We can formulate ANOVA as [[base/R/linear regression]] in [[R]] to simplify interpretation, use statistical inference to test hypotheses. A low [[p-value]] for the[[ F-statistic ]]indicates evidence of differences between groups. R will drop one factor level, selecting the first alphanumerically to drop. The dropped level is referred to as the baseline group. To relevel, use `relevel`. The intercept is the expected response in the baseline group. $\hat \mu_1 = \hat \beta_0$ The slope of each parameter is the expected change in the mean of the response between the baseline group to the $j^{th}$ group (for $j = 2, \dots, J$). $\hat \mu_{j} = \hat \beta_0 + \hat \beta_j$ Mutate the dataset to create factors from categorical variables if needed and label them for ease of use. ```R df <- read.csv("path/to/data") df <- df %>% mutate(factor_col = as.factor(factor_col)) levels(df$factor_col) <- c("A", "B")) ```