2152280
BÀI TẬP ANOVA
01-ANTIFUNGAL DATASET
## # A tibble: 6 × 4
## No Temperature Carbon Y
## <dbl> <fct> <fct> <dbl>
## 1 1 25 2 25.8
## 2 2 30 2 51.9
## 3 3 37 2 41.1
## 4 4 25 5 20.5
## 5 5 30 5 41.1
## 6 6 37 5 32.6
## Df Sum Sq Mean Sq F value Pr(>F)
## Temperature 2 8147 4073 5.155 0.017 *
## Carbon 2 1073 536 0.679 0.520
## Temperature:Carbon 4 126 32 0.040 0.997
## Residuals 18 14224 790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Shapiro-Wilk normality test
##
## data: residuals(antifungal_anova_model)
## W = 0.91751, p-value = 0.0344## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 8 0.4664 0.8639
## 18## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Temperature * Carbon, data = antifungal_df)
##
## $Temperature
## diff lwr upr p adj
## 30-25 41.76889 7.948276 75.58950 0.0145052
## 37-25 27.90667 -5.913946 61.72728 0.1167143
## 37-30 -13.86222 -47.682835 19.95839 0.5584470
##
## $Carbon
## diff lwr upr p adj
## 5-2 -14.937778 -48.75839 18.88283 0.5102518
## 7.5-2 -10.853333 -44.67395 22.96728 0.6963576
## 7.5-5 4.084444 -29.73617 37.90506 0.9491272
##
## $`Temperature:Carbon`
## diff lwr upr p adj
## 30:2-25:2 3.800333e+01 -42.41974 118.42641 0.7637706
## 37:2-25:2 2.250667e+01 -57.91641 102.92974 0.9831308
## 25:5-25:2 -1.703333e+01 -97.45641 63.38974 0.9972300
## 30:5-25:2 2.250667e+01 -57.91641 102.92974 0.9831308
## 37:5-25:2 1.022333e+01 -70.19974 90.64641 0.9999313
## 25:7.5-25:2 -1.792333e+01 -98.34641 62.49974 0.9960892
## 30:7.5-25:2 2.984000e+01 -50.58307 110.26307 0.9187441
## 37:7.5-25:2 1.603333e+01 -64.38974 96.45641 0.9981742
## 37:2-30:2 -1.549667e+01 -95.91974 64.92641 0.9985605
## 25:5-30:2 -5.503667e+01 -135.45974 25.38641 0.3411162
## 30:5-30:2 -1.549667e+01 -95.91974 64.92641 0.9985605
## 37:5-30:2 -2.778000e+01 -108.20307 52.64307 0.9438076
## 25:7.5-30:2 -5.592667e+01 -136.34974 24.49641 0.3226330
## 30:7.5-30:2 -8.163333e+00 -88.58641 72.25974 0.9999877
## 37:7.5-30:2 -2.197000e+01 -102.39307 58.45307 0.9854497
## 25:5-37:2 -3.954000e+01 -119.96307 40.88307 0.7266250
## 30:5-37:2 -2.842171e-14 -80.42307 80.42307 1.0000000
## 37:5-37:2 -1.228333e+01 -92.70641 68.13974 0.9997298
## 25:7.5-37:2 -4.043000e+01 -120.85307 39.99307 0.7043767
## 30:7.5-37:2 7.333333e+00 -73.08974 87.75641 0.9999946
## 37:7.5-37:2 -6.473333e+00 -86.89641 73.94974 0.9999980
## 30:5-25:5 3.954000e+01 -40.88307 119.96307 0.7266250
## 37:5-25:5 2.725667e+01 -53.16641 107.67974 0.9492429
## 25:7.5-25:5 -8.900000e-01 -81.31307 79.53307 1.0000000
## 30:7.5-25:5 4.687333e+01 -33.54974 127.29641 0.5364661
## 37:7.5-25:5 3.306667e+01 -47.35641 113.48974 0.8675409
## 37:5-30:5 -1.228333e+01 -92.70641 68.13974 0.9997298
## 25:7.5-30:5 -4.043000e+01 -120.85307 39.99307 0.7043767
## 30:7.5-30:5 7.333333e+00 -73.08974 87.75641 0.9999946
## 37:7.5-30:5 -6.473333e+00 -86.89641 73.94974 0.9999980
## 25:7.5-37:5 -2.814667e+01 -108.56974 52.27641 0.9397777
## 30:7.5-37:5 1.961667e+01 -60.80641 100.03974 0.9929001
## 37:7.5-37:5 5.810000e+00 -74.61307 86.23307 0.9999991
## 30:7.5-25:7.5 4.776333e+01 -32.65974 128.18641 0.5134518
## 37:7.5-25:7.5 3.395667e+01 -46.46641 114.37974 0.8509353
## 37:7.5-30:7.5 -1.380667e+01 -94.22974 66.61641 0.999366702-IRIS DATASET
## # A tibble: 6 × 5
## S.Length S.Width P.Length P.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 5.1 3.5 1.4 0.2 I.setosa
## 2 4.9 3 1.4 0.2 I.setosa
## 3 4.7 3.2 1.3 0.2 I.setosa
## 4 4.6 3.1 1.5 0.2 I.setosa
## 5 5 3.6 1.4 0.2 I.setosa
## 6 5.4 3.9 1.7 0.4 I.setosa
## Df Sum Sq Mean Sq F value Pr(>F)
## Species 2 63.21 31.606 119.3 <2e-16 ***
## Residuals 147 38.96 0.265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Shapiro-Wilk normality test
##
## data: residuals(iris_anova_model)
## W = 0.9879, p-value = 0.2189## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 6.3527 0.002259 **
## 147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = S.Length ~ Species, data = iris_df)
##
## $Species
## diff lwr upr p adj
## I.versicolor-I.setosa 0.930 0.6862273 1.1737727 0
## I.virginica-I.setosa 1.582 1.3382273 1.8257727 0
## I.virginica-I.versicolor 0.652 0.4082273 0.8957727 003-SURFACE_EXP DATASET
## # A tibble: 6 × 11
## paint dry_time result ...4 ...5 ...6 ...7 ...8 ...9 ...10 ...11
## <dbl> <fct> <dbl> <lgl> <lgl> <lgl> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 1 20 74 NA NA NA NA NA NA NA NA
## 2 1 20 64 NA NA NA NA NA 20 25 30
## 3 1 20 50 NA NA NA NA 1 74 73 78
## 4 1 25 73 NA NA NA NA NA 64 61 85
## 5 1 25 61 NA NA NA NA NA 50 44 92
## 6 1 25 44 NA NA NA NA 2 92 98 66
## Df Sum Sq Mean Sq F value Pr(>F)
## dry_time 2 27.4 13.7 0.073 0.930
## paint 1 355.6 355.6 1.902 0.193
## dry_time:paint 2 1878.8 939.4 5.026 0.026 *
## Residuals 12 2242.7 186.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Shapiro-Wilk normality test
##
## data: residuals(surface_exp_anova_model)
## W = 0.95127, p-value = 0.4452## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = result ~ dry_time * paint, data = surface_exp_df)
##
## $dry_time
## diff lwr upr p adj
## 25-20 0.500000 -20.55691 21.55691 0.9977903
## 30-20 2.833333 -18.22358 23.89025 0.9318277
## 30-25 2.333333 -18.72358 23.39025 0.953151604-WEIGHTLIFTING DATASET
## # A tibble: 6 × 5
## Age Body Snatch Clean Total
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 26 163 210 262. 472.
## 2 30 141. 205 250 455
## 3 22 161. 208. 240 448.
## 4 27 118. 200 240 440
## 5 23 125. 195 242. 438.
## 6 31 140. 190 240 430
##
## Call:
## lm(formula = Total ~ Body + Age, data = weightlifting_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.937 -21.925 1.228 22.260 36.889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 237.3660 98.3805 2.413 0.0344 *
## Body 0.8120 0.6339 1.281 0.2266
## Age 2.6810 1.9193 1.397 0.1900
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.65 on 11 degrees of freedom
## Multiple R-squared: 0.262, Adjusted R-squared: 0.1278
## F-statistic: 1.952 on 2 and 11 DF, p-value: 0.1881
##
## Shapiro-Wilk normality test
##
## data: residuals(weightlifting_lm)
## W = 0.92897, p-value = 0.2952## Analysis of Variance Table
##
## Response: Total
## Df Sum Sq Mean Sq F value Pr(>F)
## Body 1 1956.6 1956.6 1.9531 0.1898
## Age 1 1954.8 1954.8 1.9513 0.1900
## Residuals 11 11019.5 1001.8