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Console Editeur : myProg.R 1 > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16 > 1 2 3 4 5 6 dn <- read.csv2("EPO2007-Fraude-BasePropre V03.csv") summary(lm(dn$Taille~dn$Sexe+dn$Age))

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16

Résultat Tableau > summary(lm(dn$Taille~dn$Sexe+dn$Age)) Call: lm(formula = dn$Taille ~ dn$Sexe + dn$Age) Residuals: Min 1Q Median 3Q Max -15.4687 -4.1153 -0.1071 3.9136 14.6960 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 159.1877 4.3807 36.339 <2e-16 *** dn$SexeHomme 9.8116 0.7082 13.855 <2e-16 *** dn$Age 0.2812 0.2107 1.334 0.183 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.751 on 273 degrees of freedom (10 observations deleted due to missingness) Multiple R-squared: 0.4271, Adjusted R-squared: 0.4229 F-statistic: 101.8 on 2 and 273 DF, p-value: < 2.2e-16