Below you are given a partial Excel output based on a sample of 16 observations. ANOVA df SS MS F Regression 4,853 2,426.5 Residual 485.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 The sum of squares due to error (SSE) equals _____. a. 4,853 b. 485.3 c. 37.33 d. 6,308.9

Below you are given a partial Excel output based on a sample of 16 observations. ANOVA df SS MS F Regression 4,853 2,426.5 Residual 485.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 The sum of squares due to error (SSE) equals _____. a. 4,853 b. 485.3 c. 37.33 d. 6,308.9

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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Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 293.3 293.3 2.01 0.158 Residual Error 174 25386.6 145.9 Total 175 25679.9 The F-statistic is _____________.The p-value is ___________________
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…
7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression…
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?
For an ANOVA test of significance of a regression model with 10 regressor variables and 50 observations, what is the degree of freedom of the SSr? choices 11 10 39 49
Below you are given a partial Excel output based on a sample of 16 observations. ANOVA df SS MS F Regression 4,853 2,426.5 Residual 485.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 The degrees of freedom for the sum of squares explained by the regression (SSR) are _____. a. 15 b. 13 c. 2 d. 3
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…
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 20 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 2 of 2: Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?
Using the Excel output reported above, if we were to test to see whether "Attendance" is statistically significantly associated with "Score received on the exam," we would conclude that we should Regression Statistics Multiple R R Square Standard Error Observations Intercept Attendance 0.142620229 0.02034053 20.25979924 22 Coefficients Standard Error 39.39027309 37.24347659 0.340583573 0.52852452 T Stat 1.057642216 0.644404489 P-value 0.302826622 0.526635689 O Reject the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Reject the null hypothesis and conclude that Attendance is is NOT statistically significantly associated with "Score received on the exam" Accept the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Fail to reject the null hypothesis and conclude that we cannot say that Attendance is statistically significantly associated with…
Shown below is a portion of a computer output for regression analysis relating y (dependent variable) and x (independent variable). ANOVA df SS Regression 1 24.061 Residual 10 67.979 Coefficients Standard Error Intercept 11.064 2.049 x −0.566 0.301 (a) What has been the sample size for the above regression analysis? (b) Perform a t-test and determine whether or not x and y are related. Let ? = 0.05. State the null and alternative hypotheses. (Enter != for ≠ as needed.) H0: Ha: Find the value of the test statistic. (Round your answer to three decimal places.) Find the p-value. (Round your answer to four decimal places.) p-value = What is your conclusion? .
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…
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 *…