the following regression table where the dependent variable is the demand for massage services in one 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?

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the following regression table where the dependent variable is the demand
for massage services in one 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?

Model 1 Predicting
Hourly Demand
Model 2 Predicting
Hourly Demand
Model 3 Predicting
Daily Demand
Model 4 Predicting
Daily Demand
Population/1,000
Percent Unoccupied Housing
Percent Renters
Number of Reviews
0.03 (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)**
-0.64 (0.28)**
-0.87 (0.25)***
0.37 (0.37)
-0.28 (0.17)*
-0.04 (0.23)*
0.17 (0.23)
-1.06 (0.25)***
-1.02 (0.24)***
0.62 (0.34)*
0.78 (0.33)**
-0.04 (0.19)**
0.12 (0.15)
-0.52 (0.16)***
-7.80 (3.29)**
-10.53 (2.93)***
3.15 (4.27)
-3.48 (2.05)*
-0.52 (2.74)*
2.38 (2.74)
-12.92 (2.89)***
-12.30 (2.81)***
7.28 (4.12)*
9.32 (3.92)**
-0.58 (2.26)***
1.68 (1.87)
-6.30 (1.98)***
1
Star Percent
Cash Only
Total Cost/10
Worker Diversity
Number of Reviews*Cash Only
Hour
-0.08 (0.14)
-0.33 (0.14)**
-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)***
-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)***
2
4
7
10
11
12
Constant
29.89 (9.41)***
26.17 (7.52)***
N
384
384
384
384
R2
34
.37
48
.60
Note. Coefficients from ordinary least squares regressions are reported. Robust standard errors are in parentheses.
***p < .01. **p s .05. *p < .10.
Transcribed Image Text:Model 1 Predicting Hourly Demand Model 2 Predicting Hourly Demand Model 3 Predicting Daily Demand Model 4 Predicting Daily Demand Population/1,000 Percent Unoccupied Housing Percent Renters Number of Reviews 0.03 (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)** -0.64 (0.28)** -0.87 (0.25)*** 0.37 (0.37) -0.28 (0.17)* -0.04 (0.23)* 0.17 (0.23) -1.06 (0.25)*** -1.02 (0.24)*** 0.62 (0.34)* 0.78 (0.33)** -0.04 (0.19)** 0.12 (0.15) -0.52 (0.16)*** -7.80 (3.29)** -10.53 (2.93)*** 3.15 (4.27) -3.48 (2.05)* -0.52 (2.74)* 2.38 (2.74) -12.92 (2.89)*** -12.30 (2.81)*** 7.28 (4.12)* 9.32 (3.92)** -0.58 (2.26)*** 1.68 (1.87) -6.30 (1.98)*** 1 Star Percent Cash Only Total Cost/10 Worker Diversity Number of Reviews*Cash Only Hour -0.08 (0.14) -0.33 (0.14)** -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)*** -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)*** 2 4 7 10 11 12 Constant 29.89 (9.41)*** 26.17 (7.52)*** N 384 384 384 384 R2 34 .37 48 .60 Note. Coefficients from ordinary least squares regressions are reported. Robust standard errors are in parentheses. ***p < .01. **p s .05. *p < .10.
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