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Table 4 Results of Linear Regression

From: Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich

 

Dependent variable: Pid

 

1:OLS

2: RLM

Intercept

48.74

53.37

 

(6.93)

(7.04)

fast-food

−2.56

−3.57

 

(3.75)

(3.81)

lockdown

3.66

−3.50

 

(9.82)

(9.98)

lockdown:fast-food

−19.47

−16.73

 

(5.31)

(5.39)

lockdown:monday

−16.45

−16.24

 

(1.52)

(1.55)

lockdown:average stop distance/1000

19.49

20.19

 

(8.93)

(9.07)

lockdown:number of reviews/1000

0.13

−0.97

 

(1.88)

(1.91)

lockdown:parking area/1000

0.18

0.13

 

(0.95)

(0.96)

lockdown:population/1000

−1.93

−1.93

 

(1.10)

(1.12)

lockdown:rating

−1.68

−0.10

 

(2.26)

(2.30)

lockdown:supermarket

4.53

4.84

 

(2.02)

(2.05)

monday

11.20

11.29

 

(1.08)

(1.09)

average stop distance/1000

−10.52

−11.40

 

(6.32)

(6.42)

number of reviews/1000

−1.52

−1.41

 

(1.33)

(1.35)

parking area/1000

0.67

0.64

 

(0.67)

(0.68)

population/1000

1.18

1.20

 

(0.78)

(0.79)

rating

−0.40

−1.41

 

(1.60)

(1.62)

supermarket

−1.96

−2.12

 

(1.43)

(1.45)

Observations

718

718

R2

0.34

 

Adjusted R2

0.32

 

Residual Std. Error

10.18

3.21

F Statistic

21.20

 
  1. Note: p<0.1; p<0.05; p<0.01