R Programming


Always state your null and alternative hypotheses before reporting tests of significance.

Include your R codes or SAS codes in an appendix of your report

Be selective in the outputs you report. Include only the most relevant outputs and the ones you want to interpret [e.g., ANOVA tables, Confidence intervals, P-values, etc.]. Sometimes, SAS gives you a very long output. Also avoid copy-pasting ERROR messages from R console.

Always, interpret your results!


Question I: Exercise #7 (Data in Table 6.33)

a) Calculate the descriptive statistics by treatment (mean, standard deviation, number of observations, boxplot).

b) Is the design balanced or unbalanced?

c) Construct the analysis of variance (ANOVA) table and test whether the drugs are equally effective.

d) Apply the Tukey multiple comparison procedure to find out significantly different drugs.

e) Test at α = 5% whether the average of the standard drug and the new experimental drug is significantly different from the placebo. Construct a 95% confidence interval for the contrast.

f) Test whether the normality and variance homogeneity assumptions are satisfied.

Question II: Exercise #3 (Data in Table 6.30), + Estimate the ratio of the variance components by a confidence interval.

Variance component estimations is also frequently used in animal breeding. Search for "breeding value" and describe it.

Skills: Algorithm, R Programming Language, SPSS Statistics, Statistical Analysis, Statistics

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