By the end of this course, students will be able to:
- Understand and apply core statistical concepts and Interpret correlations, regressions, t-tests, ANOVAs, and chi-squared tests
- Run these analyses and apply statistical models in R.
In short, at the end of this course a successful student will be able to consume and generate basic statistical analyses.
By the end of this course, students will be able to:
- Understand and apply core statistical concepts and Interpret correlations, regressions, t-tests, ANOVAs, and chi-squared tests
- Run these analyses and apply statistical models in R.
In short, at the end of this course a successful student will be able to consume and generate basic statistical analyses.
Prerequisites
- MATH-1502 (Calculus II) /1512 (Honors Calculus II) /15X2 (Transfer Calculus) /1522 (Linear Algebra for Calculus) etc. (Calc II)
- some exposure to linear algebra
Textbook and Other Materials
- Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Los Angeles, CA: Sage. and other supplementary materials
- Additional Materials/Resources for the Course: The following are free online textbooks for statistics:
- https://onlinestatbook.com/index.html
- https://open.umn.edu/opentextbooks/textbooks/559
- https://ethanweed.github.io/pythonbook/landingpage.html
Assessment of Learning
Lecture Attendance
100 points (11.1%)
In-Class Activity/Quizzes/Homework
200 points (22.2%)
Ed Discussion
50 points (5.6%)
Lab/
Lab Assignments
200 points (22.2%)
Midterm
150 points (16.7%)
Final
200 points (22.2%)
Total Points
900 points (100%)
For each section, the earned points will be proportionally converted based on the rubric's weighting. For instance, if you earn 95 out of 100 possible scores for Final, this score will be scaled to 190 out of 200 points according to the rubric.
LECTURE ATTENDANCE: From past experience teaching various courses, we have found a strong statistical relationship between class attendance and overall course performance. To that end, attendance will be counted as a small part of the course grade to help students perform better overall.
Beginning on the second week of class, attendance will be taken at all lecture sessions. To receive points, students are expected to arrive on time and stay for the entire class period. Arriving more than five minutes late, leaving class early, or acting in a disruptive manner during class will forfeit the points. Students must attend the lecture for which they are officially registered to earn attendance points.
This attendance policy is designed to promote class participation; therefore, no makeups will be permitted under any circumstances, and no absences will be classified as "excused" without official documentation. However, to accommodate valid reasons for missing class, students can still earn full attendance points with up to one without documentation.
Attendance will be strictly monitored, and points for a session will be annulled for the entire class if the number of respondents exceeds the total number of students present on that day, so please do not have another student sign in for you, and do not sign in for another student.
Class disruptions of ANY kind will NOT be tolerated and may result in your removal from the classroom and/or loss of participation points for that day. Please show courtesy to your fellow classmates and instructor or teaching assistant by adhering to the following class rules:
• Come to class on time and stay for the entire class period.
• Refrain from conversing with your fellow students.
• Put away any reading materials, cellular phones, and other electronic devices unrelated to the course.
Course Policies
All work for this class is to be done individually. You are strongly urged to familiarize yourselves with the GT Student Honor CodeLinks to an external site. rules. Specifically, the following is not allowed:
- Copying, with or without modification, someone else's work when this work is not meant to be publicly accessible (e.g., a classmate's program or solution).
- Submission of material that is wholly or substantially identical to that created or published by another person or persons, without adequate credit notations indicating authorship (plagiarism).
- Putting your projects on public. Otherwise, if a student (in the future) copies your codes/projects, the student obviously violates the honor code but you will also be implicated.
Academic Integrity
Students are expected to uphold the highest standards of academic integrity. Any form of cheating, plagiarism, or dishonesty will not be tolerated and may result in disciplinary action.
Zero Tolerance Policy on Cheating and AI Assistance (e.g., Chat GPT, Gemini)
We maintain a strict zero-tolerance policy regarding academic dishonesty, including the use of ChatGPT and other AI tools. Any student found using AI to complete assignments/quizzes/exams will be reported immediately, receive a grade of zero for the submission, and risk a final grade of F.
Disability Accommodations
If you require any accommodation due to a disability, please inform the instructor at the beginning of the course to ensure that appropriate arrangements can be made.