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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How do I fill out the regression analysis box for my spread sheet? I attached a revised picture. Thank you.
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.
The regression equation is Health Index= y + a Age + ß Blood sugar + 8 Blood Pressu SE 20986 Coef 2912 339.28 71.95 Age Blood sugar 209.2 179.3 Blood pressure 207.2 225.4 S = 962.233 R-Sq = 86.6% R-Sq (adj) = 76.5% Coef Constant Analysis of Variance Source DF Regression 3 Residual Error 4 Total 7 SS 23863180 3703570 27566750 MS T 7.21 4.72 * 0.92 7954393 925892 F *** P 0.002 0.009 0.308 до 0.032 a) What is dependent and independent variables? b) Fully write out the regression equation. c) Fill in the missing values **, ***', and *****. d) Hence test whether & is significant. Give reasons for your answer. e) Perform the F Test making sure to state the null and alternative hypothesis.
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
A multiple regression was estimated using the SEK method and the results are given below. Based on these estimates alone, multiple Explain the level of linearity, varying variance and cascading dependency problems. Bi: 4,1234 B2: -0,2189 B3: -1,7862 sh:3,1223 R?: 0,9820 R*: 0,9750 F: 10,89 F tablo:3,07 sh:4,4420 sh:0,3179 D.W.: 0,2558 d1:0,385 dy:1,685
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…
Perform a regression on two different variables within the attached data, and exaplin what the findings determine. What decision can be made?
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
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