13. Selling price and the amount spent on advertising were entered into a multiple regression to...

Question

Question

13. Selling price and the amount spent on advertising were entered into a multiple regression to determine what affects flat

math Statistics-And-Probability

Add a comment Improve this question Transcribed image text

Answer 1

Answer #1

F value = MSR/MSE = 8238.7/112.5 = 73.23

Corresponding p - Value = 0.000

So the model is significant. Hence,

Both I and II are correct.

Option D is correct.

Add a comment

Answer 2

Similar Homework Help Questions

13. Selling price and the amount spent on advertising were entered into a multiple regression to...

13. Selling price and the amount spent on advertising were entered into a multiple regression to determine what affects flat panel LCD TV sales. Using the output below, which statement(s) is(are) correct? Analysis of Variance Source DF (Adj)SS MS Regression 2 16477.3 8238.7 Error 27 3038.0 112.5 Total 29 19515.4 I. The F statistic to determine the overall significance of the estimated multiple regression model is 73.23. II. The multiple regression model is overall significant. III. It is not appropriate...
Based on the multiple regression model, does demand for beef respond significantly to price of pork?...

Based on the multiple regression model, does demand for beef respond significantly to price of pork? Why? df MS - - - - Source SS -----------+------- Model | 235.766738 Residual 57.3509099 ----------- ------- Total L 293.117648 3 13 78.5889127 4.41160845 Number of obs = EU3, 13) = Prob>F = R-squared = Adj R-squared = Root MSE = 17 17.81 0.0001 0.8043 0.7592 2.1004 - - - - - - - - - - - 16 18.319853 - - - -...
(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Err...

(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Error.1488 Observations21 ANOVA Source Regression Residual Total df MS Significance of F 1.66E-07 3 308.68 35.117 102.893 2.930 17 20 358.49 49.81 Coefficient Standard Error 6.2000 0.7260 0.7260 0.9500 t Stat 3.7097 0.2755 -2.0523 0.5158 23.00 0.20 Intercept X2 X3 0.49 (i) Find the limits of the 95 percent confidence interval...

For this assignment I have to analyze the regression (relationship between 2 independent variables and 1...

For this assignment I have to analyze the regression (relationship between 2 independent variables and 1 dependent variable). Below is all of my data and values. I need help answering the questions that are at the bottom. Questions regarding the strength of the relationship Model: Median wage (y) = 40.3774 - 2.0614 * Population + 0.0284 * GDP Predictor Coefficient Estimate Standard Error t-statistic p-value Constant B0 40.3774 1.1045 36.558 0 Population B1 -2.0614 0.5221 -3.948 0.0003 GDP B2 0.0284...
SUMMARY OUTPUT Regression Statistics Multiple R 0.9448 R2 0.8927 Adj. R2 0.8853 SY.X...

SUMMARY OUTPUT Regression Statistics Multiple R 0.9448 R2 0.8927 Adj. R2 0.8853 SY.X 133.14 N 32 ANOVA df SS MS F P-value Regression 2 4277160 2138580 120.6511 0.0000 Residual 29 514034.5 17725.33 Total 31 4791194 Coeff. Std. Err. t Stat P-value Lower 95% Upper 95% Intercept -1336.72 173.3561 -7.71084 0.0000 -1691.2753 -982.16877 X1 12.7362 0.90238 14.114 0.0000 10.890623 14.5817752 X2 85.81513 8.705757 9.857286 0.0000 68.009851 103.620414 With respect to the null hypothesis for...
A real estate agent wants to use a multiple regression model to predict the selling price...

A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: age of the home in years Bath: total number of bathrooms LotArea: total square footage of the lot on which the house is built TotRms_AbvGrd: total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the...

G H I J K L M N O Weekly Gross Revenue (1000s) Television Advertising ($1000s)...

G H I J K L M N O Weekly Gross Revenue (1000s) Television Advertising ($1000s) Newspaper Advertising ($1000s) SUMMARY OUTPUT 96 1.5 1.5 92 2.5 25 DO VOU AWN 3.3 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.976822037 0.954181292 0.935853809 0.514395797 23 ma 42 2.5 ANOVA df Predicted Revenue 97.8529365 SS MS Significance F 2 27.55198482 13.77599 52.06287 0.000449 5 1.323015179 0.264603 28.875 5 Regression Residual Total 3.7 7 Coefficients Standard Error Stat P-value...
only part II is needed Regardless of your answer to (a), you come up with the following multiple regression model. b. Coefficients: Estimate Std. Error t value Pr>lt (Intercept) 72.2285 1.2697 56....

only part II is needed Regardless of your answer to (a), you come up with the following multiple regression model. b. Coefficients: Estimate Std. Error t value Pr>lt (Intercept) 72.2285 1.2697 56.89 2e-16 X2 X3 Residual standard error: 7.25 on 191 degrees of freedom Multiple R-squared: 0.494, Adjusted R-squared: 0.489 F-statistic: 93.3 on 2 and 191 DF, p-value: <2e-16 0.4590 0.0524-8.76 1.1e-15 0.4146 0.1290 3.21 0.0015** I) What percentage of the total variation in Life Expectancy can you explain with...
PLEASE ANSWER ALL QUESTIONS ! 1. The following sample observations were randomly selected: X Y 10...

PLEASE ANSWER ALL QUESTIONS ! 1. The following sample observations were randomly selected: X Y 10 4 5 6 6 5 4 7 3 7 What is the coefficient of correlation? Select one: a. 0.90 b. -0.46 c. -0.95 d. 0.82 2. The regression output from Excel indicates that the Significance F is 0.001. Does that mean there is a significant relationship? Select one: a. Yes, because it is small. b. Yes, because it is large. c. No, because it...

Use the chart below to answer Questions 16 through 33. The study looks at what affects...

Use the chart below to answer Questions 16 through 33. The study looks at what affects wage (wage, in dollars). The two factors being examined are years of education (X1=education, in years) and previous work experience (X2=Yes/No). Source SS df MS Sample Size 80437 Model 7.25x1012 i) 3.63x1012 F-stativ) Residual 25.73x1012 80434 3.2x102 Prob>F 0.00 Total 32.98x10 ii) R-squared v). Adj R-squared 0.20 Coef SE T-stat P-value 95%CI Educ 4113 28 iii) 0.000 4058 4168 Experience 259 5 0.000 250...

13. Selling price and the amount spent on advertising were entered into a multiple regression to...

Homework Answers