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Add TP1 GLM & DM Monte Carlo
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@@ -23,16 +23,12 @@ summary(model)
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```{r}
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library(ggfortify)
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library(car)
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autoplot(model,1)
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autoplot(model,1:3)
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```
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The points are not well distributed around 0 -> [P1] is not verified
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```{r}
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autoplot(model,3)
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```
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The points are not well distributed around 1 -> [P2] is not verified
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The QQPlot is align with the line y = x, so it is globally gaussian -> [P4] is verified
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```{r}
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set.seed(0)
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@@ -41,12 +37,6 @@ durbinWatsonTest(model)
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The p-value is 0.58 > 0.05 -> We do not reject H0 so the residuals are not autocorrelated -> [P3] is verified
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```{r}
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autoplot(model,2)
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```
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The QQPlot is align with the line y = x, so it is globally gaussian -> [P4] is verified
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```{r}
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library(GGally)
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ggpairs(Health, progress = F)
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@@ -68,16 +58,12 @@ coefficients(model2)
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```{r}
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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autoplot(model2,1:4)
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```
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The points are well distributed around 0 -> [P1] is verified
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```{r}
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autoplot(model2,3)
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```
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The points are not well distributed around 1 -> [P2] is verified
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The QQPlot is align with the line y = x, so it is gaussian -> [P4] is verified
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```{r}
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set.seed(0)
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@@ -86,8 +72,3 @@ durbinWatsonTest(model2)
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The p-value is 0.294 > 0.05 -> We do not reject H0 so the residuals are not autocorrelated -> [P3] is verified
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```{r}
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autoplot(model2,2)
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```
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The QQPlot is align with the line y = x, so it is gaussian -> [P4] is verified
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