Regression. A coach wants to see the relationship between the statistics of practice games and official games of a local soccer team. A sample of 14 players was used and the resulting (partial) Excel output is shown below. Assume both x and y form normal distributions.RegressionMultiple RR SquareAdjusted R SquareStandard ErrorObservationsStatistics0.705240.6683958.70363314Coefficients StandardErrort StatP-valueLower95%Upper95% (d) The test statistic isOA. 4.794OB. 40.78C. 0.362OD. None of the other answers(e) The degrees of freedom are:OOA. 13OB. 14OC. 12OD. 11(f) At the 5% significance level it can be concluded that there is evidence to suggest the correlation coefficient isA. positiveB. not zeroC. negativeD. zero

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Answered: Regression. A coach wants to see the…

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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I need help with these questions 1. do the covariates and factors interact? 2. can you conclude a homogeneity of regression slopes? 3. can you conclude homogeneity of variance?
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 R Square Adjusted R Square Standard Error 0.9086 A 0.8181 398.0910 Observations ANOVA df SS MS Significance F Regression 2 1.50876E-18 Residual C 7448393.148 F Total 49 42699148.82 Coefficients Standard Error t Stat P-value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household Size 356.2959 33.2009 10.7315 3.12342E-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 equation provide a good fit to the data? Explain
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 C E G 1.50876E-18 Residual D 7448393 F Total 49 42699149 Coefficients Standard Error t Stat P-Value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this outputb. Estimate the annual credit card charges…
Assume the samples are random and independent, the populations are nomally distributed, and the population variances are equal. The table available below shows the prices (in dollars) for a sample of automobile batteries. The prices are classified according to battery type. At a = 0.10, is there enough evidence conclude that at least one mean battery price is different from the others? Complete parts (a) through (e) below. E Click the icon to view the battery cost data. (a) Let u1. P2. H3 represent the mean prices for the group size 35, 65, and 24/24F respectively. Identify the claim and state Ho and H. H Cost of batteries by type The claim is the V hypothesis. Group size 35 Group size 65 Group size 24/24F 101 111 121 124 D 146 173 182 278 124 140 141 89 (b) Find the critical value, Fo, and identify the rejection region. 90 79 84 The rejection region is F Fo, where Fo = (Round to two decimal places as needed.) (c) Find the test statistic F. Print Done F= (Round to two decimal places as…
Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 291.4 291.4 2.01 0.158 Residual Error 174 25230.0 145.0 Total 175 25521.4 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is =The p-value is =
A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…
When there are omitted variables in your regression, then... Group of answer choices This has no effect on the estimation of the explanatory variable because the variable is omitted This will always bias the OLS estimation of the explanatory variable the estimation of the explanatory variable(s) is unaffected The OLS estimation is biased if the omitted variables are correlated with the included variable
3.Which model do you think is the “best” reduced model? Discuss why you choose this model. Analysis of Variance Table (Step back model) Response: rent Df Sum Sq Mean Sq F value Pr(>F) age 1 21000 21000 17.1136 0.0003079 *** sqft 1 35364 35364 28.8196 1.134e-05 *** sd 1 5961 5961 4.8576 0.0362339 * unts 1 8678 8678 7.0722 0.0130049 * gar 1 33364 33364 27.1899 1.713e-05 *** cp 1 7641 7641 6.2269 0.0189934 * Residuals 27 33131 1227 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step forward model: Response: rent Df Sum Sq Mean Sq F value Pr(>F) age 1 21000 21000 16.6255 0.000406 *** sqft 1 35364 35364 27.9976 1.757e-05 *** sd 1 5961 5961 4.7191 0.039517 * unts 1 8678 8678 6.8705 0.014697 * gar 1 33364 33364 26.4144 2.599e-05 *** cp 1 7641 7641 6.0493 0.021176 *…
Answer the Question 1. In multiple regression: a. the ANOVA table tells us whether the three main regression coefficients (R, R-square, adjusted R-square) are significantly different from 1. .50 2. 1.00 3. Zero 4. 1.50 b. the adjusted R-square can be interpreted as 1. Th percentage of variance accounted for in the dependent variable by the set of independent variables 2. The percentage of variance accounted for in the dependent variable by the set of independent variables minus an estimate penalty 3. The percentage of variance accounted for in the dependent variable by a single independent variable 4. The strength of a relationship between the dependent variable and the set of independent variables
After performing an ANOVA analysis, which of the following tests are performed in order to determine whether the means of specific categories are significantly different from each other? Goodness-of-fit Linear regression Bonferonni Comparing proportions

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