An introduction to the basic concepts and methods of statistical data analysis. Students will summarize and interpret data, evaluate important assumptions, and draw appropriate conclusions based on sample data sets from a variety of disciplines. Topics include descriptive statistics, statistical graphs, probability, discrete and continuous probability distributions, confidence intervals, hypothesis tests with one and two samples, correlation and regression, chi-square tests and one-way ANOVA.

Text: Elementary Statistics, 7th edition by Larson, published by Pearson. Available at the SUU Bookstore. Inexpensive used or loose-leaf copies may be found in other places. Don't get the 8th edition because the homework is completely different between editions!

Software: This section is designated as an Excel based course. We'll use both Microsoft Excel and LibreOffice Calc. The latter is free and available for download (Mac, Windows, Linux) at www.libreoffice.org. Microsoft Office 365 (a cloud-based and deprecated version) is available to all SUU students on their MySUU portal page. If you have a MacBook I strongly recommend LibreOffice. Various SUU computer labs have Excel. Please bring your laptop to class.

Course objectives (learning outcomes): As a core general education course, this class will give students opportunities to develop skills with respect to SUU Quantitative Literacy learning outcomes. These outcomes are:

1. Communicate: Use correct terminology and proper notation to explain quantitative or mathematical relationships (equations, graphs, diagrams, tables, data) and to support an argument, assertion, or purpose using quantitative or mathematical evidence.
2. Mathematization: Convert quantitative or mathematical information into appropriate mathematical representations and/or models such as equations, graphs, diagrams, or tables, including making and evaluating important assumptions as needed.
3. Calculation: Use algebraic skills and techniques to solve problems, including the ability to identify and correct errors in calculations and understanding the role and proper use of technology in assisting with calculations.
4. Analysis: Draw appropriate conclusions through quantitative or mathematical analysis of data or models, including understanding and evaluating important assumptions in order to recognize the limits of the analysis.
5. Application: Solve concrete and abstract problems across multiple disciplines.

AI: Generative artificial intelligence (AI) tools, such as ChatGPT, BingAI, Claude, et al, may be used on h/w assignments in this class, but with some caveats:

• • AI is not infallible.
  • AI is not allowed for extra credit assignments.
  • You will not be permitted to use AI in the Testing Center, where your weekly quizzes are given.
  • You will not be permitted to use AI during the final exam, which is proctored by the instructor.
  • This means that at most 1/3 of your grade can be assisted by AI, so if you use AI to do your homework, you are not learning statistics and will likely fail your quizzes, final, and hence the entire course.

Homework Policy: Homework is found on the Assignments tab in Canvas, and should be completed prior to the due date/time. Don't fall behind---you won't be able to catch up! You may collaborate on homework (but not merely copy each others answers). Homework will usually be due on Tuesday nights. I won't accept late homework (in any event only your best 10 assignments will be counted). Please go to Modules--->Before you begin and read/view the homework procedures. 

Quizzes: Instead of mid-term exams, there will usually be one quiz per week, offered on Wednesdays in the SUU Testing Center, from 8:30am until 8:00pm. Students that are sick/quarantined or otherwise unable to attend the Testing Center may take a remote version of the quiz using Canvas while being proctored on Zoom by me. Times will be determined as needed. I would appreciate if you notify me in advance about using this option so I can schedule it. No late or make-up quizzes will be given unless arrangements are made beforehand. On the plus side, only your best 10 quiz scores will count towards your grade.

Grading: There are 300 points, comprising 100 from your best ten HW, 100 from your best ten quizzes, 100 from the final exam (and I'll add in any extra credit on top). The usual college grading scale will apply (93%=A, 90%=A-, 87%=B+, 83%=B, 80%=B-, etc.)

Extra Credit:

(individual effort---no AI or collaboration on extra credit---cite all source material--see Assignments page for details)

1.) Students pointing out obvious mistakes or typos from my lectures, handouts, etc. are eligible for one point per error discovery (only the first person to report the error gets the point). Also I will give a point for any particularly erudite student comment or observation (ongoing over the semester).

2.) Derive the variance (should be   ) for a uniformly distributed random variable having equiprobable outputs. (4 points)

3.) Do the handout (whether certain random variables are or are not normally distributed. (8 points)

4.) There are two extra credit exercises from Chapter 6. (2 points each)

5.) There are two mini-projects to be done in class for variable extra credit (up to 4 points each).

More Extra Credit may be announced in class

Daily lecture and homework schedule:

Homework for Monday--Friday lectures is usually due the following Monday at midnight. The cells colored in boldface cyan (below) indicate material not found in our textbook, but is supplemental (and required). Please note that weekly quizzes are on the Assignments tab of this Canvas course.

Homework is found on the Assignments tab in Canvas, and should be completed prior to the due date/time. Homework will usually be due on Tuesday nights. I won't accept late homework (in any event only your best 10 assignments will be counted).

No late or make-up quizzes will be given unless arrangements are made beforehand.

Attendance: This is a four hour class, with (longish) TR lectures each week. Wednesdays are reserved for quizzes (see below). The SUU community places a high priority on your health and the health of those around you. If you are symptomatic or you have an underlying health condition, you are not required to come to a face-to-face class. However, your engagement in this class is required and you can participate by attending face-to-face (if you are healthy) or viewing class videos asynchronously after the fact (on Media Gallery). SUU rules say wearing a mask is optional for face-to-face attendance.

Prerequisite

Prerequisite: A recent (within the last two years) Math ACT of at least 23, or Accuplacer Elementary Algebra score of 100, or ALEKS PPL score of 50, or a grade of C or better in Math 1010 within the last two years.

Zoom and Lecture Recordings

Zoom: There will be a live class Zoom session for this section (link is on Canvas). Previously recorded class lectures will be posted to the Media Gallery tab of this Canvas shell. I will have live Zoom sessions available during office hours (days/times and channel listed above).