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.

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. Renting is available at this link.

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.

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, art work, etc.  Please see the student  handbook for policies regarding plagiarism.