Multiple Regression and Binomial Regression Sample, R

Multiple Regression and Binomial Regression Sample

Language: Layout:

[ + ] Show input
Absolute running time: 3.74 sec, cpu time: 3.74 sec, memory peak: 128 Mb, absolute service time: 3,82 sec

Error(s), warning(s):

The following object is masked from package:ggplot2:

    mpg

Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

The following objects are masked from mtcars (pos = 7):

    am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt

The following object is masked from package:ggplot2:

    mpg

'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa Call: lm(formula = Petal.Length ~ Sepal.Length) Residuals: Min 1Q Median 3Q Max -2.47747 -0.59072 -0.00668 0.60484 2.49512 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.10144 0.50666 -14.02 <2e-16 *** Sepal.Length 1.85843 0.08586 21.65 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8678 on 148 degrees of freedom Multiple R-squared: 0.76, Adjusted R-squared: 0.7583 F-statistic: 468.6 on 1 and 148 DF, p-value: < 2.2e-16 Call: lm(formula = Petal.Length ~ ., data = iris) Residuals: Min 1Q Median 3Q Max -0.78396 -0.15708 0.00193 0.14730 0.65418 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.11099 0.26987 -4.117 6.45e-05 *** Sepal.Length 0.60801 0.05024 12.101 < 2e-16 *** Sepal.Width -0.18052 0.08036 -2.246 0.0262 * Petal.Width 0.60222 0.12144 4.959 1.97e-06 *** Speciesversicolor 1.46337 0.17345 8.437 3.14e-14 *** Speciesvirginica 1.97422 0.24480 8.065 2.60e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2627 on 144 degrees of freedom Multiple R-squared: 0.9786, Adjusted R-squared: 0.9778 F-statistic: 1317 on 5 and 144 DF, p-value: < 2.2e-16 Call: lm(formula = Petal.Length ~ . - Species, data = iris) Residuals: Min 1Q Median 3Q Max -0.99333 -0.17656 -0.01004 0.18558 1.06909 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.26271 0.29741 -0.883 0.379 Sepal.Length 0.72914 0.05832 12.502 <2e-16 *** Sepal.Width -0.64601 0.06850 -9.431 <2e-16 *** Petal.Width 1.44679 0.06761 21.399 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.319 on 146 degrees of freedom Multiple R-squared: 0.968, Adjusted R-squared: 0.9674 F-statistic: 1473 on 3 and 146 DF, p-value: < 2.2e-16 'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ... Call: glm(formula = am ~ wt, family = binomial(link = "logit")) Deviance Residuals: Min 1Q Median 3Q Max -2.11400 -0.53738 -0.08811 0.26055 2.19931 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 12.040 4.510 2.670 0.00759 ** wt -4.024 1.436 -2.801 0.00509 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 43.230 on 31 degrees of freedom Residual deviance: 19.176 on 30 degrees of freedom AIC: 23.176 Number of Fisher Scoring iterations: 6 1 0.6842243 'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ... [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" [11] "carb" mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 Shapiro-Wilk normality test data: Mod$residuals W = 0.9373, p-value = 0.0628 Non-constant Variance Score Test Variance formula: ~ fitted.values Chisquare = 2.592692 Df = 1 p = 0.1073577 Durbin-Watson test data: Mod DW = 1.636, p-value = 0.09084 alternative hypothesis: true autocorrelation is greater than 0