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.

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

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

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…

A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls is

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?

Analysis of Variance Source DF SS MS Regression 1 Residual Error 13 0.2364 Total 14 11.3240 What is the value for MSR (Mean Square for Regression)?

The data are the ages of criminals and their victims. The regression output is shown in a separate tab from the data. Do the data support that a prediction of victim age can be obtained given the data provided in the file? Cite the elements of the output you used to draw your conclusion.