About

Table of contents

  1. About
  2. Weekly schedule
  3. Grading
    1. Attendance
    2. Assignments
    3. Final project
  4. General Policies
  5. Generative AI policy
  6. Academic Honesty
  7. Academic Accommodations for Students with Disabilities
  8. Expectations for Classroom Conduct
  9. Copyright Notification

About

This course introduces students to statistical modeling and, more broadly, statistical thinking (more on statistical thinking here). We will cover linear regression, ANOVA, model checking, and model selection. We may cover more advanced topics like generalized linear (mixed) models, Bayesian regression, and missing data techniques. At the end of this course, students are expected to know how to apply different basic statistical concepts using R software.

Main Goal of this Course:
The main goal is that this course acts as a primer for statistical thinking. Students taking this class should learn the basics to fit a statistical model to data. The process to fit a statistical model to data taught in this course focuses on (1) the data generating process (including the sources of uncertainty), (2) writing out the statistical model that reflects that data generating process, and (3) fitting the model to observed data using R.

Prerequisites: One previous statistics course.

Weekly schedule

  • 8:00 AM
  • 8:30 AM
  • 9:00 AM
  • 9:30 AM
  • 10:00 AM
  • 10:30 AM
  • 11:00 AM
  • 11:30 AM
  • 12:00 PM
  • 12:30 PM
  • 1:00 PM
  • 1:30 PM
  • 2:00 PM
  • 2:30 PM
  • 3:00 PM
  • 3:30 PM
  • 4:00 PM
  • 4:30 PM
  • 5:00 PM
  • 5:30 PM

  • Monday

    • Lecture
      9:30 AM–10:20 AM
      D 207
    • Office Hours
      3:00 PM–4:00 PM
      D 107 (Lacasa)

  • Tuesday

    • Office Hours
      9:00 AM–10:00 AM
      D 009C (Sholl)

  • Wednesday

    • Lecture
      9:30 AM–10:20 AM
      D 207
    • Office Hours
      3:00 PM–4:00 PM
      Zoom [link on Canvas] (Lacasa)

  • Thursday

    • Office Hours
      9:00 AM–10:00 AM
      D 009C (Sholl)

  • Friday

    • Lecture
      9:30 AM–10:20 AM
      D 207
    • Office Hours
      10:30 AM–11:30 AM
      D 107 (Lacasa)

Grading

The course will be for 3 credits, graded on an A-F scale. A (>90%), B (90%-80%), C (80%-70%), D (70%-60%), and F (<60%). Final grade will be based on the following criteria: Attendance and participation 20% | Assignments 40% | Final project and presentation 40%

Attendance

Attendance to lectures and in-class participation are expected. Coming late to class, leaving early, or failing to attend class will lower your grade.

Assignments

Homework assignments will be notified at least a week in advance.

Final project

Semester projects may deal with any topic that interests the student and is approved by the instructor.Projects are expected to identify a research problem and elaborate a statistical model that is appropriate for solving that problem. Projects consist of a manuscript and a tutorial that describes all the steps of the analysis in a reproducible manner. More information here.

General Policies

Generative AI policy

Students may use generative AI tools as an assistant to complete their homework or projects but are required to understand every step of their work. Failure to justify their own work may reduce the student’s grade.

Academic Honesty

Undergraduate and graduate students, by registration, acknowledge the jurisdiction of the Honor System (www.ksu.edu/honor). The policies and procedures of the Honor System apply to all full and part-time students enrolled. A grade of XF can result from a breach of academic honesty.

Academic Accommodations for Students with Disabilities

Students with disabilities who need classroom accommodations, access to technology, or information about emergency building/campus evacuation processes should contact the Student Access Center and/or their instructor. Services are available to students with a wide range of disabilities including, but not limited to, physical disabilities, medical conditions, learning disabilities, attention deficit disorder, depression, and anxiety. If you are a student enrolled in campus/online courses through the Manhattan or Olathe campuses, contact the Student Access Center at accesscenter@k-state.edu, 785-532-6441.

Expectations for Classroom Conduct

All student activities in the University, including this course, are governed by the Student Judicial Conduct Code as outlined in the Student Government Association By Laws, Article VI, Section 3, number 2. Students that engage in behavior that disrupts the learning environment may be asked to leave the class.

During this course, students are prohibited from selling notes to or being paid for taking notes by any person or commercial firm, or posting lecture notes on any websites without the express written permission of the professor teaching this course.