University of Arizona

Mathematics and Data Science 363

Introduction to Statistical Methods 

Spring 2022

Course Syllabus

Section: 002

Course Schedule

Course Textbook (by Joe Watkins)

Resource Webpage

Homework Guidelines

Course’s D2L website

Instructor: Prof. Tonatiuh Sánchez-Vizuet

Office: ENR2 - S459

Email: tonatiuh@arizona.edu

Please use the subject line "MATH363" for all class related emails

Emails will be answered within 24 hours on weekdays.

Office hours: To be determined

Class modality: in person. Students are expected to be present in the classroom at meeting times. Unjustified absences are grounds for administrative drop or failing grade.

Overview

Introduction to Statistical Methods (DATA363) is the foundation course for the Statistics and Data Science undergraduate major and minor. The course was designed by Prof. Joe Watkins and we will be using many educational resources designed by him over the years for this class, including a free textbook and video lectures.

The class will be taught in a flipped format and you will learn by doing. This means that students are expected to be very actively involved in the learning process and a considerable amount of work is expected from you. For every lecture there are a few associated videos and simple set of checkpoint exercises. Students will be required to watch the video lecture and complete the checkpoint before class. During class meetings, answers for the checkpoints will be discussed and there will be some time to answer questions about the lecture video, but most of the class time will be spent going over a worksheet that will use the concepts introduced in the video. Students will work through the suggested problems in groups and the instructor will be available to provide help. The completed worksheets will be due 24hrs. after the lecture.

Objectives

We shall be using your background in the natural or social sciences, the humanities, or engineering and your previous knowledge of algebra, calculus and linear algebra to consider the issues of collection, model derivation and analysis, interpretation, explanation, and presentation of data. The objective this course is to take advantage of the coherent body of knowledge provided by statistical theory having an eye consistently on the application of the subject. This approach will allow you to extend your ability to use methods in data science beyond those given in the course.

The major prerequisites are comfort with calculus and a strong interest in questions that can benefit from statistical analysis. Willingness to engage in explorations utilizing statistical software is an important additional requirement. The breadth of our examples will show that statistics is applicable to a wide variety of academic disciplines, from the natural and social sciences to the humanities and engineering. 

Goals

• Leave students capable of understanding what statistical thinking is and how to integrate this with scientific procedures and quantitative modeling
• Learning how to ask statistics experts productive questions, and how to implement their ideas using statistical software and other computational tools
• Organizing data numerically and visually
• Understanding the axioms of probability, conditional probability and independence
• Understanding the concept of random variables and expectation with emphasis on parametric families
• Law of Large Numbers and Central Limit Theorem
• Estimation, bias and variance, confidence intervals
• Hypothesis testing, significance and power
• Likelihood ratio tests such as proportion tests, t-tests, chi-square tests, and analysis of variance

Day-to-Day Operations  

The class meets Tuesdays and Thursdays from 2:00 PM to 3:15 PM in room 207 of the Psychology Building. You can find the planned class schedule, the course textbook, and assignments in the online version of the course syllabus. Office hours will take place by zoom, the dates and times will be determined during the first two weeks of class.

This course is taught using a flipped format. Thus, you will be typically watching 5 to 6 short video lectures, answering 2 to 3 checkpoint exercises and submitting responses before the beginning of class. No extensions for checkpoints will be granted. Each checkpoint set will be worth five points and the lowest six grades will be dropped from your final grade computation. The remaining assignments will be averaged and will contribute with 15% of the course grade.

The class will begin with a discussion to assess your understanding of the lectures and to discuss the checkpoints. Students will present their solutions to the checkpoints; participation will be recorded and will amount to 5% of the class grade. The remainder of the class will be devoted to solving a worksheet involving one or two statistics or probability problems that use the concepts covered in the video lectures. These problems can be completed during class time and you are encouraged to work together during class in groups of at most four. The worksheets will be submitted through GRADESCOPE , and will be due by midnight on the same day of the class. Students must be present in the class in order for their work to belligible for credit. No extensions will be granted, but the worksheets with the lowest six grades will be dropped from the final grade computation. The remaining assignments will be averaged and will contribute with 15% of the final grade. You will have to be physically present in the classroom for your worksheet to be eligible for credit. If only one team member is present, only they will receive credit for the assignment.

Finally, there will be a weekly assignment with slightly more involvedd problems that will often, but not always, involve some simple coding. Using the software package R you will to solve 1 or 2 problems involving the concepts covered in the week’s lectures. These worksheets can be submitted in teams of no more than four members and will be due on Gradescope by 11:59 PM (Arizona time) on Sundays, unless otherwise stated. Late worksheets will be accepted up to one week after the due date for up to 70% of the credit. The lowest 3 worksheets will be dropped, and the remaining assignments will be averaged and scaled to 15% of the course grade. You are encouraged to work with others to complete the coding assignments, which will be submitted as a team with at most four members. Each team must make an effort to distribute the work fairly and evenly among its members.

Participating in the course and attending lectures and other course events are vital to the learning process. As such, attendance is required at all lectures and discussion section meetings. Absences may affect a student's final course grade. If you anticipate being absent, are unexpectedly absent, or are unable to participate in class online activities, please contact me as soon as possible. Unjustified absences are grounds for administrative drop or failing grade. 

Calculator

A graphing calculator; any model in Ti-83 or Ti-84 series is recommended.

Computer

For this class you will need daily access to a laptop or web-enabled device, and regular access to reliable internet signal. Our course work will take place in Microsoft Teams, D2L and Gradescope. You will be writing electronically on lecture notes and worksheets, both in the classroom and out. Having access to either a tablet or an electronic drawing tablet that connects to your computer will make the task easier.

Enrolled students can borrow technology from the UA Library on a first come, first served basis. See https://new.library.arizona.edu/tech/borrow for details.

Use of Software

We will do some software computation using R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror. As with any computer software, the syntax in R will seem awkward at first. Many of you will also want to download Rstudio, which is also free. Rstudio provides a graphical user interface that will make the use of R go more smoothly.

Copies of Introductory Statistics with R by Peter Dalgaard are available at the bookstore. Other options for software assistance can be found on the resource webpage.

Evaluation and Grades

There will be six components contributing to your final grade: 

1. Class participation - 5% of the final grade.
2. Class checkpoints - 15% of the final grade (One per class, the lowest six will be dropped).
3. Class worksheets - 15% of the final grade (One per class, the lowest six will be dropped).
4. Coding assignments - 15% of the final grade (One per week, the lowest three will be dropped).
5. Midterm exams - 25% of the final grade (Two midterms).
6. Comprehensive final exam - 25% of the final grade (Scheduled for Monday May 9th, from 3:30 PM to 5:30 PM)

The checkpoints are short exercises described in the videos and are meant to solidify your understanding of a concept. These are due before the beginning of class. There will be no extension on due dates. Check point exercises are graded based on honest effort. The lowest six grades will be dropped and the rest will be averaged and rescaled to 15% of the final grade.

The in-class worksheets are generally 1 or 2 problems with several parts that are designed to deepen and integrate your knowledge. They are designed to be solved in class with input from the instructor and your classmates. Students are encouraged to work together in groups of at most four. Groups can submit a single worksheet. Students are expected to attend class for their worksheet to be eligible for grading. The worksheets will be due by 11:59PM the day of the class. No extensions will be granted. Worksheets will be marked according to the following guidelines. The lowest six grades will be dropped and the rest will be averaged and rescaled to 15% of the final grade.

The coding assignments involve the use of R to solve problems involving the manipulation of data. There will be one such assignment per week, due by 11:59 PM of every Sunday. Coding assignments can be turned in up to a week after the due date with a penalty of 30% of the credit earned. Students are encouraged to discuss and work together. Coding assignments will be submitted as a team with at most four members. Each team must make an effort to distribute the work fairly and evenly among its members. These assignments will be graded according to the following guidelines. The lowest 3 grades will be dropped and the rest will be averaged and rescaled to 15% of the final grade.

We shall have 2 in-class midterm exams, each of them worth 50 points. Each of the two midterms will contribute with 12.5% of the final grade, for a total of 25%. Finally, there will be a comprehensive final exam worth 25% of the final grade. Our final is scheduled for 

Monday May 9th, 2022 from 3:30pm to 5:30 pm.

Final grading scale

The class will be graded based on the percentage of points collected by the student at the end of the semester. The letter grades will be determined according to the standard University guidelines, followed strictly. 

A is 90%-100%, B is 80%-89%, C is 70%-79%, D is 60%-69%, and E is below 60%. 

Withdrawal deadlines

A student may withdraw from the course with deletion from record through January 25, 2022, using UAccess. A student may withdraw with a grade of “W” through March 29, 2022. It is suggested that students consult their academic advisor before withdrawal from any course.

Honors Contracts

This course is available for honors contract. To negotiate the details of an honors contract for this course, please contact the instructor within the first week of classes.

Academic Integrity and University policies

The class will adhere strictly to the University’s policies and codes. Students are expected to take the time to familiarize themselves with them; notably the academic integrity policy and student code of conduct. Students with special needs should contact SALT - Strategic Alternative Learning Techniques Center or the Disability Resources Center.

Class Schedule

The following is the intended class schedule and the topics that will tentatively will be covered every class, including the videos and textbook sections. The videos are also available from the course’s D2L website. All the topics marked in RED are OPTIONAL .