I need to run a one way ANOVA on some data (water_content) from 3 treatment groups (Water), but am struggling to interpret whether I need to transformation the data to make it appropriate for my lm() models. The outputs from the plots of my lm() model are included below:
The code I used was:
water_content_model <- lm(water_content~ Water, data=df)
If I was eyeballing the Normal Q-Q plot I would say it was not normally distributed, however when I performed the Shapiro-Wilk normality test it gave a non-significant p-value of 0.2169, indicating that the data is normally distributed. I also did the Shapiro-Wilk test on data that looked more normally distributed than this to me, but got a significant p-value indicating that it was not normally distributed. I am interpreting the graph and test results correctly?
If it is non-normally distributed, how do I correct for this before running the anova model?
The data is also non-orthogonal as there are a different number of samples from each treatment group so I have additionally run a Type III ANOVA as I read that this was appropriate for this: water_content_aov <- aov(water_content~ Water, data= df) Anova(water_content_aov, type = "III") Is this appropriate for this data?