STAT 216 - Introduction to Statistics
Two "textbooks" are required for this course:
- Montana State Introductory Statistics with R - free online textbook
- STAT 216 Coursepack - workbook with reading guides and in-class activities, purchase here
Please read through the syllabus linked below for more details about the course.
Classroom Format and Organization
Stat 216 will meet 3 times per week. Each week, students will:
- read assigned sections of the online textbook and watch videos on that week’s content prior to attending your assigned in-person class day, including concept check video quizzes embedded in the videos,
- meet with your fellow students in your assigned classroom three class periods per week for in-class group activities and discussion (Mondays and Wednesdays) and in-class Rstudio labs (Friday),
- complete one assignment in Gradescope.
See your D2L Announcements page for your instructor's contact information.
Course Student Success Coordinator
Course Student Success Coordinator Assistant
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 using statistical software;
- 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.
Data sets for course activities and assignments are available below.
- World Bank Indicators (WorldBankIndicators.csv))
- Montana fishing log (FishingLog.csv)
- Roller Coaster Data (RollerCoasters.csv) and Roller Coaster Description
- Gapminder data (gapminder.csv) from this Github repository
- New Jersey Prisoners (NJPrisoners.csv)
- Arsenic Levels in New Hampshire (arsenic.txt) - Bootstrapping supplement
- Polar bear weights (polarbear.csv)
- Tuition costs (Tuition.csv)
- Birth weight data (Birth_Weights.csv) - Quiz 4
- OkCupid data for Women (OkCupidWomen.txt) OkCupid data 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
- Staring at drivers (StaringDrivers.csv) - Assignment 6 data
- Life expectancy (LifeExpectancy.csv)
- Ski resorts (SkiResorts.csv)
- Tennis ball data (TennisBallData.csv) - Quiz 7 data
- Pregnancy test (PregnancyTest.csv)
- Activity tracker data (step.csv) - Assignment 8
Data Sets for Course Activities and Labs
- Activity 2a and 2b: American Indian Address (Becenti.csv)
- Activity 3a: Nearsighted children study (ChildrenLightSight.csv)
- Activities 3b, 4a, 4b: IMDb movies data set (Movies2016.csv)
- Lab Week 3: IPEDS (IPEDS_2018.csv)
- Lab Week 4: Penguins (Antarctica_Penguins.csv)
- Exam Reviews: Exam Review Data (ExamReviewData.csv)
- Activities 6 and Week 6 Lab: Helper-hinderer study (infantchoice.csv)
- Activities 7a and 7b: Male boxers (Male_boxers_sample.csv)
- Activities 8a and 8b: Good Samaritan (goodsam.csv)
- Lab Week 8: Iliad Fatalities (iliad.csv)
- Lab Week 8: Edibility of Mushrooms (mushrooms.csv)
- Activities 9a and 9b: Head injuries in alpine skiers and snowboarders (HeadInjuries.csv)
- Lab Week 9: Glycemic control in diabetics (diabetes.csv)
- Activity 11a: COVID-19 and air pollution (AirPollutionCOVID.csv)
- Activity 11b: Color Interference (interference.csv)
- Lab Week 11: Swearing data set (pain_tolerance.csv)
- Activity 12: Winter weather patterns (SnowfallByWeatherPattern.csv)
- Activity 12: Behavior and Performance (rude.csv)
- Lab Week 12: Hiking Trail Weight (Trail_Weight.csv)
- Activity 13a: Diving Penguins (Diving_Penguins.csv)
- Activity 13b: Golf Driving Distances (golf.csv)
- Lab Week 13: COVID Vaccinations (covid_vaccinations.csv)
- Lab Week 13: Big Mac Index (big_mac_adjusted_index_22.csv)
- Activity 14b: Titanic (titanic.csv)