Overview

Professor: Joe Roith

Office: 403 Regents Hall of Mathematical Sciences (RMS)

Telephone: (507) 786-3681

Email: roith1@stolaf.edu

Note: I will respond to emails as quickly as possible during the week before 5 PM. I may respond to emails in the evenings or on weekends, but do not rely on it.

Drop-in office hours: You can book appointment slots with me on MWF 1 - 2 PM or TTh 9:00 - 10:30 AM. Each appointment slot is 15 min.

You may also schedule an appointment with me (check my calendar for availability).

Class meetings: We will meet online only from 2/15 - 2/19 during our regularly scheduled class time, MWF 9:05 - 10 AM (Section A) and 10:45 - 11:40 AM (Section B). See below for more information about class format.

Textbook: Seeing Through Statistics (4th edition), Jessica M. Utts

Companion website: www.cengage.com/UttsSTS4e

Link to the “real” syllabus: Shadow Syllabus for all your classes

Course technology:

  • Moodle: All course files and information will be posted on our Moodle site. Check Moodle daily for updates!

  • Zoom: Class meetings will be held via video conference. Recordings can be made available by prior request and my approval.

  • Google Meet: This will be the primary mode for my virtual office hours.

  • Google Docs and Spreadsheet: I will occasionally ask you to work in small groups and submit collaborative work.

  • Jamovi: A free statistical software we will use throughout the semester. You will be expected to download and install Jamovi on your personal device. Speak to me if you have restrictions on downloading or installing. I will provide more information and guidance in class.

  • Other: We will use other online resources, applets, and software throughout the semester. Please do not hesitate to seek help with using any of these technologies.

Course Format

This will be another challenging semester for both students and professors. With so much uncertainty, there are bound to be plans that fall through and changes that need to be made. With that in mind, this is how I envision the structure of our course at this moment.

  1. During the first week we will meet online through the Zoom link above. And at some point we may (but hopefully not) be required to go back online. I will post topics, assignments, and expectations weekly. Be prepared to attend virtually and contribute to our conversations. During the first week of class you can expect:

    • Textbook and article readings

    • Zoom lectures

    • An online lab component

    • Worksheets, activities, and small group discussions completed during Zoom meetings

    • Homework and Moodle quizzes

  2. Once we are able to meet in person, much of the format will stay the same. Our room is large enough to hold everyone at once while maintaining a safe distance. This means class should be fairly normal, we will hold discussions, complete activities, work on homework, and I will lecture. It is essential that you show up to in person sessions with work completed and prepared to participate. Virtually attending an in person meeting will be available for those who require it (quarantined, etc.) and must inform me prior to class when and why you cannot attend in person.

Course description

This course is an introduction to the principles of statistical thinking in the spirit of the liberal arts. Students will learn the language, practical applications, and concepts involved in statistical reasoning. Statistics is the science of learning from data. By now you are aware that vast amounts of data are collected every moment in a variety of settings like political polls, clinical trials, stock markets, and social media user metrics to name just a few. Statistical methods are necessary to extract information, create predictions, and make evidence based decisions using data.

This is not a course in mathematics. Although we use mathematics, statistics is quite different. Be prepared to think and read critically in this class. In addition, there is a language and vocabulary of statistics that is important to use properly.

Course objectives

  1. To explore how statistics assists in understanding and reasoning with the vast amounts of data produced by society.

  2. To become a critical consumer of statistical data, scientific reports, and their conclusions.

  3. To make decisions under uncertainty.

  4. To design a data collection protocol, collect data, analyze the data to answer research questions, and summarize the conclusions using visualizations, summary statistics, and clear exposition.

  5. To learn how to perform the above using the software program SPSS.

Features of this course

Philosophy

This course and this textbook are centered on the idea that you will better understand and retain important statistical concepts if you build your own knowledge and practice using it, rather than by memorizing and regurgitating a set of facts. In order to actively construct knowledge in statistics, you must:

  • Engage in the material and think carefully about it; there are rarely rote, black and white solutions in statistics.

  • Attend class, participate in class, and complete pre-class preparations.

  • Attend labs, develop skills using SPSS, and transfer those skills to data beyond homework and test situations.

  • Expect weekly homework sets, occasional table quizzes, and longer projects which allow you to pull your knowledge together.

Grades

Your course grade will be determined as follows:

Category Weight
Homework Assignments 15%
Online labs, in class worksheets, and participation 10%
Quizzes 15%
Midterm Exams (2) 15% each
Group Project 15%
Final Exam 15%

College wide grading benchmarks can be found at: http://catalog.stolaf.edu/academic-regulations-procedures/grades/

Homework

Weekly homework assignments will be assigned and submitted through Moodle as a PDF document. Homework assignments are designed to give you practice applying new statistical concepts to new data contexts. Homework will be drawn from the exercises at the end of each chapter as well as additional questions.

  • You are encouraged to discuss problems together, but each person must hand in their own work.

  • If you work with other students, you must note this fact (along with the students’ names) on your assignment.

  • You must show your work for full credit.

  • Homework is due by 11:59 PM on the due date (generally Friday). Late homework assignments will not be accepted. This includes late assignments due to technical issues. Plan ahead. The lowest homework score will be dropped.

  • I expect that you will start soon after receiving the assignment. The assignments are definitely not designed to be one-night jobs.

  • Short reading reflections (less than one page) will occasionally be due, especially at the beginning of the semester. These will be graded on your ability to thoughtfully and concisely respond to a specific reading. Please keep to the length restrictions.

Online labs, in class worksheets, and participation

I will post interactive tutorials each week to walk you through examples using real world data and introduce you to some new technologies for analysis. At the end of each tutorial you will complete a Lab Response and submit it as a pdf to Moodle by 11:59 PM each Monday night. I will drop the lowest lab score.

We will have in class worksheets that I will occasionally collect for credit. These worksheets will sometimes be completed individually, in small groups, or as a large group. Worksheets must be submitted on Moodle by the end of the class session.

You are expected to come to class having read the assigned readings and completed assigned work. Participation in this class can take several different forms: participation in discussions, contributing to small group activities, peer reviews of projects, attending office hours, and plenty of other ways. Be prepared to actively engage in all of these areas for full participation credit.

Moodle Quizzes

Short quizzes will be posted weekly on Moodle (Usually closing on Thursdays). You will have 30 mins and 2 attempts to complete each quiz. You are allowed to use slides, notes, textbook, and any other materials for these quizzes. You must complete the quizzes independently. I will drop the lowest quiz score.

Exams

The midterm and final exams will focus on your abilities to interpret results, to express an understanding of statistical concepts, and to engage in statistical thinking on open-ended questions. They will not focus on plug-and-chug mathematics or hairy mathematical proofs. Make-up exams will be granted only under very special circumstances, and only if arranged in advance.

Both exams will be administered through Moodle with one hour to complete and one attempt. The midterms will each be available over a two day weekend for you to take at your leisure. You will be allowed to use class materials on these exams. The final exam will be administered during the scheduled final time.

Project

A group project will be completed by the end of the semester. These are team-oriented tasks that will require hypothesis generation, data analysis, critical thinking, and thoughtful presentation of statistical results. Additional information on the project, including due dates will be provided later on in the semester.

Classroom behavior

I am happy to take any questions in class. To facilitate an open learning environment, please respect each other. Raise your hand (virtually), listen when others are talking (including me), and avoid distracting or rude behavior. Please turn off your cell phones, and refrain from using your phone during class. During Zoom meetings, please leave your video on when possible, virtual backgrounds are encouraged! Lecture slides or notes will be posted on Moodle.

Covid-19 Guidelines

When we meet in person, you are required to wear a mask at all times, sit in your assigned seats, and maintain a distance of 6 feet from others in the class. If you forget a mask, I will ask you to return to your room to get one. If you refuse to attend class with a mask, I will ask you to leave for the day and the appropriate Deans will be contacted.

For your safety, as well as mine, my family’s, my colleagues and their families, these rules are non-negotiable.

Available help

You can all be successful in this class! If you are struggling or if you’re feeling good about things but have some questions, there are several resources:

Come see me at my drop-in office hours, or make an appointment, or see if my door’s open

  • Connect with your classmates.

  • I will work with the TAs to set up some extra virtual office hours and study space every week. More details will be posted on Moodle.

  • Visit the Academic Support Center if you want to improve your general study skills and habits.

Tutor Requests

Tutors are available through the CAAS. I will only approve a tutor request once you have taken full advantage of resources available to you in and out of class. You are encouraged to work with classmates on homework and form study groups. You are required to attend SI sessions and office hours prior to requesting a tutor. Additionally, you must discuss with me your goals for working with a tutor prior to placing a request with ASC. Absence from class will prevent you from obtaining or continuing with an assigned tutor.

Notes and Statements

Note about Disabilities

I am committed to supporting the learning of all students in my class. If you have already registered with Disability and Access (DAC) and have your letter of accommodations, please meet with me as soon as possible to discuss, plan, and implement your accommodations in the course. If you have or think you have a disability (learning, sensory, physical, chronic health, mental health or attention), please contact Disability and Access staff at 507-786-3288 or by visiting wp.stolaf.edu/academic-support/dac.

Statement of Inclusivity

In keeping with St. Olaf College’s mission statement, this class strives to be an inclusive learning community, respecting those of differing backgrounds and beliefs. As a community, we aim to be respectful to all citizens in this class, regardless of race, ethnicity, religion, gender or sexual orientation.

I am committed to making course content accessible to all students. If English is not your first language and this causes you concern about the course, please speak with me.

Note about Academic Integrity

Plagiarism, the unacknowledged appropriation of another person’s words or ideas, is a serious academic offense. It is imperative that you hand in work that is your own, and that cites or gives credit to others whenever you draw from their work. Please see St. Olaf’s statements on academic integrity and plagiarism at: https://wp.stolaf.edu/thebook/academic/integrity/. See also the description of St. Olaf’s honor system at: https://wp.stolaf.edu/honorcouncil/

St. Olaf’s Academic Integrity Policy, including the Honor System, is an integral part of your academic experience. I consider any violation of this code to be extremely serious and will handle each case appropriately. Here are some guidelines for this class. They do not cover all eventualities so if you have any doubts about a course of action you can ask me.

  • Homework assignments may be done in collaboration with other students (this is highly encouraged). However, the final product must written by you, in your own words, unless group assignments have been specifically allowed.

  • In no event can you copy answers from another student, a website, solutions manuals, or elsewhere.

  • When you sign your pledge on an exam that you have “neither given nor received assistance, and seen no dishonest work” I treat your signature as your solemn pledge that all your actions have been honorable. For example, if we have a take-home exam, you are assuring me that you shared no information with others, that you did not solicit or receive help from anyone besides me, etc.

  • Don’t treat the honor code lightly; if you’re in doubt about a possible violation, ask me.

Schedule

Tentative Outline of topics: The following table provides a rough sketch of the topics we’ll cover during specific weeks, along with the associated reading assignments in our textbook:

Week Topics Book Chapter
Week 1: Feb. 15 - 19 Introduction to Stats and stats in the media 1 & 2
Week 2: Feb. 22 - 26 Consuming and collecting Data 3 & 4
Week 3: Mar. 1 - 5 Sampling and Studies 5, 6 & 7
Week 4: Mar. 8 - 12 Summarizing and visualizing data, Normal distribution 8 & 9
Week 5: Mar. 15 - 19 More visualizing and review 9
Rest Day: Wed March 17
Exam #1 (Sat March 20 - Sun March 21)
Week 6: Mar. 22 - 26 Categorical Relationships 12
Week 7: Mar 29 - Apr. 2 \(2 \times 2\) tables 12 & 13
Week 8: Apr. 5 - 9 Relationships and Correlation 10 & 11
Rest Day: Wed April 7
Week 9: Apr. 12 - 16 Probability and Samples 14 & 19
Week 10: Apr. 19 - 23 Samples and Review 19
Exam #2 (Sat April 24 - Sun April 25)
Week 11: Apr. 26 - 30 Confidence intervals 20 & 21
Rest Day: Tues April 27
Week 12: May 3 - 7 Hypothesis testing 22 & 23
Week 13: May 10 - 14 Hypothesis testing (cont.)
Week 14: May 17 “Statistical Significance” and open topics 24
Final Exam Section A (9:05) - Mon, May 24, 9:00-11:00 AM
Section B (10:45) - Fri, May 21, 9:00-11:00 AM

Other notable dates

  • Fri, Feb 26 - Last day to add full semester course

  • Mon, Mar 8 - Summer Session 1 & 2 registration opens

  • Mon, April 5 - Mon, April 12 - Quiet Week for Advising

  • Thurs, April 15 - Last day to drop or S/U full semester course

  • Tues, April 20 - Fri, April 23 Summer and Fall Registration

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