ANOVA Mot Nhan To

1. Doc du lieu

printer_data <- read.csv("data.csv")
new_DF <- printer_data[, c("layer_height", "wall_thickness",
                           "infill_density", "infill_pattern", "nozzle_temperature", "bed_temperature",
                           "print_speed", "material", "fan_speed", "tension_strenght")]

2. Phan nhom

new_DF$nozzle_temperature_2 <- ifelse(new_DF$nozzle_temperature < 220,
                                      "Group_1",
                                      ifelse(new_DF$nozzle_temperature > 230,
                                             "Group_3", "Group_2"))
Group_1 <- subset(new_DF, new_DF$nozzle_temperature_2 == "Group_1")
Group_2 <- subset(new_DF, new_DF$nozzle_temperature_2 == "Group_2")
Group_3 <- subset(new_DF, new_DF$nozzle_temperature_2 == "Group_3")

3. Kiem dinh phan phoi chuan

par(mfrow = c(1, 3))
qqnorm(Group_1$tension_strenght, main = "QQ-Plot Group 1")
qqline(Group_1$tension_strenght)
qqnorm(Group_2$tension_strenght, main = "QQ-Plot Group 2")
qqline(Group_2$tension_strenght)
qqnorm(Group_3$tension_strenght, main = "QQ-Plot Group 3")
qqline(Group_3$tension_strenght)

par(mfrow = c(1, 1))
shapiro.test(Group_1$tension_strenght)

## 
##  Shapiro-Wilk normality test
## 
## data:  Group_1$tension_strenght
## W = 0.91815, p-value = 0.09128

shapiro.test(Group_2$tension_strenght)

## 
##  Shapiro-Wilk normality test
## 
## data:  Group_2$tension_strenght
## W = 0.95684, p-value = 0.4829

shapiro.test(Group_3$tension_strenght)

## 
##  Shapiro-Wilk normality test
## 
## data:  Group_3$tension_strenght
## W = 0.87204, p-value = 0.1056

4. Kiem dinh dong nhat phuong sai

library(car)
leveneTest(tension_strenght ~ as.factor(nozzle_temperature_2), data = new_DF)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  2  0.9575 0.3912
##       47

5. Phan tich phuong sai

ANOVA_model <- aov(tension_strenght ~ as.factor(nozzle_temperature_2), data = new_DF)
summary(ANOVA_model)

##                                 Df Sum Sq Mean Sq F value Pr(>F)  
## as.factor(nozzle_temperature_2)  2    688   343.9   5.026 0.0105 *
## Residuals                       47   3216    68.4                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

6. So sanh boi

TukeyHSD(ANOVA_model)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = tension_strenght ~ as.factor(nozzle_temperature_2), data = new_DF)
## 
## $`as.factor(nozzle_temperature_2)`
##                   diff        lwr        upr     p adj
## Group_2-Group_1  -3.10  -9.430522  3.2305221 0.4678313
## Group_3-Group_1 -10.15 -17.903275 -2.3967255 0.0074607
## Group_3-Group_2  -7.05 -14.803275  0.7032745 0.0814970

plot(TukeyHSD(ANOVA_model))