# Syllabus

This is 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.

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

## Learning objectives

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.

## Expected workload

A full time semester is 12 credits; this means that a 4 credit course is about 33% of a fulltime.

Out of 40 hours, the 33% makes out 13h20m in a week. We meet 4h in class. You should be spending about 9h every week out of class working with our material.

It has been shown in research that “Time on Task” is the most important predictor of how much you learn. I will assign homework to train the most recent material, but I strongly encourage you to work through all the problems in the book.

# Exams and grading

Your performance in this course will be measured on your class presence and participation, one semester-long research paper critique project, and a final exam.

## Class presence

The course will build on class participation and discussion. For every 5 missed class meetings without prior approval or documented severe crisis your final grade will be penalized by one full grade step.

## Research paper critique

The source of your grades in this course will be on a written report, evaluating the statistical methods of a research paper. You will pick a paper to review by the third meeting of the course, on September 14.

Your report should

- Comprehensively describe the statistical analysis methods and the data collection methods used by the authors of the paper.
- Critically evaluate the choices made by the authors: data collection designs, analysis methods.
- Recalculate the statistical analyses of the authors, alternatively a comprehensive explanation of why the research paper does not provide enough information to replicate the analyses.

Each of these tasks is graded on a scale of A/C/D:

D is awarded for a completed report, with no more than 4 minor flaws.

C is awarded for a completed report without any flaws.

A is awarded for a completed report without any flaws that also contains a suggestion and evaluation of alternative choices the paper authors could have made.

## Final exam

The course has a final exam: this final exam is Pass/Fail. Failing the exam will fail the course. There will be an opportunity in the final that allow you to raise your grade by up to two minor grade steps: for instance, raise from a B to a B+ or an A-.

## Grade calculation

Your final grade, assuming all three tasks as well as the final exam have been passed, is an arithmetic mean of the three tasks, using quality points. The calculation is rounded to an even letter grade: decimal part strictly exceeding 0.6 is rounded up, all other decimal parts are rounded down.

An average grade of E is rounded up to a D.

# Required materials

Textbook: Wackerly, Mendenhall, Scheaffer: Mathematical Statistics with Applications

We will be working with computer laborations throughout the course. Ensure you are able to login and use campus computers before August 31. Inability to login will count as a missed class meeting.

# Additional resources

- Explore the central limit theorem, and how it fails for the
`t(df=1)`

distribution. - Download R and RStudio to use RMarkdown to write your final report with inline code execution, calculations, graphs and data wrangling.
- Learn about how to use R and RStudio.