# Catalog course description

A course in the basic concepts of applied mathematical statistics: parametric models, estimation, confidence intervals, hypothesis testing.

Prerequisites cover i.a. elementary probability theory (sample space, events, probability), density & distribution functions, conditioning, independence, expectation, samples, parameter estimation, confidence intervals, hypothesis testing, central limit theorem.

MTH 411 continuation covers regression, correlation, linear models, ANOVA, randomized block designs, non-parametric methods

# Learning goals and examination

After you finish this course, you will be able to:

- Explain, derive and prove core results of applied statistics, including but not limited to:
- Point estimators, and their properties – how are the standard computations for normal and binomial distributions derived, and why do they work; computations such as sample means, proportions.
- Hypothesis testing – how and why do the standard hypothesis tests work?
- Recognize when the tests and methods you have learned fail to apply.