that more doors on a car reduce the car's mileage. b. The "Sound" coefficient is unexpectedly negative, suggesting that cars with upgraded

speakers are associated with a lower selling price.

c. The "Mileage" coefficient is unexpectedly negative, since higher miles driven should be

associated with a higher selling price.

d. The "Mileage" coefficient is unexpectedly small compared to the others, suggesting that

miles driven is unimportant in the selling price of a used car.

Question 19

The results from the t-statistics and p-values suggests that

a. Only the coefficient for "Liter" is statistically insignificant. All of the other coefficients are statistically significant at the 1% level.

b. Mileage, Liter, Doors, and Sound are all insignificant since the t-stats are negative.

c. Mileage, Cylinder, Doors, Cruise, and Leather are all insignificant since the p-values are

zero, meaning unrelated to car price.

d. "Liter" is the only statistically significant estimate since it's p-value is 36.4%.

Question 20

Which of the following statements is correct, based on the regression results above?

a. Cars with cruise control have about 6,300 fewer miles on them than cars without cruise control, everything else equal.

b. Since the "Liter" coefficient is insignificant, the true effect is actually +787.22 and not -787.22

c. Every additional mile driven increases the price of the car by $0.17

d. Having four doors instead of two is associated with more than a $3,000 lower price, everything else equal.