Overview

Professor: Joe Roith

Office: 403 Regents Hall of Mathematical Sciences (RMS)

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 M/F 2 - 3 PM or T/Th 9:30 - 11 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 8/21 - 9/2 during our regularly scheduled class time, MWF 9:05 - 10 AM. 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 a 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 two weeks we will meet online through the Zoom link above. Many of these classes will have a lecture format to set a good foundation of statistics. Many will be include discussion based on assigned readings. There will also be worksheets, online tutorials, and homework that I expect you to complete on your own. I will post topics, assignments, and expectations weekly. Be prepared to attend virtually and contribute to our conversations. During these two weeks you can expect:

    • Textbook and article readings

    • Zoom lectures

    • Interactive online tutorials

    • 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. The class will be split into three groups (assigned at a later date), each meeting one day a week in person. During these in person meetings, we will hold discussions, complete activities, work on homework, and I will occasionally present new material. It is essential that you show up to in person sessions with work completed and prepared to participate. Virtually attending an in person meeting outside of your group day will be possible but not required. Attending an “off” day will not count for your participation that week. After 9/2 you can expect:

    • Textbook and article readings

    • Short lecture videos and slides

    • Interactive online tutorials

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

    • Homework and Moodle quizzes

    • Project work in small groups

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 statistical software.

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 (both in person and virtually), participate in class, and complete pre-class preparations.

  • Complete online labs/tutorials, develop skills using technology, and transfer those skills to real data beyond homework and test situations.

  • Expect weekly homework sets, occasional Moodle quizzes, and a project which will 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 10%
Midterm Exams (2) 15% each
Group Project 15%
Final Exam 20%

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

All homework assignments, lab responses, and quizzes will be due each week by 11:59 PM Friday night. The best advice I can give is to just not wait until Friday night to submit everything.

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 each Friday night. Late homework assignments will be accepted within reason (24 hrs) for the first offense, not accepted afterwards. 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 Friday 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.

Participation in this class can take several different forms: participation in discussions, contributing to small group activities, completing daily class summaries/class diary (I’ll explain more when we meet in person), 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. 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. slides Exams will be administered through Moodle, with one hour to complete and one attempt. You will be allowed to use class materials on the exams.

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, no matter how trivial they may seem. 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.

We will all need to get used to this situation, and I will allow you some grace as we fall into a routine. But 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. Call/text/Zoom them to ask questions, work on homework or labs together. There is a participant list on Moodle if you’re not sure who your classmates are.

  • This section of Stat 110 is supported by Supplemental Instruction (SI). SI is a series of weekly review sessions for students lead by the SI Leader: Josh Berkesch. SI is provided for all students in the course who want to acquire effective learning strategies, develop a stronger understanding of course material, and improve their grades. In most cases, regular SI attendance results in at least one letter grade higher than if a student never attended. SI is a structured and collaborative learning environment where students can further engage with course material and enhance their learning. SI sessions will start the first week of class and end the last day of classes. Faculty support and SI are the primary resources for students enrolled in this class.

Session Days Times
Sundays: 3 - 3:40 PM
Tuesdays: 7:15 - 7:55 PM
Thursdays: 7:30 - 8:10 PM

A link to these Zoom sessions will be available on Moodle.

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 and Antirascism

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 acknowledge that my gender, race, and education contributes to my privilege hazard. I am committed to learning of and about inherent biases in my teaching and my field, and while they may not be intentional, they are not acceptable. I hope you will be comfortable approaching me if you feel any aspect of this class inhibits your ability to be equally heard and represented. Statistics can be a powerful tool to place a spotlight on the societal inequities we face, but we also need to confront the ways that data are racist, sexism, and classist. Throughout this course, we will use and discuss data that may make you feel slightly uncomfortable, but the goal is to approach important, complex questions through empirical data analysis.

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.

  • Exams and quizzes MUST be completed independently. Any suspicion of collaborative work will result in reporting to the Honor Council and potential for immediate failure of the course.

  • 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.

Tentative 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
Day 1: 8/21 Introduction to Stats 1
Week 2: 8/24-8/28 Consuming and collecting Data 2 & 3
Week 3: 8/31-9/4 Sampling and Studies 4, 5 & 6
Week 4: 9/7-9/11 Summarizing and visualizing data, Normal distribution 7, 8 & 9
Week 5: 9/14-9/18 More visualizing and review 9
Exam #1 (Friday, September 18)
Week 6: 9/21-9/25 Relationships and Correlation 10 & 11
Week 7: 9/28-10/2 Categorical Relationships, \(2 \times 2\) tables 12
Week 8: 10/5-10/9 Testing Two Categorical Variables 13
Week 9: 10/12-10/16 Probability, Sample Distributions, and review 14 & 19
Exam #2 (Friday, October 16)
Week 10: 10/19-10/23 Confidence intervals 20 & 21
Week 11: 10/26-10/30 Confidence intervals continued, Hypothesis tests 22
Week 12: 11/2-11/6 Hypothesis tests continued 22 & 23
Week 13: 11/9-11/13 Additional topics, Project peer reviews
Week 14: 11/16 Review
Final Exam: Thursday, November 19, 9-11 AM
https://xkcd.com/552/

https://xkcd.com/552/