E-Mail: szhang@stcloudstate.edu Office: CH366AF
The in-class session meets in Ch/115 on Tuesday and Thursday from 2:00 pm to 3:15 pm. Students in the online session study at their own pace, keeping due dates in mind.
Students are expected to adhere to the guidelines provided in the “Announcement” section on D2L. In order to facilitate your learning experience, relevant links have been established, including access to this syllabus, course lecture notes, and potentially other supplementary materials.
Office hours are MWF 1:00 pm - 4:30 pm. Students can also seek assistance from the instructor via Zoom using the following link: https://minnstate.zoom.us/j/96907848410 (Passcode: 3.14). Whenever assistance is required, it is recommended that students schedule an appointment with the instructor by sending an email. The instructor’s availability is flexible to accommodate various time slots.
Summarize data distributions using descriptive statistical methods.
Use appropriate probability distributions.
Choose an appropriate statistical method when analyzing engineering data.
Interpret the results of inferential statistics when analyzing engineering data.
Students should become critical consumers of statistically-based results reported in popular media, recognizing whether reported results reasonably follow from the study and analysis conducted.
Students should be able to recognize questions for which the investigative process in statistics would be useful and should be able to answer questions using the investigative process.
Students should be able to produce graphical displays and numerical summaries and interpret what graphs do and do not reveal.
Students should recognize and be able to explain the central role of variability in the field of statistics.
Students should recognize and be able to explain the central role of randomness in designing studies and drawing conclusions.
Students should gain experience with how statistical models are used.
Students should demonstrate an understanding of the basic ideas of statistical inference, both hypothesis tests and interval estimation.
Students should be able to interpret and draw conclusions from standard output from statistical software packages.
Students should demonstrate an awareness of ethical issues associated with sound statistical practice.
(Based on https://www.amstat.org/docs/default-source/amstat-documents/gaisecollege_full.pdf)
Probability distributions; introduction to statistical methods, including hypothesis testing and confidence intervals, one-way anova, simple linear regression, quality control basics; applications, and the use of statistical software.
The assessment breakdown for this course comprises two midterm exams (each 50 points), three individual projects (each worth 50 points), and a concluding final exam (150 points). For STAT 353-40, all exams are in-person. For STAT 353-41, all assessments will be on D2L with dates.
The grading scale is as follows:
A = 90% B = 80% C = 70% D = 60% F = below 60%
| CourseWork | Topics | Date |
|---|---|---|
| Exam #1 | Chapter 2 | 9/18 |
| Exam #2 | Chapters 3-5 | 10/14 |
| Project #1 | Descriptive Statistics & Confidence Intervals | 11/6 |
| Project #2 | Hypotheses Testing | 11/20 |
| Project #3 | Regression | 12/9 |
| Final Exam | All Chapters | 12/11 |
Makeup Policy: Makeup opportunities will not be provided for any assignments or assessments, unless valid and legitimate reasons are presented.
Incorporating AI: The integration of AI to enhance learning within this course is encouraged. It is important to transparently outline your utilization of AI tools. It is imperative that your work genuinely reflects your effort and understanding. Delve into discerning when the use of AI is appropriate versus when it might serve as a shortcut. Don’t hesitate to engage in discussions with both your peers and myself. Given that AI’s impact on society is continually evolving, part of your educational journey is to expand your comprehension of potential innovations, rather than being bound by the past. Moreover, please consult the Academic Integrity policies that are applicable to all courses within our institution. If there’s any uncertainty regarding how Academic Integrity intersects with the use of AI in this course, please seek my guidance. (Excerpt sourced from SCSU)
For further insight, refer to the SCSU guidelines concerning AI utilization: SCSU AI Guidelines https://services.stcloudstate.edu/TDClient/1919/Portal/KB/?CategoryID=24236
Kindly print a copy of this syllabus and affix it to your bedside wall for reference.
It is advised to prepare for the required assignments at least 24 hours prior to their respective due times.
Don’t hesitate to reach out to the instructor with any queries you may have; your questions are welcome.
Utilize our tutoring service available at: https://www.stcloudstate.edu/cose/resources/tutoring/iself-332.aspx. Additionally, you are entitled to 15 hours of free service on tutor.com, accessible via the “Resources” section after logging into D2L.
Remember, thinking is a demanding process, yet engaging in critical thinking requires even more effort.
Instances of academic integrity violations, in any form, by a student warrant potential disciplinary measures from the instructor, college, or university. It’s important to note that I uphold a strict stance against cheating, and any breach of academic integrity will incur significant penalties. Should you require clarification on the parameters of academic misconduct, please feel free to reach out to me without hesitation.
There is no required textbook.
Course covers