About

Table of contents

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

About

This course introduces students to designed experiments and the statistical modeling of data generated by designed experiments. The modeling aspect will focus on statistical thinking (more on statistical thinking here). We will cover basics of designed experiments, including the different components of designed experiments, types of treatment designs, types of experiment designs and their different characteristics, how to write out a statistical model that corresponds to the design, and what to write the materials section in a paper. We may cover more advanced topics like power analyses. Note that more advanced topics are typically covered in Analysis of Messy Data (STAT 870), taught in the Fall. Ultimately, the main goal is that this course acts as a primer for statistical thinking applied to designed experiments in each student’s academic field.

Main Goal of this Course:
The main goal of this course is to present the basic concepts of designed experiments. Students are expected to learn to identify key design components, write out the corresponding statistical model and fit it to the data using R software.

Course topics: This course will cover the basic concepts for designed experiments, including the data generation process.

Prerequisites: STAT 705

Weekly schedule

  • 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
      9:10 AM–10:10 AM
      BB
    • Office Hours
      10:10 AM–11:10 AM
      BB

  • Tuesday

    • Lecture
      9:10 AM–10:10 AM
      BB

  • Wednesday

    • Lecture
      9:10 AM–10:10 AM
      BB
    • Zoom Office Hours
      3 PM–4 PM
      See Syllabus

  • Thursday

    • Lecture
      9:10 AM–10:10 AM
      BB

  • Friday

    • Lecture
      9:10 AM–10:10 AM
      BB
    • Office Hours
      10:10 AM–11:10 AM
      BB

Software

Course material and examples will be provided in R. However, students may use other programming languages if appropriate.

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. Incorrect assignments may be resubmitted once for full points. After that, assignments may be resubmitted for 80% of their last point worth. Late submissions will be considered for 80% of the original points.

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 develop a designed experiment that is appropriate for solving that problem. Projects consist of a manuscript and a tutorial that describes the research problem, the experiment design and the treatment design. More information here.

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% | Midterm Exam 20% | Final project and presentation 20%

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.