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Schedule and Syllabus

Welcome to course website for MATH 256 - Introduction to Probability and Statistics! On this page you can find all materials we’ll use throughout the semester, starting with the syllabus linked below:

You can find course content by scrolling, or by using the navigation bar in the upper left. Please note that some material may not be posted until we’ve reached that point in the course.

In-class Labs

Throughout the semester we will practice applying key concepts in applications involving real data during in-class labs. During labs, you will work collaboratively in a group of 2-3 students to progress through a guided data analysis. You will have the majority of class to work independently with your group, and we will will often go over a few key questions towards the end of class.

Homework Assignments

Homework will be assigned approximately every two weeks. These assignments can be lengthy, so please start them sooner rather than later. Your responses should be submitted electronically via Canvas as either a scanned or typed document. Please double-check to make sure your uploads are legible.

  • Homework #1: 1.2, 1.5, 1.10, 1.14, 1.18, 1.21, 1.28, 1.30, 1.40, 2.28, 2.30, 2.31, 2.34
    • Due date: Tuesday 8/31 at 11:59pm
    • A brief set of solutions will be posted to Canvas on Wednesday morning (to help you prepare for Exam #1)
  • Homework #2: 3.3, 3.8, 3.9, 3.14, 3.17, 3.24, 3.26, 3.29, 3.33, 4.6, 4.8, 4.18, 4.20, 4.22
    • Due date Friday 9/24 at 11:59pm
  • Homework #3: 5.11, 5.12, 5.14, 5.21, 5.22, 5.25, 5.30, 6.5, 6.10, 7.7, 7.11, 7.12
    • Due date: Wednesday 10/20 at 11:59pm
  • Homework #4: 6.23, 6.24, 6.26, 6.28, 7.23, 7.25, 7.27, 7.28, 7.30
    • Due date: Friday 11/5 at 11:59pm
  • Homework #5: 6.33, 6.34, 6.35, 6.36, 6.37, 7.37, 7.38, 7.39, 7.42
    • Due date: Wednesday 11/17 at 11:59pm

Exam Dates and Information

There will be 4 midterm exams covering the following topics:

  • Exam 1 - Descriptive statistics, graphical presentations, and study design (Ch 1 and 2)
    • Exam #1 will take place Thursday 9/2 during class - you will need R for the exam.
    • Click here for a practice exam
  • Exam 2 - Probability and random variables (Ch 3 and 4)
    • Scheduled for Tuesday 9/28 during class - you will need R for the exam.
    • Click here for a practice exam
  • Exam 3 - Statistical inference for one-sample data (Ch 5, 6.1, and 7.1)
    • Scheduled for Tuesday 10/26 during class - you will need R for the exam.
    • Click here for a practice exam
  • Exam 4 - Statistical inference for two or more groups (remaining parts of Ch 6, 7, and 8)
    • Tentatively scheduled for Thursday 11/18 during class - you will need R for the exam.
    • Click here for a practice exam

You will be given an entire class period to complete each exam, though I try to write them with a target average completion time of around 60-minutes.

All exams are “open everything” and will require you to use R.

Final Project Information

The final project due at the end of the semester (during final’s week).

Please contact me if you are having trouble figuring out a topic. Also note that an extra credit opportunity is described at the end of the project description.

Daily Graphs

Below is a compendium of the various daily graphs we’ve discussed:

Datasets

This section provides links and a brief description to datasets we will work with throughout the semester.

  • Iowa City Home Sales
    • This dataset contains information on homes sold in Iowa City, IA between 2005 and 2008. It was scraped from the Johnson County county assessor website by University of Iowa professor to help them understand the local housing market. It contains information such as the home’s sale price, assessed value, square footage, and a variety of other features.
  • Happy Planet
    • This dataset was assembled by The Happy Planet Index using data from a global survey that asks respondents questions about how they feel their lives are going. It documents the health and well-being of the inhabitants of various nations around the world.
  • Colleges 2019
    • This dataset contains select variables describing higher education institutions that primarily bachelors degrees. It was obtained from the College Scorecard, a comprehensive government database. For each institution, it includes enrollment and admissions variables, student demographics, costs and faculty salaries, as well as student outcomes such as median debt upon graduation and median 10-year salary.
  • Death Penalty Sentencing
    • This dataset comes from a widely cited study on racially biased sentencing in the Florida court system during the 1970s. Researchers collected data on all murders that took place during a felony committed in the state of Florida between 1972 and 1977. They record the race of the victim and the offender, as well as whether the offered was sentenced to the death penalty.