ANOVA Test in R Programming

ANOVA also known as Analysis of variance is used to investigate relations between categorical variables and continuous variable in R Programming. It is a type of hypothesis testing for population variance. 

R – ANOVA Test

ANOVA test involves setting up: 

• Null Hypothesis: All population means are equal.
• Alternate Hypothesis: Atleast one population mean is different from other.

ANOVA tests are of two types: 

• One way ANOVA: It takes one categorical group into consideration.
• Two way ANOVA: It takes two categorical group into consideration.

The Dataset

The mtcars(motor trend car road test) dataset is used which consist of 32 car brands and 11 attributes. The dataset comes preinstalled in dplyr package in R. 

To get started with ANOVA, we need to install and load the dplyr package.

Performing One Way ANOVA test in R language

One way ANOVA test is performed using mtcars dataset which comes preinstalled with dplyr package between disp attribute, a continuous attribute and gear attribute, a categorical attribute.

R

# Installing the package
install.packages("dplyr")

# Loading the package
library(dplyr)

# Variance in mean within group and between group
boxplot(mtcars$disp~factor(mtcars$gear),
        xlab = "gear", ylab = "disp")

# Step 1: Setup Null Hypothesis and Alternate Hypothesis
# H0 = mu = mu01 = mu02(There is no difference
# between average displacement for different gear)
# H1 = Not all means are equal

# Step 2: Calculate test statistics using aov function
mtcars_aov <- aov(mtcars$disp~factor(mtcars$gear))
summary(mtcars_aov)

# Step 3: Calculate F-Critical Value
# For 0.05 Significant value, critical value = alpha = 0.05

# Step 4: Compare test statistics with F-Critical value
# and conclude test p < alpha, Reject Null Hypothesis

Output:

The box plot shows the mean values of gear with respect of displacement. Hear categorical variable is gear on which factor function is used and continuous variable is disp.

The summary shows that the gear attribute is very significant to displacement(Three stars denoting it). Also, the P value is less than 0.05, so proves that gear is significant to displacement i.e related to each other and we reject the Null Hypothesis.

Performing Two Way ANOVA test in R

Two-way ANOVA test is performed using mtcars dataset which comes preinstalled with dplyr package between disp attribute, a continuous attribute and gear attribute, a categorical attribute, am attribute, a categorical attribute.

R

# Installing the package
install.packages("dplyr")

# Loading the package
library(dplyr)

# Variance in mean within group and between group
boxplot(mtcars$disp~mtcars$gear, subset = (mtcars$am == 0),
        xlab = "gear", ylab = "disp", main = "Automatic")
boxplot(mtcars$disp~mtcars$gear, subset = (mtcars$am == 1),
            xlab = "gear", ylab = "disp", main = "Manual")

# Step 1: Setup Null Hypothesis and Alternate Hypothesis
# H0 = mu0 = mu01 = mu02(There is no difference between
# average displacement for different gear)
# H1 = Not all means are equal

# Step 2: Calculate test statistics using aov function
mtcars_aov2 <- aov(mtcars$disp~factor(mtcars$gear) *
                            factor(mtcars$am))
summary(mtcars_aov2)

# Step 3: Calculate F-Critical Value
# For 0.05 Significant value, critical value = alpha = 0.05

# Step 4: Compare test statistics with F-Critical value
# and conclude test p < alpha, Reject Null Hypothesis

Output:

The box plot shows the mean values of gear with respect to displacement. Hear categorical variables are gear and am on which factor function is used and continuous variable is disp.

The summary shows that the gear attribute is very significant to displacement(Three stars denoting it) and am attribute is not much significant to displacement. P-value of gear is less than 0.05, so it proves that gear is significant to displacement i.e related to each other. P-value of am is greater than 0.05, am is not significant to displacement i.e not related to each other.

Results

We see significant results from boxplots and summaries. 

• Displacement is strongly related to Gears in cars i.e displacement is dependent on gears with p < 0.05.
• Displacement is strongly related to Gears but not related to transmission mode in cars with p 0.05 with am.