The output of a different regression analysis on the effect of television advertising spending on sales is given below. For this study they explored only 1type of advertising spending. They modelled it as y = ao + a1 + a1x?. The table below gives the summary output of this regression analysis.Regression Analysis: Sales versus SpendAnalysis of VarianceSourceDFMSF-ValueP-ValueRegressiond freg23,825.03MSRF-valp-valErrordferr571.02MSETotal55Find the F-value in the above table. Give your answer to 3 decimal places.

In the given model there are two x variables. That is, p=2. The regression degrees of freedom is,…

Answered: The output of a different regression…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 4SGR

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D& T LTD marketing team needed more information about the effectiveness of their 3 main mode of advertising. To determine which type is the most effective, the manager collected one week’s data from 25 randomly selected stores. For each store, the following variables were recorded: Weekly gross sales Weekly expenditure on direct mailing (Direct) Weekly expenditure on newspaper advertising (Newspaper) Weekly expenditure on television commercials (Television) Following is the regression output based on the above-mentioned data. SUMMARY OUTPUT Regression Statistics Multiple R 0.442…
D& T LTD marketing team needed more information about the effectiveness of their 3 main mode of advertising. To determine which type is the most effective, the manager collected one week’s data from 25 randomly selected stores. For each store, the following variables were recorded: Weekly gross sales Weekly expenditure on direct mailing (Direct) Weekly expenditure on newspaper advertising (Newspaper) Weekly expenditure on television commercials (Television) Following is the regression output based on the above-mentioned data. SUMMARY OUTPUT Regression Statistics Multiple R 0.442 R Square A Adjusted R Square 0.080 Standard Error 2.587 Observations 25 ANOVA Df SS MS F Significance F Regression B 34.1036 E F…
The regional transit authority for a major metropolitan area wants to determine whetherthere is a relationship between the age of a bus and the annual maintenance cost. A sampleof ten buses resulted in the following data: a. Develop a scatter chart for these data. What does the scatter chart indicate about therelationship between age of a bus and the annual maintenance cost?b. Use the data to develop an estimated regression equation that could be used to predictthe annual maintenance cost given the age of the bus. What is the estimated regressionmodel?c. Test whether each of the regression parameters b0 and b1 is equal to zero at a 0.05level of significance. What are the correct interpretations of the estimated regressionparameters? Are these interpretations reasonable?d. How much of the variation in the sample values of annual maintenance cost does themodel you estimated in part b explain?e. What do you predict the annual maintenance cost to be for a 3.5-year-old bus?
A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: What is the population model of this regression problem? What is the sample estimates of the regression problem? Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer.
The county assessor feels that the use of more independent variables in the regression equation might improve the overall explanatory power of the model. In addition to size, the assessor feels that the total number of rooms, age, and whether or not the house has an attached garage might be important variables affecting selling price. The data for the 15 randomly selected dwellings are shown in the following table. What proportion of the total variation in selling price is explained by the regression model?
A study was conducted on 64 female college athletes. The researcher collected data on a number of variables including percent body fat, total body weight, height, and age of athlete. The researcher wondered if % body fat (%BF), height (HGT), and/or age are significant predictors of total body weight. All conditions have been checked and are met and no transformations were needed. The technology output from the multiple regression analysis is given below. What percent of the variation in total body weight is being explained by the regression model with these three explanatory variables?
As a marketing manager for TriFood, you want to determine whether store Sales (# sold in one month) of TriPower bars are related to price (in cents) of TriPower bars and in-store promotional expenditures (in dollars) for TriPower bars. You conduct a multiple regression analysis with store Sales (Y) as the response variable, and Price (X1) and Promotion (X2) as explanatory variables. Use the pictured Excel regression output below to answer the questions. a) Write the estimated multiple regression equation. b) Should one interpret the estimated value for the intercept (yes or no)? c) Interpret the value for Standard Error under Regression Statistics. d) Interpret the value for R square. e) State the hypotheses for assessing the statistical significance of the overall regression equation. f) Interpret the estimated coefficient for price. g) An external consultant to TriFoods believes that for every $1 increase in promotional expenditures, sales will increase by 4.7 units. Test the…
The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance. Copy-paste the data and run the analysis using MS Excel. starting salary GPA Years of experience Civil Service Ratings 15000 80.1 1 79.5 15000 81.2 1 78.0 15500 81.3 2 79.0 16000 82.4 3 80.0 16200 83.4 3 85.0 17500 87.9 4 89.9 18000 90.3 5 89.1 16300 84.2 3 84.1 17000 87.0 4 89.0 17900 88.1 5 89.2 What is the computed R square of the resulting multiple linear regression and its interpretation?

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