Answer Happy

40 35 Activations 30 25 20 0 B 10 Ext Linear Ft Linear Fit Activations 20.696944 +1.65563 Summary of Fit RSquare 0.395166 RSquare Adj 0.368869 Root Mean Square Error 5.077984 Mean of Response 29.24 Observations for Sum Wats) 25 Analysis of Variance Sum of Source DF SquaresMean Square F Ratio Model 1 3B7 48388 387 4B4 15 0270 Error 23 593 07612 25.786 Prob > F C Total 24 980.56000 0.0008 Parameter Estimates Term EstimateStd Errort Ratio Prob>|t| Interce 696944 2 426581 8.53 <0001 Exp 1 655631 0.427098 3.88 0.0008 1. State the coefficient of determination. How is it interpreted? 2. Interpret the coefficient of slope in context of the problem. 3. Calculate the 95% confidence interval for the slope of the regression line. Interpret it in context of the problem. 4. Conduct the hypothesis test to see if the number of cellphones activated increases with the increase in the experience of the salesperson (a) State the hypotheses.
(b) Calculate the test statistic and the p-value. (c) What is the conclusion of the test? Use 5% level of significance 5. Calculate the strength of the linear relationship between the phones activated and the experience in years. Recall the hotdog demand example discussed in the semester (Sample P53). I would like to estimate the number of hotdogs sold on a day using the price of each hotdog and the day (weekday or weekend) as the predictors. Analysis of Variance Source DF Sum of Mean F Ratio Squares Square Model 14831.574 7415.79 2 Error 21 7581.384 361.02 Prob F C. Total 23 22412.958 Indicator Function Parameterization Term Estimate Std Error Ratio Prob> Intercept 177.18033 25.34046 6.99 <.0001 Price -85.92213 19.23268 -4.47 0.0002 Day Weekdayl 29.283372 3.015996 3.65 0.0015 6. Which category is used as a reference category? a 7. State the regression model. Test if this model is significant. Use 5% level of significance. 8. State the hypotheses. 9. Calculate the test statistic and the p-value. 10. What is the conclusion of the test? Use 5% level of significance. 11. State the regression equation for weekday. 12. Estimate the number of hotdogs sold if the price of each hotdog is $0.80 and the day is a weekday. 13. Calculate the 99% confidence interval for the coefficient of price. Interpret it in context. 14. Recall the hotdog demand example discussed in the semester (Sample PS2). I would like to estimate the number of hotdogs sold on a day using the price of each hotdog, the day (weekday or weekend), and the interaction term (price day) as the predictors. Indicator Function Parameterization Analysis of Variance Term Estimate Std Errort Ratio Prob|t| Sum of Source Intercept 220.25532 39.183 5.62 <.0001 DF Square Mean Square F Ratio -119.5745 30.26639 Price -3.95 0.0008 Model 315524 229 5174 74 15 0238 Duy Vladiday -39.09818 48.8524 -0.80 0.4329 Em 344 44 Prob > F 20 6888 730 Price"Day Weekday 54.741135 38.60201 142 0.1716 C. Total 23 22412958 <0001 (a) State the multiple linear regression model. (b) Test if the model is significant. Use 5% level of significance. (c) Estimate the number of hotdogs sold if the price of each hotdog is $0.80 and the day is a weekday.
A real estate investor wants to study the relationship between annual return on his commercial retail shops as it relates to their location and the number of homes near the shops. Specifically, the investor has collected data on the annual return of the shops (measured in thousands of dollars), the number of households within 15 miles of the shops (measured in thousands), and the location of the shops (whether the shops are in a suburban area, near a shopping mall, or downtown). 15. Provide the detailed interpretations of b, b, and b, in the context of the problem. 16. Use your estimated regression equation to predict the annual return for a shop in downtown with 122,000 households near the shop. 17. Compute the 95% c.i. for the coefficient of number of households. Annual Return (Thousands of Number of Households... Summary of Fit Location 105.60 162 Mall RSquare 0.99995 203 215 Suburbon RSquare Ad) 0999944 245.31 232 Mall Root Mean Square Error 0.288707 137.07 100 Mall Mean ot Response 189.601 207.30 220 Suburban Observations or Sum Wots) 30 140.12 150 Suburban 111.21 102 Downtown Analysis of Variance 10 108 Suburban Humor 152.23 149 Downtown Source DF Square Mean Square F Ratio Modol 192 Suburban 141221 Error 20 21 CO Prob let 04:10 0001 Indicator Function Parameterization Term EstimateStd Errort Ratio Prob>Lower 95%pper 95% Intercept 15 5070740.261038 59.41 < 0001 14970504 16.043644 Number of Households (Thousa204419 0.001225 710 56 < 0001 0.8679239 0 87296 Location Downtown) 6.71854240 132232 50 81 <0001 6.4467351 6.9903498 Location Mall 27 818570.138874 200.32 < 0001 27.53311 28.104029 Note: When we say number of households is 1, that means 1,000. Similarly, when we say the annual return is $1, that means $1,000.