Course Syllabus

Course Description:

Statistical modeling introduces students to statistical modeling beyond what they have learned in an introductory statistics course. Building on basic concepts and methods learned in that course, it empowers students to analyze richer datasets that include more variables and address a broader range of research questions. Other than a working understanding of exponential and logarithmic functions, there are no prerequisites beyond the successful completion of their first statistics course. The modeling focus continues throughout the course as students encounter new and increasingly more complicated scenarios. Analyze and draw conclusions from real data, which is crucial for preparing students to use statistical modeling in their professional lives. This course incorporates real and rich data throughout the text. Using real data to address genuine research questions helps motivate students to study statistics. The richness stems not only from interesting contexts in a variety of disciplines but also from the multivariable nature of most datasets.

Location

After the first week of classes, we'll meet in person in Olin 207 on the second floor of Olin Hall.

Office Hours

In-person Office hours: TTh 11:30 am -12 pm (PT), 2:30-3 pm (PT) in Olin 219

Zoom Office hours: MWF 10:10:30 am

Office hours Zoom link: https://whitman.zoom.us/j/2381981909 (Links to an external site.)

Note: Please contact me if these hours do not work for your schedule!

Preferred method of contact: email ptukhim@whitman.edu.

If I'm free, I'll respond pretty quickly, but don't wait for me, keep working at whatever prompted you to reach out.

Textbook:

STAT2 Modeling with Regression and ANOVA, Ann R. Cannon; George W. Cobb; Bradley A. Hartlaub; Julie M. Legler; Robin H. Lock; Thomas L. Moore.

Purchasing the book isn't required. You can rent the E-book at this link. Renting of the hardcopy is available here.

Additional reference:

OpenIntro Statistics, 2nd Ed. by Diez, Barr, & Cetinkaya-Rundel, 2012.

It is a free book, you could choose to add a contribution to authors if you wish to do so. Otherwise, use the slider to select $0.

You can also download it here.

Statistical Packages:

We will use the (free) statistical package R and the RStudio interface via RStudio Server Pro. Familiarity with R/RStudio is assumed, but not required. You will be able to access RStudio Server Pro on any device with internet access by clicking this link. Use your MathLab account credentials to access Rstudio Server Pro. If you forgot your password you can reset it. If you need help with that, email Dustin Palmer at palmerdl@whitman.edu.

Why now is the time to learn R

Student Learning Outcomes:

Choose, fit, assess, and use appropriate statistical models.
Understand and explain the limitations of statistical analysis.
Employ statistical software to solve data-based problems.
Present statistical analysis in both a technical and non-technical format.
Understand the difference between statistical significance and practical significance.
Be able to read, write, and critique a statistical report.
Be able to distinguish between good data and "not-so-good" data.

Course Content:

Classical one and two sample hypothesis tests and confidence intervals (t-tests).
Simulation methods (simulated p-values, bootstrap method, permutation tests).
Simple linear regression (modeling and inference).
Multiple linear regression.
Advanced regression techniques.
Analysis of variance (one-way and two-way).
Contingency tables and the Chi-squared test.
Logistic regression

Statistics is not a spectator sport. You learn by doing.

In this class, I expect you to actively participate and get involved to learn the material. We don't focus on memorizing formulas and complex computations! While there are some formulas involved, and you'll probably need a calculator occasionally, I'm more interested in whether you can apply knowledge of statistical concepts to everyday situations. The answer in this class is almost never just a number.

Time Commitment

Statistical Modeling is a 3 cr class. Generally speaking, you should spend about 3 hours a week in-class and 6 hours per week outside of class working on assignments, presentations, and projects. It is a good idea to schedule your time outside of class and stick to that schedule.

Canvas Modules

On Canvas, the course is divided up into Modules. There is a Module associated with each chapter.

Within a Module, there are tasks associated with each section/chapter of the textbook.

Task 1: Discussions

1. Read the Discussion assignment for the section/chapter. You'll find general prompts for how to respond to the discussion and, at times, notes specific to the section.

2. Read the corresponding section/chapter of the book and /or watch the video. The videos are all hosted on YouTube. If you look at the video description (under the video), you'll see timestamps for each of the exercises and brief descriptions. It is not necessary to watch the whole video or any of the videos. They are a resource for you as you work through the section/chapter.

3. Read some of the comments from the Discussion assignment. Make a comment of your own. This is public, everyone in the class can see it.

To receive full points make sure that your comment is meaningful and reflects the fact that you have read the content and/or watched the video with the intent to learn and understand the material on a deeper level. Late discussions are not accepted.

Task 2: Labs

Work on a Lab during class time, complete the assignment by the next class meeting, and be ready to discuss your work and ask/answer questions.

How to prepare for a Lab discussion:

1. Make sure that your R code works, I will be there to help you during class and office hours to make sure that it does.

2. Make sure that you answer all of the questions and that you use R output as evidence to support your answer.

3. Knit your file into a pdf and be ready to share it on Zoom while talking about what you have done.

Note: It doesn't need to be 100% correct to get a passing grade. The main purpose of the Lab is for you to practice the material and coding with R and learn from your mistakes. I will be evaluating your engagement with the course rather than expecting a perfect solution.

Task 3: Homework Assignment

Each week upload your completed Homework to the Canvas as a pdf file.

How to get a good grade on a Homework assignment.

1. Make sure that your R code works, I will be there to help you during class and office hours to make sure that it does.

2. Make sure that you answer all of the questions and that you use R output as evidence to support your answer.

3. Use Writing Style Guidelines

4. Study the rubric to make sure you know how your answers will be evaluated.

Task 4: Self-assessment Quiz

Complete the Self-assessment quiz. The purpose of this assessment is to reflect on your learning strategies and techniques and learn from your own experience and let me know of any concerns/ questions you might have about the course as a whole. This is private, only I can see it.

How to get a good grade on a Self-assessment quiz.

Make sure that your answers are meaningful and reflect the fact that you have read the content and/or watched the video with the intent to learn and understand the material on a deeper level.

Task 5: Course Project

There will be a project to determine your mastery of the overarching course goals and the synthesis of the chapter ideas/goals.

There will be a media presentation that you will need to do as a part of the project as well as a peer-reviewed component.

Details to follow.

Task 6: Participation/Attendance

You are expected to attend class regularly and participate in class discussions.

Late Assignment Policy

All assignments must be readable, and when appropriate, all work must be shown to receive credit.

Late work will receive a 5 percentage points deduction per calendar day, with no work accepted more than 3 calendar days after the deadline (unless other arrangements have been made before the due date). My main recommendation to avoid the late submission penalty is to pay close attention to deadlines and start working on the assignments early to avoid the stress of trying to complete them at the last minute.

You are encouraged to work together on labs and in-class activities, but all work you submit must be your own (unless the assignment specifically states otherwise). The first act of academic dishonesty will result in a score of zero on the item in question. A subsequent offense will result in an F for the course. Students should consult the Academic Honesty Procedures if they have any questions.

Grading

Below is a table listing the different components of the course and their weight in calculating your final numeric grade.

Discussions	10%
Labs	15%
Self-assessment Quiz	10%
Homework	35%
Project	25%
Participation/Attendance	5%

Course Grade

In this, class I regard a “B” as the default grade you get for doing what is expected.

An “A” requires going above & beyond – show intellectual curiosity, strive to understand the “big ideas,” don’t stop at the recipe.

A “C” means you pass – but barely, with serious gaps in your knowledge that you need to address.

Any grade lower than a "C" means that you do not pass the course.

Final letter grades will be determined as follows:

Letter Grade	Weighted Score
A +	97-100
A	93-96
A-	90-92
B+	87-89
B	83-86
B-	80-82
C+	77-79
C	73-76
C-	70-72
D+	67-69
D	63-66
D-	60-62
F	0-59

Note that Canvas will display unweighted scores.

Important Notes:

Any student needing accommodations should inform the instructor. Students with disabilities who may need accommodations for this class are encouraged to notify the instructor and contact the Academic Resource Center (ARC) early in the semester so that reasonable accommodations may be implemented as soon as possible. All information will remain confidential.
Academic dishonesty and plagiarism will result in a failing grade on the assignment. Using someone else's ideas or phrasing and representing those ideas or phrasing as our own, either on purpose or through carelessness, is a serious offense known as plagiarism. "Ideas or phrasing" includes written or spoken material, from whole papers and paragraphs to sentences, and, indeed, phrases but it also includes statistics, lab results, artwork, etc. Please see the student handbook for policies regarding plagiarism.