Overview

Professor: Joe Roith

Email: roith1@stolaf.edu

Note: I will respond to emails as quickly as possible during the week before 5 PM. I may have slower responses to emails in the evenings or on weekends.

Drop-in office hours:

  • MWF (in my office) 2 - 3 PM

  • TTh (virtual Zoom link) 9:00 - 10:30 AM.

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

Class meetings: MWF 12:55 PM - 1:50 PM in Tomson Hall 182

  • Zoom link for class sessions If you are unable to attend class due to illness or an excused absence, you may attend virtually using the previous link. Note that you are expected to attend class in person when able.

Course technology:

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

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

  • R and RStudio: This is a free statistical software used by many in the statistical and data science industry. You will use and alter code I provide as well as write your own and run it through our campus RStudio server (the link above).

  • 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.

Textbook: Introductory Statistics with Randomization and Simulation (2014), David M. Diez, Christopher D. Barr, Mine Çetinkaya-Rundel

Available free online or from Amazon for under $10

Companion website: https://www.openintro.org/stat/textbook.php

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

Course Format

We are still in the pandemic and there is a chance the following plans will change. I will do my best to provide ample notice of any major course design alterations. With that in mind, this is how I envision the structure of our course at this moment.

There will be the usual in class aspects like lectures, examples, and worksheets. A lot of the examples will done by you either individually or at your tables. I try to make my lectures based on big picture ideas and reinforcing the readings and tutorials. A lot of my time speaking in class will be working through examples in R so you have the code and understanding to perform the analysis on your own.

A large part of the content delivery for my classes are interactive online tutorials. You can expect to see 1-2 of these tutorials each week, and their completion is required before we meet. I will update Moodle weekly with the tutorial links and when they need to be completed. Everyone will move through these at their own pace, but typically they are intended to take between 15-45 minutes each. My intent is that you will find these much more engaging and easier to follow than recorded video lectures or readings.

In addition to the tutorials, I do expect you to read the textbook, participate in class worksheets, and complete homework and quizzes. In general, this is what you can expect on a weekly basis for this class:

  • Textbook and article readings

  • Lectures with slides

  • Interactive online tutorials

  • Worksheets, activities, and small group discussions completed during class

  • Homework and Moodle quizzes

  • A multi-stage project completed in groups

Course description

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 especially critical to the sciences, as they are our only real way to test theories, quantify natural phenomenon, create accurate predictions, and make evidence-based decisions.

Some of the practical ways in which statistics is used in the sciences include:

  • Medicine (monitoring patient information and history for more accurate diagnosis and prognosis)

  • Biostatistics (designing and analyzing clinical trials and epidemiological studies)

  • Bioinformatics (studying DNA data and human genetics as related to disease)

  • Actuary (performing analysis in the insurance and superannuation industry)

  • Ecology (environmental monitoring, species management, land surveying)

  • Climatology (weather forecasting using historical data)

  • Demography (studying the dynamics of human populations)

  • Psychometrics (constructing instruments for educational or psychological measurement)

  • Image processing (aiding in computer vision, facial detection, and remote sensing)

Statistics is a unique field of study as it lends itself equally to any of the areas mentioned above, but is built on its own theories and rules. We constantly evolve approaches to data collection, analysis, and interpretation. 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 learn ways of investigating questions involving statistical concepts.

  2. To develop basic skills in three key areas of statistics:

    • data collection – methods for obtaining meaningful data

    • data analysis – methods for exploring, organizing, describing, and modeling data

    • statistical inference – making decisions with data

  3. To develop an understanding of statistical concepts and an ability to interpret the results of statistical analyses and to communicate those results using clear and precise statistical language.

  4. To receive an exposure to statistical problems from a wide variety of sources (medical studies, newspaper surveys, sociology studies, etc.) and encompassing a variety of data types and collection methodologies.

  5. To obtain practical experience in study planning, data collection, and the written communication of statistical concepts through individual homework assignments and group projects.

Features of this course

Philosophy

This course is 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.

  • Become skillful at using R, a software package for exploring, modeling, and making decisions with data. You’ll have opportunities to use R inside and outside of class.

  • Expect small amounts of daily homework, decent-sized weekly homework sets, and longer projects which allow you to pull your knowledge together.

  • Most importantly, you must become comfortable with failure. Not the 0/100 on an exam type of failure, but the type that comes with having a typo in your code that prevents homework from running correctly the first time. Just know that there is no real success without failure. Mistakes are tolerated in this class because they lead to understanding. You probably won’t make the same mistake once you realize the cause. Not attempting something or giving up at the first sign of resistance will not get you very far.

Class Preparation and Participation

My expectations of you before each class:

  1. Read the appropriate sections in the textbook (reading guides are available on Moodle, but I will not collect/grade them).

  2. Complete any online tutorials assigned for that day.

  3. Read any supplemental material posted.

I expect your participation in our classes. They will often include small and large group work. Occasionally you will be asked to lead a small group on a worksheet or task. This is not meant to intimidate you, put you on the spot, or force you to be the only contributor that day. It is meant to develop your leadership skills, promote interaction with classmates, and give me a chance to hear everyone’s voice. This will be a safe environment where mistakes and uncertainty are welcome!

Computing

This course will use R extensively. Course datasets and code are easily available on the R server which can be accessed by the link on Moodle. Supplemental materials will be provided on Moodle for learning to use R. Further instructions will be provided during class.

Learning R is necessary to do statistics. There is a learning curve and you will make mistakes, it just happens (and still happens to me). It is important that you take a breath, step back, and remember to seek help to resolve your issue. Come to my office hours, ask classmates for help, and ask me questions in class. Past students have found learning R to be ultimately very useful and even fun. R is freely downloadable for both Mac and PC at https://cran.r-project.org/, and it’s available on a St. Olaf server!

Classroom behavior

I am happy to take any questions in class. To facilitate an open learning environment, please respect each other. Raise your hand, 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. 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. 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.

I will update these policies as the campus situation changes. Note that my classroom policies take precedent over the college’s.

Grading

Your course grade will be determined as follows:

Grade Subgroup Weight
Homework Assignments 20%
Quizzes 10%
Exams (3) 10% each
Project 30%
Participation 10%

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

Homework

There will be weekly homework assignments. 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 should start working through them as we go along. Many of the problems require computation. Code snippets in these cases will usually be provided.

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

  • You must show your work for full credit.

  • Homework is ALWAYS due by 11:59 PM on the posted due date (typically Wednesdays). Anything after this will be assessed a late penalty. This includes late assignments due to technical issues. Plan ahead.

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

  • Homework is completed using RMarkdown and you are required to upload the knitted pdf file to Moodle and have a copy of your RMarkdown (.Rmd) file in your Submit folder in the R server.

What if I don’t finish my homework? What if I’m falling behind?

Turn in whatever you have completed by the due date (if any), and send me an email to discuss options. Without prior approval, homework turned in after the key is posted (approximately 24 hrs after due date) will receive at most 50% credit.

If you have extenuating circumstances (physical or mental health, death or illness of a loved one, etc) which you anticipate may result in difficulty completing assignments, please speak to me, preferably before the assignment due date. With prior approval, we can make arrangements for extending due dates.

Quizzes

We will have roughly one quiz a week that closes on Monday. You will have 1 hour to complete each quiz and two attempts. You must complete quizzes independently. You may use the textbook, notes, slides, worksheets, tutorials, or any other materials when taking the quizzes. All quizzes must be completed by 11:59 PM on their due date.

Exams

We will have three (3) exams throughout the semester administered through Moodle. These will essentially be long quizzes with only one attempt. You will have 2 hours to complete each exam and one attempt. You must complete exams independently. You may use the textbook, notes, slides, worksheets, tutorials, or any other materials when taking the quizzes. Exams will not be cumulative.

These exams will focus on your abilities to interpret results, to express an understanding of statistical concepts, and to engage in statistical thinking on several 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.

Exams will be available on Moodle to complete over the course of a weekend.

  • Exam #1: March 18 - 20

  • Exam #2: April 23 - 24

  • Exam #3: May 13 - 15

Project

The semester project will be done in an assigned group (you’ll get input for this). You will choose the dataset and research questions. The project will be split into several stages, with deadlines throughout the semester. The ultimate result will be a 5-8 page paper including background research about your topic, the results of the data analysis, graphs, tables, and conclusions about what you learned.

Participation

Participation in this class can take several different forms: participation in discussions, contributing to small group activities, contributions to worksheet keys and class study guides, attending office hours, and plenty of other ways. Be prepared to actively engage in all of these areas for full participation credit.

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 during my office hours, or make an appointment.

  • Connect with your classmates. Create study groups, test each other. Get a pet fish as a mascot and try to teach concepts to it. there’s no better way to learn something than to try and teach it.

  • Attend SI session to see more examples or get extra practice.

  • Visit TA office hours with a homework question or to meet up with other students.

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

Supplemental Instruction

This section of STAT212 is supported by Supplemental Instruction (SI). SI is a series of weekly review sessions for students lead by the SI Leader, Yuzu Mi. 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.

Sessions will be held:

  • Tuesdays: 6:00 - 6:40 PM in TOH 182

  • Thursdays: 7:00 - 7:40 PM in TOH 182

  • Sundays: TBC

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 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, sexist, 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.

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. 9 - 11 Welcome, Introduction to R, Big Picture ideas 1.1-1.3
Week 2: Feb. 14 - 18 Data basics, data collection, experiments & studies 1.4-1.6
Week 3: Feb. 21 - 25 Data visualization, randomization, hypothesis tests 1.7, 2.1-2.3
Week 4: Feb. 28 - Mar. 4 Hypothesis test examples, error types 2.3-2.4
Week 5: Mar. 7 - 11 Central Limit Theorem, Normal distributions, confidence intervals 2.5-2.8
Week 6: Mar. 14 - 18 Examples and review
Exam #1 (Fri March 18 - Sun March 20)
Week 7: Mar. 21 - 25 Single proportion, difference in two proportions, Chi-squared tests 3.1-3.3
Spring Break: Mar. 26 - Apr. 3
Week 8: Apr. 4 - 8 Chi-squared tests, one sample means, t-distribution 3.4, 4.1
Week 9: Apr. 11 - 15 Paired t-tests, difference in means 4.2-4.3
Week 10: Apr. 18 - 22 ANOVA, bootstrapping, review 4.4-4.5
Exam #2 (Sat April 23 - Sun April 24)
Week 11: Apr. 25 - 29 Regression lines, least squares, outliers 5.1-5.3
Week 12: May 2 - 6 Regression inference, transformations 5.4
Week 13: May 9 - 13 Multiple regression, project peer reviews 6.1
Week 14: May 16 Review
Final Exam Section A (9:05) - Thursday, May 19, 2-4 PM

Other notable dates

  • Mon, Feb 21 - Last day to add full semester course

  • Mon, Mar 7 - Summer Session 1 & 2 registration opens

  • Mon, April 4 - Mon, April 8 - Quiet Week for Advising

  • Thurs, April 14 - Last day to drop or S/U full semester course

  • Tues, April 19 - Thurs, April 21 Summer and Fall Registration