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Table 3 Regression analysis

From: The median age of a city’s residents and population density influence COVID 19 mortality growth rates: policy implications

Variables

(1)

(2)

(3)

\(\Delta \mathrm{ln}(Cum\_Deaths)\)

\(\Delta \mathrm{ln}(Cum\_Deaths)\)

\(\Delta \mathrm{ln}(Cum\_Deaths)\)

Constant

− 0.00150***

− 0.00193***

− 0.00214***

(8.43 × 10–6)

(7.53 × 10–8)

(1.55 × 10–9)

MedianAge

8.81 × 10–5***

8.50 × 10–5***

7.95 × 10–5***

(< 0.01)

(< 0.01)

(7.67 × 10–11)

PopulationSize × t

5.08 × 10–11***

4.22 × 10–11***

5.19 × 10–11***

(< 0.01)

(< 0.01)

(< 0.01)

Dum_vaccine × .PopulationSize × t

− 7.32 × 10–12***

− 1.02 × 10–11***

− 4.37 × 10–12***

(7.50 × 10–7)

(2.06 × 10–10)

(0.00679)

Population_Density

1.82 × 10–7***

1.63 × 10–7***

(< 0.01)

(< 0.01)

Lockdowns

0.00197***

(< 0.01)

Holidays

− 4.14 × 10–5

(0.642)

Observations

71,580

63,555

63,555

R-squared between estimators

0.318

0.3471

0.4736

Calculated F-value for the regression significance

900.92***

633.38***

557.88***

Number of CityCode

173

152

152

  1. Estimation outcomes are based on the empirical model given by Eq. (2). The R-Squared between estimators gives the goodness of fit for the general equation \({\overline{y} }_{i}=\alpha +{\overline{x} }_{i}\beta +{\nu }_{i}+{\overline{\varepsilon }}_{i}\) where \({\overline{y} }_{i}={\sum }_{t}{y}_{it}/{T}_{i}\); \({\overline{x} }_{i}={\sum }_{t}{x}_{it}/{T}_{i}\); \({\overline{\epsilon }}_{i}={\sum }_{t}{\epsilon }_{it}/{T}_{i}\) (the sample mean of cities across time) and \({\nu }_{i}\) reflect generic differences across cities. p-values are given in parentheses
  2. ***p < 0.01