STAT 216 - Introduction to Statistics
- Contact information
- Course description and learning outcomes
- Required textbook and supplemental readings
- Course information - syllabus, calendar, and project instructions
- Course resources
- Data sets
- Various apps for potential project data collection
See your D2L Announcements page for your instructor's contact information.
Interim Course Student Success Coordinator
Office: Wilson 1-145
Course Student Success Coordinator
Office: Wilson 2-263
Stat 216 is designed to engage you in the statistical investigation process from developing a research question and data collection methods to analyzing and communicating results. This course introduces basic descriptive and inferential statistics using both traditional (normal and t-distribution) and simulation approaches including confidence intervals and hypothesis testing on means (one-sample, two-sample, paired), proportions (one-sample, two-sample), regression and correlation. You will be exposed to numerous examples of real-world applications of statistics that are designed to help you develop a conceptual understanding of statistics. After taking this course, you should be able to:
- Understand and appreciate how statistics affects your daily life and the fundamental role of statistics in all disciplines;
- Evaluate statistics and statistical studies you encounter in your other courses;
- Critically read news stories based on statistical studies as an informed consumer of data;
- Assess the role of randomness and variability in different contexts;
- Use basic methods to conduct and analyze statistical studies;
- Evaluate and communicate answers to the four pillars of statistical inference: How strong is the evidence of an effect? What is the size of the effect? How broadly do the conclusions apply? Can we say what caused the observed difference?
MUS Stat 216 Learning Outcomes
- Understand how to describe the characteristics of a distribution.
- Understand how data can be collected, and how data collection dictates the choice of statistical method and appropriate statistical inference.
- Interpret and communicate the outcomes of estimation and hypothesis tests in the context of a problem.
- To understand the scope of inference for a given dataset.
Introduction to Statistical Investigations (ISI) by Tintle, Chance, Cobb, Rossman, Roy, Swanson, and VanderStoep (Wiley, 2016). MSU negotiated a reduced price for the textbook available only through the MSU Bookstore (ISBN -9781119385943), and offers a custom e-textbook that includes videos for each section (videos are not required for the course). If you prefer to purchase the custom e-textbook alone, you may purchase it from the MSU Bookstore or purchase it from VitalSource.
You may purchase either the print textbook (used or new) or an e-textbook, but you must have access to the textbook during each class period. Other materials, such as readings and assignments, will be downloaded from D2L.
- Probability reading: Supplement to Section P.3
- Normal distribution reading: Supplement to Section 1.5
- Bootstrapping reading: Supplement to Sections 2.2 and 3.3
- Fall 2019 Syllabus
- Fall 2019 Course Calendar:
- Project information:
- How to succeed in an active learning course
- Stat 216 YouTube Channel
- ISI Resources:
- Tableau Resources:
- Christian Stratton's Conditional Probability Visualization Applet
- Math Learning Center (MLC) is open 9 am to 7 pm Monday - Thursday and 9am to 5pm Friday. Do use it as a resource! Solutions to the textbook explorations are available in a binder to use while you're in the MLC.
- We use Brightspace (D2L) to organize the course, so log in and find your section.
- If you have any problems logging in, read their password help page.
Link to ISI datasets. Data sets for course explorations, investigations and assignments are available below.
- World Bank Indicators (WorldBankIndicators.xlsx)
- Current Population Survey from 1985 (cps.csv)
- Nearsighted children study (ChildrenLightSight.csv)
- Roller Coaster Data (RollerCoasters.csv) and Description
- Gapminder data (gapminder.csv) from gapminder R package
- Head injuries in alpine skiers and snowboarders (HeadInjuries.csv)
- New Jersey Prisoners (NJPrisoners.xlsx)
- Arsenic Levels in New Hampshire (arsenic.txt) - Bootstrapping supplement
- Polar bear weights (polarbear.csv) or Excel file
- Tuition costs (Tuition.csv)
- Birth weight data (Birth_Weights.csv) - Quiz 4
- OkCupid data for Women (OkCupidWomen.txt) and for Men (OkCupidMen.txt) - Regression and Correlation activity
- Contractor audits (Audits.csv)
- Murderous nurse (Murderous_Nurse.csv) - Exploration 5.1 data
- Peanut allergies (PeanutAllergy.csv) - Assignment 5 data
- CloseFriends (CloseFriends.txt) - Exploration 6.3 data
- Memorizing words (Chap6 Investigation.txt)
- Staring at drivers (StaringDrivers.xlsx) - Assignment 6 data
- JJvsBicycle (JJvsBicycle.txt) - Exploration 7.2 data
- Auction (Auction.txt) - Exploration 7.3 data
- E. coli time (Ecoli-time.txt) and E. coli sand (Ecoli-sand.txt)