This new regression, on French Départements data confirms the intuition : both population and density of population are significant factors in explaining the COVID-29 outbreak in France.
NB. Regional data retained density only as significant independent variable, reflecting mostly the high degree of centralisation in France.
Notice that, for population, the slope of the least squares adjusted line is 0.00016163. It is expected to rise in the coming weeks.
This is an analysis based on public data, and subject to revisions or errors including the processing.
Data sources: Géodes, données en Santé Publique, INSEE.
Here, the analysis
Multiple Regression – COVID19 Cases in hospitals
Dependent variable: COVID19 Cases in hospitals
Independent variables:
Population
Density
Standard T
Parameter Estimate Error Statistic P-Value
CONSTANT -12.9895 20.4993 -0.633655 0.5278
Population 0.00016163 0.0000269478 5.99787 0.0000
Density 0.0469516 0.00570321 8.23248 0.0000
Analysis of Variance
Source Sum of Squares Df Mean Square F-Ratio P-Value
Model 2.75644E6 2 1.37822E6 93.87 0.0000
Residual 1.43885E6 98 14682.1
Total (Corr.) 4.19529E6 100
R-squared = 65.7032 percent
R-squared (adjusted for d.f.) = 65.0033 percent
Standard Error of Est. = 121.17
Mean absolute error = 71.1496
Durbin-Watson statistic = 1.20888 (P=0.0000)
Lag 1 residual autocorrelation = 0.392756
COVID19 Cases in hospitals = -12.9895 + 0.00016163*Population + 0.0469516*Density
The prediction plot
The components charts
The residual plot
The residual plot mainly reflects the incidence of départements from the “Grand Est”, région, (to some extend also the Rhône). In these areas the outbreak was earlier and linked to a specific origin.
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