Upon successful completion of the course, each participant should be able to:
1. Explore and summarize datasets both numerically and graphically using appropriate statistical methodology.
2. Evaluate and compare experimental designs and sampling methods to control error in the context of a real-world experiment.
3. Model data and obtain probabilities and critical values using the normal, binomial, Poisson, t, Chi-squared, F, and other basic models for the distribution of a single variable.
4. Use the Central Limit Theorem and properties of sampling distributions to perform statistical inference for means and proportions by calculation and interpretation of hypothesis tests and confidence intervals.
5. Make inferences about the mean and median for both normal and non-normal populations using the t-distribution and bootstrap methods.
6. Make inferences comparing two population central values for paired and unpaired data from normal and non-normal populations using Wilcoxon Rank Sum and Signed-Rank tests.
7. Make inferences comparing two or more population variances using Chi-squared, F, and BFL tests.
8. Make inferences about more than two population central values using analysis of variance and non-parametric procedures (Kruskal-Wallis).
9. Perform and interpret multiple comparison procedures using parametric (Scheffé’s, Tukey’s, or Dunnett’s test) and non-parametric methods.
10. Calculate and interpret Chi-squared goodness of fit tests, tests of independence and homogeneity, contingency tables, and odds ratios.
11. Perform, evaluate, and interpret single and multiple linear regressions and logistic regressions using proper procedures to check required conditions.
12. Build statistical models using appropriate procedures for selecting variables, formulating the model, and checking model assumptions.
13. Carry out analyses of real data sets using the R programming language and communicate the results in written form.