Coca-Cola's revenue changes from year to year. Use the model to determine what percentof the variation is explained by Advertising?SUMMARY OUTPUTRegression StatisticsMultiple R0.5829R Square0.3398Adjusted R Square0.3058Standard Error4.514Observations14CoefficientsStandard ErrorIntercept120.302.954Items Ordered2.100.41152.63%58.29%66.02%33.98%41.71%

Using the given output, the value of n is 14.

Answered: Coca-Cola's revenue changes from year…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Interpret the slope in this model.
Regression: Determine what percentage of variation in College GPA is NOT explained by High School GPA. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square ANOVA Standard Error 0.25 Observations 20 Residual 0.3895 Total 0.1517 Regression 1 Intercept 0.1398 High School GPA 18 19 Coefficients 0.79 0.002 Significance F 2.502556445 2.502556 37.77623 8.36481E-06 $5 MS 1.192443555 0.066247 3.695 Standard Error t Stat P-value 0.348877197 2.393669 0.027785 0.000344183 6.146237 8.36E-06
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Explain using evidence from graph a and graph b which graph indicates that the model for the data is a good fit .
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Does Job stress influence overall life satisfaction? A sample of 7,814 people were surveyed, and this is the result of the regression analysis: Descriptive Statistics Mean Std. Deviation N Life satisfaction 7.97 1.194 7817 Job stress 3.16 1.667 7817 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .228a .052 .052 1.162 .052 427.062 1 7815 .000 a. Predictors: (Constant), Job stress b. Dependent Variable: Life satisfaction ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 577.130 1 577.130 427.062 .000b Residual 10561.161 7815 1.351 Total 11138.291 7816 a. Dependent Variable: Life satisfaction b. Predictors: (Constant), Job stress Coefficientsa Model Unstandardized…
Carbon dioxide is produced by burning fossil fuels such as oil and natural gas, and has been connected to global warming. The following output presents the average amounts (in metric tons) of carbon dioxide emissions for the years 1999-2006 per person in the United States and per person in the rest of the world in an effort to determine if non-US per person emissions can help predict US per person emissions. US 20.2 20 19.8 SUMMARY OUTPUT 19.6 19.4 Regression Statistics Multiple R 0.51622776 19.2 R Square 0.2664911 19 Adjusted R Square Standard Error 0.2175905 18.8 0.29668345 Observations 17 18.6 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 39 ANOVA df MS F iignificance F Regression 1 0.479683973 0.479684 5.44965 0.0338864 Residual 15 1.320316027 0.088021 Total 16 1.8 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 22.8013544 1.416019771 16.10243 7.16-11 19.78318 25.819529 19.7831797 25.8195291 Non-US -0.95936795 0.410961292 -2.33445 0.033886…
Age 6 8 10 12 Shoe size 3 4.5 5 7.5 What is the correlation coefficient of the linear regression
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Data for 50 U.S. “states" was used to examine the relationship between violent crime rate (violent crimes per 100,000 persons per year) and the independent variables of urbanization (percentage of the population living in urban areas) and poverty rate. 50 observations were examined. The Excel sheet output for the analysis of this data is shown in the Figure (with some information intentionally left blank). SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.69 Standard Error 8.54 Observations 50 ANOVA df SS MS Significance F Regression 2060 54.9 0.000 Residual 47 18.77 Total 2942 Coefficients Standard Error t Stat P-value B0 B1 B2 31.9 148.2 -2.17 0.035 -4.6 1.654 2.83 0.007 39.3 13.52 2.91 0.006
Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R 0.9086 R Square A Adjusted R Square 0.8181 Standard Error 398.091 Observations B ANOVA df SS MS F Significance F Regression 2 D E G 1.51E-18 Residual C 7448393 F Total 49 42699149 Coefficients Standard Error t Stat P-value Intercept 1304.9048 197.6548 6.6019 3.29E-08 Income ($1000s) 33.133 3.9679 H 7.68E-11 Household Size 356.2959 33.2009 10.7315 3.12E-14 a. Complete the missing entries from A to H in this output b. Estimate the annual credit card charges for a three-person household with an annual income of $40,000.c. Did the estimated regression…

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