7. Consider the following ANOVA table for a multiple regression model:SourcedfMSFRegression2118.847559.4237540.92168993Residual913.06921.452133333Total11131.9167a) Complete the remaining entries in the table.b) What is the size of this sample?c) How many independent variables are in this model?d) Calculate the multiple coefficient of determinatione) Test the significance of the overall regression model using a=0.05.

Dear students as per guidelines we answer the first three subparts only . Please post remaining in a…

Answered: 7. Consider the following ANOVA table…

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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Last year, TimeWise conducted a study that aimed at predicting the waiting time (in minutes) at the checkout line of the Ranch 99 Supermarket in South Jakarta based on the number of customers in one particular month. Using the simple regression method on a dataset with a sample size of 30, the regression output tables are given below: SUMMARY OUTPUT Regression Statistics Multiple R 0.88728417 R Square 0.7872732 Adjusted R Square 0.77967582 Standard Error 0.59521532 Observations 30 ANOVA MS Significance F Regression 36.7121241 36.7121241 103.6242271 6.48782E-11 Residual 28 9.91987592 0.35428128 Total 29 46.632 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept -0.4479791 0.2782914 -1.6097484 0.118670876 -1.018033233 0.12207495 Customers 0.12847188 0.01262053 10.1795986 6.48782E-11 0.102619905 0.15432385 a. Determine the simple regression model to predict the waiting time b. Interpret the meaning of the regression coefficient of the independent variable c. What is…
comparison of the precisions of two machines developed for extracting juice from oranges is to be made using the following data. Is there sufficient evidence to indicate that there is a difference in the precision of the two machines at the 5% level of significance? Machine A Machine B s2 = 1.4 ounces2 s2 = 3.1 ounces2 n=25 n=20
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 multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept -139.609 2548.989 -0.05477 0.957154x 24.24619 22.25267 1.089586 0.295682x 32.10171 17.44559 1.840105 0.08869Source df SS MS F p-valueRegression 2 302689 151344.5 1.705942 0.219838Residual 13 1153309 88716.07Total 15 1455998Using = 0.01 to test the null hypothesis H :?1 = ?2 = 0, the critical F value is ____.6.701.964.845.995.70
a) which for these data is 32.1470, 42.3520, or 10.0674 c) that minimizes the error sum of squares, total sum of squares, or regression sum of squares…for which these data is 32.1470, 42.3520, or 10.0674
Refer to the ANOVA table for this regression. Source d.f. SS MS Regression 2 578,850 289,425 Error 21 378,420 18,020 Total 23 957,270 Using Appendix F on the pictures, calculate F.05, a=0.05, F statistics, R2, and R2 adj.
Use the Stata output below to answer the following question. The data used in this analysis is from a sample of airlines. The variables used are: fare-avg price of a one-way fare, in dollars dist- distance of the flight, in miles .reg fare ldist. Source Model Residual 551.391705 Total lfare SS ldist _cons 875.094374 df The OLS results suggest that 1 323.702668 120024315 4,594 MS Coef. Std. Err. 4,595 190444913 Number of obs F(1, 4594) Prob > F R-squared Adj R-squared Root MSE t P>|t| .4025646 .0077517 51.93 0.000 2.399834 .0521601 46.01 0.000 4,596 2696.98 0.0000 0.3699 0.3698 .34645 [95% Conf. Intervall .3873676 .4177617 2.297575 2.502093 a 1% increase in distance is associated with approximately a $.40 increase in the price of the fare. a 1% increase in distance is associated with approximately a .40% increase in the price of the fare. a 1% increase in distance is associated with approximately a 40 % increase in the price of the fare. None of the above.
In the following regression, X = total assets ($ billions), Y = total revenue ($ billions), and n = 64 large banks. R2 0.519 Std. Error 6.977 n 64 ANOVA table Source SS df MS F p-value Regression 3,260.0981 1 3,260.0981 66.97 1.90E-11 Residual 3,018.3339 62 48.6828 Total 6,278.4320 63 Regression output confidence interval variables coefficients std. error t Stat p-value Lower 95% Upper 95% Intercept 6.5763 1.9254 3.416 .0011 2.7275 10.4252 X1 0.0452 0.0055 8.183 1.90E-11 0.0342 0.0563 (a) Write the fitted regression equation. yˆy^ = Not attempted + Not attempted X (b-1) State the degrees of freedom for a two-tailed test for zero slope, and use Appendix D to find the critical value at α = .05. (Round t critical value to 3 decimal places.) Degrees of freedom Not attempted tcrit ± Not attempted…
Consider the following ANOVA table for a multiple regression model relating housing prices (in thousands of dollars) to the number of bedrooms in the house and the size of the lot on which the house was built (in square feet). There were 9090 total observations. Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size)Estimated Price=20,160.07+2188.83(Bedrooms)+0.2139(Lot Size) ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 306,443.7975306,443.7975 153,221.8988153,221.8988 21.727421.7274 2.2211E-082.2211E-08 Residual 8787 613,525.5190613,525.5190 7052.01757052.0175 Total 8989 919,969.3165919,969.3165 What percent of variation in housing prices is explained by the number of bedrooms and lot size? Round your answer to two decimal places
Refer to the ANOVA table for this regression. Source SS d.f. MS Regression 1,164,578 5 232,916 Residual 1,500,689 45 33,349 Total 2,665,267 50 (a) State the degrees of freedom for the F test for overall significance.
A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418 Observations 18 ANOVA df SS MS F Significance F Regression 2 1562918.941 781459.5 170.6503 4.80907E-11 Residual 15 68689.55855 4579.304 Total 17 1631608.5 Coefficients Standard Error t Stat P-value Intercept 1959.709718 306.4905312 6.39403 1.21E-05 X1 -0.469657287 0.264557168 -1.77526 0.096144 X2 -2.163344882 0.278361425 -7.77171 1.23E-06 Using α = 0.01 to test the…

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