Overview

Professor: Joe Roith

Office: 403 Regents Hall of Mathematical Sciences (RMS)

Telephone: (507) 786-3681

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: Tue 1:15 - 2:45 PM, Wed 2 - 3 PM, Thu 9 - 10:30 AM.

You may also schedule an appointment with me (check my calendar for availability) or drop by and talk if my door is open.

Class meetings: MWF 12:55 - 1:50 PM TOH 182

Course computing: R and RStudio

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

Available free online or from Amazon or the bookstore for under $10

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

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

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

To learn ways of investigating questions involving statistical concepts.
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
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.
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.
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 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.
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.

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 in 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, attend SI sessions, ask friends for help, post to our computing issues thread, 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!

Our classroom (TOH 182) is equipped with computers, but feel free to use your own device. COMPUTERS ARE TO BE USED FOR EDUCATIONAL PURPOSES ONLY! Students who use computers during class time for purposes beyond Stat 212 (email, internet surfing, paper writing, etc.) may have their computer privileges revoked and participation points reduced.

Grades

Your course grade will be determined as follows:

Grade Subgroup	Weight
Homework Assignments	15%
Quizzes & Independent Homework	15%
Midterm Exams (2)	20% each
Group Project	10%
Final Exam	20%

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. The homework assignments are long, so you should work 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 will be graded in large part for completeness, with some specific answers spot checked.
Homework is due by 11:59 PM on the due date. 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 both the RMarkdown (.Rmd) file and knitted pdf file to Moodle.
At the end of the semester, your lowest homework score will be dropped.
There will be 1-2 “Independent Homework” assignments in which you must sign an Honor Code pledge that you worked alone. This is to encourage everyone to develop their own skill set for analyzing data with R.

Quizzes and Independent Homework

We will have several Moodle quizzes in between exams. You will have limited time to complete these quizzes and are expected to work alone. There will also be an independent homework assignment using R around midterm. This is also meant to be completed alone and assess your progress using the statistical software. At the end of the semester, your lowest quiz score will be dropped.

Class Preparation and Participation

You are expected to come to class having read the assigned readings and completed assigned work. You will be asked to show work on the board, work with classmates on in-class activities, and engage in classroom assignments. Course grades may be adjusted based on class participation and attendance.

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, listen when others are talking (including me), and avoid distracting or rude behavior. Please turn off your cell phone ringers, and refrain from using your phone during class.

Exams

The midterm and final exams will focus on your abilities to use the statistical software, 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.

Project

A group project will be completed by the end of the semester. These are team-oriented tasks that will require more extensive writing, data analysis, and critical thinking than a typical homework assignment. The project will involve both a verbal presentation at the midway point and a written report at the end. No projects will be accepted after the due date.

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.
CIR Office Hours are Mon 1-2 pm; 7:30-8:30, Tues 2-3pm; 8-9pm, Wednesday 7:30-8:30pm, Thursday 3-4pm; 7:30-8:30pm, and Sunday 7:30-8:30pm.
Visit the Academic Support Center if you want to improve your general study skills and habits.

Supplemental Instruction

This section of Stat 212 is supported by Supplemental Instruction (SI). SI is a series of weekly review sessions for students lead by the SI Leader Alden Aaberg. 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.

Alden’s SI sessions

Sundays: 8 - 9 PM in TOH 182
Tuesdays: 7 - 8 PM in TOH 182
Thursdays: 7:15 - 8:15 PM in TOH 182

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 Sections
Day 1	Introduction to data and RStudio
Week 2	Introduction to data; Foundations for inference	1.1-1.7; 2.1-2.4
Week 3	Hypothesis testing: Randomization tests for two groups	2.1-2.4
Week 4	Hypothesis testing: Randomization tests for one group
Week 5	Central Limit Theorem; Normal distribution; Applying the Normal model	2.5-2.7
	Exam #1 (Friday, March 6)
Week 6	Confidence intervals; Inference for one proportion	2.8, 3.1
Week 7	Inference for two proportions; Two-way tables, Chi-Square tests	3.2-3.4
Week 8	SPRING BREAK (March 21 - 29)
Week 9	One-sample t-tests; Paired data	4.1-4.2
Week 10	Two-sample t-tests; ANOVA	4.3-4.4
	Exam #2 (Friday, April 10)
Week 11	Bootstrap; Simple linear regression - line fitting & least squares	4.5; 5.1-5.2
Week 12	Simple linear regression - outliers; inference	5.3-5.4
Week 13	Project work
Week 14	Multiple linear Regression; Model selection	6.1-6.2
Week 15	Multiple linear Regression - checking assumptions	6.3
Week 16	Wrap up and Review
	Final Exam: Friday, May 15, 2 - 4 PM

https://xkcd.com/552/

STAT 212b: Statistics for the Sciences

Course Syllabus

Spring 2020