The results of a multiple regression analysis, using Minitab, follow. Analysis of Variance Source Regression Residual Error DF SS MS 3 3050 1016.67 26 2200 84.62 Total 29 5250 Predictor Coefficient SE Coefficient Constant 14.00 7.00 2.00 X1 -1.00 0.70 -1.43 X2 30.00 5.20 5.77 X3 0.20 0.08 2.50 a. What are the estimated sales for the Bryne store, which has four competitors, a reglonal population of 0.4 (400,000), and an advertising expense of 30 ($30,000)? b. Compute the R? value. c. Compute the multiple standard error of estimate. d. Conduct a global test of hypothesis to determine whether any of the regression coefficients are not equal to zero. Use the .05 level of significance. e. Conduct tests of hypothesis to determine which of the Independent variables have significant regression coefficients. Which varlables would you consider eliminating? Use the .05 significance level.

The results of a multiple regression analysis, using Minitab, follow. Analysis of Variance Source Regression Residual Error DF SS MS 3 3050 1016.67 26 2200 84.62 Total 29 5250 Predictor Coefficient SE Coefficient Constant 14.00 7.00 2.00 X1 -1.00 0.70 -1.43 X2 30.00 5.20 5.77 X3 0.20 0.08 2.50 a. What are the estimated sales for the Bryne store, which has four competitors, a reglonal population of 0.4 (400,000), and an advertising expense of 30 ($30,000)? b. Compute the R? value. c. Compute the multiple standard error of estimate. d. Conduct a global test of hypothesis to determine whether any of the regression coefficients are not equal to zero. Use the .05 level of significance. e. Conduct tests of hypothesis to determine which of the Independent variables have significant regression coefficients. Which varlables would you consider eliminating? Use the .05 significance level.

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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A prediction equation for starting salaries (in $1,000s) and SAT scores was performed using simple linear regression. In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square 0.935018123 0.87425889 0.860287655 Standard Error 3.307295949 Observations 11 ANOVA df Significance F MS Regression 684.4652 10.93821 1 684.4652324 62.57564 2.42144E-05 Residual 98.44385847 Total 10 782.9090909 Coefficients -29.14060468 t Stat -3.36493 7.910476 Stændard Error P-Value Lower 95% Intercept SAT 0.008324 2.42E-05 -48.73108387 0.046728866 8.660080997 0.065443841 0.008273059 SAT is not a good predictor for starting salary. The significance level for SAT indicates the slope is equal to zero. The significance level for SAT indicates the slope is not equal to zero. The significance level for the intercept indicates the model is not valid.
This model regresses Salary in a financial services company onto education Q3: [educ] measured as highest grade level attained in formal education. Fill in the blanks and answer the questions at the end [4 points each except where stated. SEE DESCRIPTIVE STATS AT THE END OF THIS QUESTION - YOU MAY NEED IT IN THE QUESTIONS BELOW Regression Analysis: salary versus educ Analysis of Variance Source DF MS F-Value Р- Value Regression 0.000 1 60178217760 60178217760 365.38 Error 472 (а) (b) Total 473 1.37916E+11 *** ***NOTE: This number = 1.37916 х 1011 Model Summary R-sq R-sq (adj) R-sq (pred) 43.51% 42.98% (c) (d) Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -18331 2822 -6.50 0.000 educ 3910 205 0.000 1.00 (е) Regression Equation salary = -18331 + 3910 educ f) Calculate the confidence interval for the prediction of 'salary' when educ = 12 g) Perform a hypothesis test to determine whether the model is usable. Show your work.
See the attached image for the introduction. Question: Fill in a blank ANOVA table.
A multiple regression analysis produced the following tables. Predictor Coefficients StandardErrort Statistic p-valueIntercept 624.5369 78.49712 7.956176 6.88E-06x 8.569122 1.652255 5.186319 0.000301x 4.736515 0.699194 6.774248 3.06E-05Source df SS MS F p-valueRegression2 1660914 830457.1 58.319561.4E06Residual 11 156637.5 14239.77Total 13 1817552The adjusted R is ____________.0.91380.88910.88510.8981
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The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
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