If a regression model of the form y = Bo+B,x¡ +... +B4*4 is fit to 104 observations on each variable and yields an R'value of 0.6, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Degrees of freedom Sums of Source of variation Mean F statistic squares squares Regression Error 9 Total

Use the following ANOVA table for regression to answer the questions. Response: Y Source DF Sum Sq Mean Sq F-value Pr(>F) Regression 1 10.964 10.964 3.02 0.083 Residual Error 342 1241.460 3.630     Total 343 1252.424           Give the F-statistic and p-value.Enter the exact answers.The F-statistic is Enter your answer; F-statistic .The p-value is Enter your answer; p-value .     Choose the conclusion of this test using a 5% significance level. Reject H0. The model is not effective. Do not reject H0. We did not find evidence that the model is not effective. Reject H0. The model is effective. Do not reject H0. We did not find evidence that the model is effective.

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

If a regression model of the form y= B,+B,x,+... + B,x, is fit to 132 observations on each variable and yields an R´value of 0.87, fill in the blanks in the following ANOVA table. Do all calculations to at least three decimal places. Source of Degrees of freedom Sums of Mean F statistic variation squares squares Regression 69 Error Total

The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7   Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample?   (b) Write the estimated regression equation. ŷ =        (c) What is the value of  sb1?   Find the value of the test statistic. (Round your answer to two decimal places.)   Find the p-value. (Round your answer to three decimal places.) p-value =

Use the following ANOVA table for regression to answer the questions. Response: Y Source DF Sum Sq Mean Sq F-value Pr(>F) Regression 1 351.47 351.47 13.32 0.000 Residual Error 359 9474.01 26.39     Total 360 9825.48                 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is .The p-value is .