About
Table of contents
- About
About
This course covers the most fundamental topics in the science of Statistics and Probability. We’ll discuss everything from descriptive vocabulary and methods of data visualization, to theoretical statistics and statistical analyses.
My primary goal for my students is to increase your understanding and literacy with the concepts in Statistics to arm you with knowledge for the real world. While this class is a foundational and extremely important course for the sciences, it’s more important that you capture the information that you would use every day of your lives.
As such I hold very high standards for your understanding of interpretations and concepts, while I hold lower standards on mathematical rigor than most instructors in the field.
My secondary goal is to ensure that if you attend class every day you will not have to study more than 2 hours per week outside of class (inclusive of homework time) to succeed in the class.
Main Goal of this Course:
To builds students statistical literacy and introduce them to basic statistical analysis.
This course is loosely split into three segments:
The first portion of this course will focus on developing a baseline vocabulary for the science of Statistics, learning basic measurement calculations, methods of observational studies,and performing regression analysis.
The second portion of this course will introduce experimental design, as well as probability and distribution theory.
The third portion of this course will focus on inference, hypothesis testing, and comparisons.
Prerequisites: There are no formal prerequisites for this course, however it is highly suggested you have completed a high school algebra course before taking this class. If you lack that prerequisite, or feel incredibly shakey/rusty on those skills, we offer a “catch-up” course to help students understand enough algebra/math to succeed in this class. Details will be included closer to the semester.
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
- Lecture11:30 AM–12:20 PMDickens 106
- Office Hours12:20 PM–1:20 PMDickens 106
Tuesday
Wednesday
- Lecture11:30 AM–12:20 PMDickens 106
- Office Hours12:20 PM–1:20 PMDickens 106
Thursday
Friday
- Help Lab Hours10:20 AM–11:20 AMDickens 205
- Lecture11:30 AM–12:20 PMDickens 106
Textbook
There is no required textbook for this course. Most of the homework questions come from “The Practice of Statistics in the Life Sciences, 4th Edition, Baldi and Moore”, but you will not need access to the book in order to do the homework.
If you’d like to follow along in that textbook, feel free to acquire it.
Recommended readings and materials may pop up over time, but will be freely accessible if they’re required.
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:
| Item | Grade Weight | Extra Credit |
|---|---|---|
| Exam 1 | 20% | 2.5% |
| Exam 2 | 20% | 2.5% |
| Final Exam | 25% | 5% |
| Homework | 20% | 0% |
| Quizzes | 15% | 10% |
| Attendance | 0% | 5% |
| Total | 100% | 25% |
Attendance
Attendance to lecture and in-class participation are completely optional, but highly recommended. If you’re late or need to leave early, you are only asked to be respectful and not be disruptive. I would rather you be in class at all than not in attendance.
Attendance Credit/Question of the Day
5% of extra credit will be given out for attendance, determined through participation and submission of individual practice problems for each day of class.
These problems are not graded, simply counted for participation and assigned the full points.
These will be scored as they are submitted, based off of level of understanding. This is purely for data collection and will never reflect on you negatively or positively. They simply provide a persistent understanding of the classes competency level, so that instruction can be adjusted as needed. These scores also help students gauge their understanding regularly.
Quizzes
There will be 3 quizzes throughout the course, held shortly before the exams. They’re aimed at preparing you for the Exams by covering the section content up to that point.
For each quiz students will have the opportunity to correct every incorrect problem, following a pre-specified rubric, for up to 100% of the lost points.
Homework
There will be 5 homework assignments throughout the semester. Homework should be submitted through Canvas before the due time. Please show your work to get full credits.
Homework assignments should be submitted to Canvas. Please scan or take pictures of your answers and upload it to Canvas. Supported file formats include .pdf, .doc, .docx, .jpg, and .jpeg.
Late homework will not be accepted. It is up to instructor discretion if an extension is provided.
Homework Corrections
For all homework assignments in the course, students are allowed to submit corrections of their work for each problem completed for less than full points. Incomplete questions will not be offered the opportunity to be corrected.
Corrections provide 100% points back. Submissions will only be accepted through the Canvas assignment.
For the entire homework assignment, per question you wish to correct, provide the following:
- Why you got the answer wrong (i.e., I mistook a qualitative variable for a quantitative variable, so I described it as an Interval variable as a result)
- Why that answer is wrong (i.e., the variable describes a ranking of salespeople at Amazon, which is not a quantification of salespeople nor is it an interval where 0 doesn’t reflect absence of something)
- What the correct answer is (i.e., the variable is Qualitative and Ordinal)
- Why it’s the correct answer (i.e., a numeric ranking is a categorization of something so it is qualitative and it has a natural ordering where 1 is above 2 so it is ordinal)
General Policies
Generative AI policy
The University has yet to release a comprehensive AI policy. Thanks to that, I get to define my own.
I cannot stop any student from using Large Language Models (LLMs), Small Language Models (SLMs), or Generative AI. However if I suspect you are using one of these tools to replace knowledge work I will dole out the most egregious punishment any instructor can use: I will make you do the work again. If you truly believe you can circumvent my methods of detection, please refer to the “About” page of my academic website.
AI related tools are tools. Please recognize that they are something you should be competent with, and I will gladly help you understand their myriad of use-cases in and outside of this class. Do not use them to replace knowledge work, and if you’re unsure whether the action you’re doing qualifies as that, please reach out to me directly and just ask.
Academic Honesty (Gracefully Plagarized from the University Code of Conduct)
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.
Copyright Notification
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.