🏷️ LIMITED TIME OFFER: GET 20% OFF GRADE+ YEARLY SUBSCRIPTION →
Log in
Economics
11
answers
0
watching
33
views
27 Nov 2019

The _________ measures the percentage of total variation in the dependent variable that is explained by the regression equation. If it is close to _____, the regression line is a good fit to the data.
Answer
A. p-value; 0.05
B. F-test; 1.0
C. R2 statistic; zero
D. R2 statistic; 1.0

Watch
11
answers
0
watching
33
views

For unlimited access to Homework Help, a Homework+ subscription is required.

Unlock all answers

Get 1 free homework help answer.
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Get unlimited access
Already have an account? Log in
Share
Patrina Schowalter
Patrina SchowalterLv2
22 Jul 2019
Get unlimited access
Already have an account? Log in
Share

Related textbook solutions

Principles of Economics
2nd Edition, 2017
Openstax
ISBN: 9781947172364
Principles of Macroeconomics
2 Edition,
Openstax
ISBN: 9781947172388
Principles of Microeconomics
2 Edition,
Openstax
ISBN: 9781947172340

Related questions

Demand Estimation 1. A multiple regression model, Y = a + bX + cX 2, is estimated by creating a new variable named “X2” that equals X 2 (X square). A computer package produces the following output:

Dependent Variable Y

R Square

F-Ratio

P-Value on F

Observations :27

0.8766

85.25

0.0001

Parameter

standard

Variable

Estimate

Error

T-ratio

P-value

Intercept

8000

3524.0

2.27

0.0325

X

-12.00

4.50

-2.67

0.0135

X2

0.005

0.002

2.5

0.0197

a. Provide the estimated equation. b. Test to see if the estimates of a, b, and c are statistically significant at the 5 percent significance level. c. What is the exact level of significance for a, b, and c? d. What is the fraction of total variation in Y that is explained by the regression equation? e. Is the overall regression equation statistically significant at the 5 percent level? What is the exact level of significance of the equation as a whole? f. If X is equal to 1,200, how much is Y?

Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of its nonprescription cold remedies:

Y = −1.04 + 0.24X1 − 0.27X2

where Y = quarterly sales (in thousands of cases) of the cold remedy

X1 = Cascade’s quarterly advertising (x$1,000)for the cold remedy

X2 = competitors’ advertising for similar products (x $10,000)

Here is additional information concerning the regression model:

sb1 = 0.032

sb2 = 0.070

R2 = 0.64

se = 1.63

F-statistic = 31.402

Durbin-Watson (d) statistic = 0.4995

a. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the cold remedy?

b. What proportion of the total variation in sales is explained by the regression equation?

c. Perform an F-test (at the 0.05 level) of the overall explanatory power of the model.

d. What additional statistical information (if any) would you find useful in the evaluation of this model?

TYPE 1.300** (0.590)

PAR -0.195** (0.291)

SR -0.005 (0.068)

CR 0.483 (0.329)

LOC -0.575 (0.655)

THANDICAP -0.037 (0.091)

PHANDICAP -0.181 (0.075)

FEMALE 0.159 (0.708)

The table shows regression coefficients (standard errors in parenthesis) for the independent variables. The dependent variable is score. (again, under the stabelford system higher is better).

Type is a dummy variable with 1 for net score tournaments and 0 for gross score tournaments. Explain the meaning of the coefficient.

Is it significant at the 5% level? Explain.

What is the t-statistic on TYPE? ________________

The variables PAR and CR are measures of difficulty of the course, and PHANDICAP is a measure of the golfer's ability. Why are these variables included in the regression equation?

Which variable should be added to the regression if you want to test whether the effect of TYPE is different for male and female players? Why?

Weekly leaderboard

Home
Homework Help 3,900,000
Economics 400,000
Start filling in the gaps now
Log in