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Hi I am new to R programming, and have begun learning the different types of statistical tests that can be run on a dataset.

I am trying to analyze a set of data with both numerical and categorical variables, and determine which statistical tests would be best to run.

I am looking to compare the body length (BL) between two species of birds (sp) for each sex (sex).

I have selected the variables I want and organized into a data frame and calculated the average body length by species and sex and organized it into a data table below.

But am a bit lost on which tests I can use or would be best to use (t-test, chi, fisher, ANOVA, etc.) considering I have 2 categorical variables and not just 1 like a straightforward t-test.

Any advice for a newbie getting started?

bird.df<- select(birdData, BL, sex, sp)
bird.df


bird.dt <- bird.df %>%
   group_by(sp,sex) %>%
   summarize(mean_bl = mean(BL)) %>%
   filter(mean_bl == min(mean_bl) | mean_bl == max(mean_bl))
bird.dt
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  • $\begingroup$ Please make a more descriptive title $\endgroup$
    – kjetil b halvorsen ♦
    Commented Oct 7, 2022 at 0:57
  • $\begingroup$ What do you want to test? $\endgroup$
    – Dave
    Commented Oct 7, 2022 at 3:59

1 Answer 1

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If your response is body length (BL) and your predictors sex and species (sp), it seems that a two-way ANOVA might be adequate. You need to make some assumptions, like common variance, independence of observations and normality (although ANOVA is fairly robust to lack of normality).

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