Using the R built-in data set called Chick Weight, we want to compare the mean weight...

Using the R built-in data set called Chick Weight, we want to compare the mean weight across the different types of Diet. IMPORTANT: We only want to compare chicks at the final value of Time, 21. In this problem, use ?? = 0.05.
1. Make a boxplot to compare weight across the different types of Diet. Based on the boxplot, describe any differences (or lack of differences) you see.
2. Run an ANOVA to compare weight across the different types of Diet. Is there a significant difference in means?
3. Regardless of your answer to part b, using Tukey’s HSD approach, which pair or pairs of Diet have significantly different means?
4. Check the assumptions of the ANOVA: Do the variances look similar across groups? And do the residuals look like they could have come from a Normal Distribution?

Answers should be in the form of R code on how to accomplish each part and include the correct statistical explanation for those that require it in the question. Please be as thorough as possible. Thank you so much!!!

Expert Solution

R-codes

head(ChickWeight) ## data entry

boxplot(weight~Diet,data=ChickWeight) ## boxplot
ChickWeight.aov=aov(weight~Diet,data=ChickWeight) ## fitting model
anova(ChickWeight.aov) ## anova table
TukeyHSD(ChickWeight.aov) ## pairwise comparison

bartlett.test(weight~Diet,data=ChickWeight) ## assumption of equality of variance

shapiro.test(ChickWeight.aov$res) ## checking normality of residuals
qqnorm(ChickWeight.aov$res)## checking normality of residuals
qqline(ChickWeight.aov$res)## checking normality of residuals

I hope I have made it clear, If you have any doubt please ask in comment section.

orchestra answered 1 year ago

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