Look at the following regression table where the dependent variable is the demand for illegal massage services in a city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4).(a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4?(b) Can you reject H0 in Model 1 if H0 : βP opulation/1,000 = 0.01, H1 : βPopulation/1,000 6= 0.01, and α = 0.01? Model 1 PredictingHourly DemandModel 2 PredictingHourly DemandModel 3 PredictingDaily DemandModel 4 PredictingDaily DemandPopulation/1,000Percent Unoccupied Housing0.03 (0.01)**-0.04 (0.02)*-0.01 (0.00)**0.04 (0.01)*-0.05 (0.01)***-0.01 (0.00)**0.43 (0.13)***-0.55 (0.23)**-0.22 (0.20)****0.53 (0.13)*-0.67 (0.21)*-0.18 (0.08)**\*****Percent RentersNumber of Reviews-1.06 (0.25)***-1.02 (0.24)***0.62 (0.34)*0.78 (0.33)*-7.80 (3.29)**-10.53 (2.93)***3.15 (4.27)-3.48 (2.05)*-0.52 (2.74)*2.38 (2.74)-0.64 (0.28)*-12.92 (2.89)*\***-0.87 (0.25)***0.37 (0.37)-0.28 (0.17)*-0.04 (0.23)*0.17 (0.23)-12.30 (2.81)***7.28 (4.12)*9.32 (3.92)*-0.58 (2.26)***1.68 (1.87)-6.30 (1.98)**2Star PercentCash OnlyTotal Cost/10Worker DiversityNumber of Reviews*Cash Only-0.04 (0.19)**0.12 (0.15)-0.52 (0.16)****Hour-0.08 (0.14)-0.33 (0.14)**-0.08 (0.14)-0.33 (0.14)**-0.37 (0.15)**-0.30 (0.17)*0.56 (0.26)*1.44 (0.27)***1.22 (0.24)***1.03 (0.28)***1.06 (0.29)*0.69 (0.26)***0.44 (0.14)***1.71 (0.62)*23-0.36 (0.15)**-0.30 (0.17)*0.57 (0.26)*1.44 (0.27)***1.22 (0.25)***1.03 (0.28)***1.07 (0.29)***0.69 (0.26)***0.44 (0.14)***2.02 (0.79)***38445*789.10***111226.17 (7.52)***384Constant29.89 (9.41)******N384384R2.34.3748.60Note. Coefficients from ordinary least squares regressions are reported. Robust standard errors are in parentheses.*p < .01. **p < .05. *p < .10.***

Introduction: The regression coefficient depends on the change of scale of the dependent, as well as…

Look at the following regression table where the dependent variable is the demand for illegal massage services in a city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4). (a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4? (b) Can you reject H0 in Model 1 if H0 : βP opulation/1,000 = 0.01, H1 : βPopulation/1,000 6= 0.01, and α = 0.01?

Look at the following regression table where the dependent variable is the demand for illegal massage services in a city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4). (a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4? (b) Can you reject H0 in Model 1 if H0 : βP opulation/1,000 = 0.01, H1 : βPopulation/1,000 6= 0.01, and α = 0.01?

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter6: Applications Of The Derivative

Section6.CR: Chapter 6 Review

Problem 33CR

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