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

We will 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

Note: Please contact me if you would like to schedule additional office hours.

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 Software:

We will use the (free) statistical software R and the RStudio interface via RStudio Workbench. Familiarity with R/RStudio is assumed, but not required. You will be able to access RStudio Workbench 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 or section of the chapter.

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

Course Assessment

Your grade will contain the following components.

1. Discussions (15%): Comment/ask a question about the topic. To receive full points make sure that your comment/question is meaningful and reflects the fact that you have read the content with the intent to learn and understand the material on a deeper level. No Late discussions will be accepted.

2. Weekly Labs (40%): Each Thursday we'll start a lab designed to explore new techniques for working with data. Most labs are designed to take about 2-3 hours to complete, so you'll most likely need to finish them outside of class. Labs are due in Canvas the following week.

3. Final Project (includes multiple assignments) (45%): Your Final Project will represent a complete exploration of a large-scale data project, suitable for use in a portfolio of your work. The Final Project is in lieu of a Final Exam. More details to follow.

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

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 (e.g. a grade of 85% would be reduced to 80% up to 24 hours later). No late work is accepted more than 7 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	15%
Labs	40%
Project Proposal	5%
Project Report including RPubs link	20%
Project Presentation	20%

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

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.
In accordance with the College’s Religious Accommodations Policy, I will provide reasonable accommodations for all students who, because of religious observances, may have conflicts with scheduled exams, assignments, or required attendance in class. Please review the course schedule at the beginning of the semester to determine any such potential conflicts and let me know by the end of the second week of class about your need for religious accommodations. You can contact your academic advisor or Adam Kirtley, Whitman’s Interfaith Chaplain, for support in making this request. If you believe that I have failed to abide by this policy, here is a link to the Grievance Policy, Grievance Policy | Whitman College where you can pursue this matter.