Do Schools have an effect on the evolution of the pandemic in Catalonia?
The test…
The test…
Robust Gamma Rank Correlation:
data: dependent and independent (length = 254)
similarity: linear
rx = 574.95 / ry = 536.7214
t-norm: min
alternative hypothesis: true gamma is not equal to 0
sample gamma = 0.6353018
estimated p-value = < 2.2e-16 (0 of 1000 values)The test…
Response: dependent
Input: independent
Number of inputs: 1
Model: y ~ X + 1
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Engle-Granger Cointegration Test
alternative: cointegrated
Type 1: no trend
lag EG p.value
5.00 -10.65 0.01
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Type 2: linear trend
lag EG p.value
5.000 -0.535 0.100
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Type 3: quadratic trend
lag EG p.value
5.000 0.807 0.100
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Note: p.value = 0.01 means p.value <= 0.01
: p.value = 0.10 means p.value >= 0.10 lag EG p.value
Min. :5 Min. :-10.6549 Min. :0.010
1st Qu.:5 1st Qu.: -5.5951 1st Qu.:0.055
Median :5 Median : -0.5353 Median :0.100
Mean :5 Mean : -3.4610 Mean :0.070
3rd Qu.:5 3rd Qu.: 0.1360 3rd Qu.:0.100
Max. :5 Max. : 0.8073 Max. :0.100 The Chow test, proposed by econometrician Gregory Chow in 1960, is a test of whether the true coefficients in two linear regressions on different data sets are equal. In econometrics, it is most commonly used in time series analysis to test for the presence of a structural break at a period which can be assumed to be known a priori (for instance, a major historical event such as a war). In program evaluation, the Chow test is often used to determine whether the independent variables have different impacts on different subgroups of the population. Source: https://en.wikipedia.org/wiki/Chow_test
We plot all the p.values for the Chow test to find the periods where the trends between the 10-19 serie and the 40-49 serie differs, when the p.value is over the critical value of 0.05 implies that the series moves along.