Syllabus

Table of contents

1. Learning Objectives
   1. List of Topics
   2. Textbooks
   3. Prerequisites
2. Grading
   1. Assignments
   2. Class participation
   3. Exams
3. Collaboration & Academic Integrity

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Learning Objectives

This course will cover the foundations of mathematical statistics. These include the principles and methods for statistical inference. One of the essential skills you will learn is how one formulates and communicates statistical ideas clearly with mathematical rigor. Furthermore, you will develop a conceptual understanding behind the (seemingly) abstract mathematical formulations through simulations and numerical analyses. To this end, all of the homework and take-home exams are expected to be submitted using Pluto notebooks, and typeset in Markdown and TeX. The course will, broadly, cover the following list of topics

List of Topics

• Review of probability, random variables, transformations
• Statistics and sampling distributions
• Point estimation
• Statistical intervals based on a single sample
• Tests of hypotheses based on a single sample
• Inference based on two samples
• Regression and correlation
• Analysis of variance

Textbooks

Modern Mathematical Statistics with Applications, 2nd Edition,
Devore, J. L., Berk, K. N., & Carlton, M. A. (2012)
(Electronic copy available online via PSU Libraries: Link)

All of Statistics: A Concise Course in Statistical Inference,
Wasserman, L. (2004)
(Electronic copy available online via PSU Libraries: Link)

Prerequisites

{MATH / STAT 318} OR {MATH / STAT 414} OR {MATH / STAT 416}

This course will require a background in elementary probability, single-variable calculus, and basic familiarity with linear algebra and multi-variable calculus.

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Grading

The grades will divide among the following activities

Assignments

Assignments will, typically, encompass the topics covered in the lectures. They will comprise of theoretical parts, i.e., deriving results, and numerical parts, i.e., simulations and compiling plots. One of the key learning objectives in this course is to communicate statistical ideas with technical proficiency. Here are the key expectations for your assignment submissions:

1. Your assignment solutions should be typed up
2. All mathematical expressions should be written in TeX
3. All code should be clear and human-readable (at least, by the instructor and the TA)
4. They need to be submitted as static HTML files exported using Pluto notebooks
5. You will get partial credit if your main ideas are well-organized and communicated clearly

Please have a look at the following example for an example assignment along with the solutions.

Class participation

Class participation will require you to make scribed lecture notes on a weekly basis. You will be divided into groups on Canvas, and each group is expected to submit the compiled scribed notes as HTML files exported from Pluto notebooks.

Exams

Exams will, essentially, be longer versions of assignments testing your learning on all topics covered until that point.

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Collaboration & Academic Integrity

You are expected to complete the assignments on your own. If you happen to discuss them with a fellow MATH / STAT 319 student that’s okay; Please acknowledge your collaborators. However, you are expected to type up your own solutions from scratch and list the students you collaborated with. In a nutshell, each student must understand their assignment solution well enough in order to reproduce it by themselves.

✅ The following is OK:

You and your friends work through the problems together over a couple of study sessions. You bounce ideas off each other, and eventually come up with a pretty good solution. You sit down at your computer and type up that solution in your own words, perhaps lightly consulting notes you took while working with your friends.

❌ The following is NOT OK:

You and your friends work through the problems together over a couple of study sessions. You bounce ideas off each other, and eventually come up with a pretty good solution. One of your friends types up their solutions first. Since you participated in the study session and helped come up with the answers, you take the liberty to use your friend’s solutions as a starting point for your own. Or, worse yet, you submit their solutions as your own.

Here are a few of the examples of honor code violations:

• Looking at the writeup or code of another student.
• Showing your writeup or code to another student.
• Discussing homework problems in such detail that your solution (writeup or code) is almost identical/ or showing high similarity to another student’s answer.
• Uploading your writeup or code to a public repository (e.g. github, bitbucket, pastebin) so that it can be accessed by other students. - if you are aware of any, please alert the teaching team.
• Looking at solutions from online repository or previous years’ homeworks
• Collaborating with others during exams.
• Entering homework questions into any software, apps, or websites. Accessing resources that directly explain how to answer questions from the actual assignment or exam is a violation of course policy.

We will be using plagiarism detection software. Students who do not follow the policies of the course on collaboration and academic honesty will be reported to the office of academic honesty and should expect to receive an F in the course. ⚠️ All students involved in an incident, regardless if they are copying or sharing their work, are going to be reported to The Academic Integrity Committee. To learn more about academic integrity at Penn State, please visit the Penn State Academic Integrity site. Academic dishonesty can lead to a failing grade or referral to the Office of Student Conduct.