MathStat474

Welcome to Math 474 @ IIT by Sonja Petrović. This course is being run in person, and the material we use and all assignments are posted on Canvas. This page serves as an archive for future references, and is updated weekly by your instructor. The most useful thing here is the weekly schedule of topics, which you may wish to keep up with.

In Spring 2026, we will be using a new book - ZyBook. Please log into Canvas to find the book, and the first reading assignment due at end of week 1!

There are two steps: 1. Click on the fist homework reading assignment link in Canvas. (Do not go to the zyBooks website and create a new account!!) 2. Subscribe.

The section numbers in the tables below refer to the book we are using: MATH 474: Probability and Statistics, zyBook ISBN: 979-8-203-16191-8, by Douglas C. Montgomery & George C. Runger. This is the online textbook linked through Illinois Tech bookstore.

Important dates

• Midterm will take place in class on date tbd, tentatively during week 8 of classes (March 10th). Midterm exam topics: tbd

• Final exam will take place on date tbd. The exam will be comprehensive, with 2/3 of the topics from after the midterm, and 1/3 before the midterm!

Weekly schedule

A note on Homework: all assignments will be posted on Canvas. Linked here for your convenience. Please note, the table below only highlights HW exercises that count toward your HW grade in the class. The table does not include reading assignments and participation grade; for example, there is a reading homework assignment posted on Canvas, due Friday 1/16. You need to keep up to date with the course on Canvas.

Topics	Reading	Homework
Weeks 1 & 2
Introduction to basics Research questions Populations, samples, and data collection Sample spaces and types of data Summarizing quantitative data graphically	Chapter 1	Homework 1 is due week 2, day 2 [Thu 1/22]. Homework 2 is due week 3, day 2 [Thu 1/29].
Week 2
Wrapping up the basics of EDA: measures of spread in the data: range, quartiles, IQR, standard deviation, and what they all mean for data summaries and statistics. Also we will go over the construction and interpretation of boxplots. Introduction to probability (time permitting).	Chapter 2	Homework 2 is due week 3 [Thu 1/29].

Main concepts / ideas:

• wk1, day1: Research questions, population, sample, sample space with notation, data, data collection mechanisms, types of data. Where does randomness come from (I am not random!)? How data are generated.
• wk1, day2: “the big picture” relating probability to inference; examples of studies and conclusions. Why is it important to quantify the uncertainty (e.g., “with 95% confidence we conclude that 65% of the US adult population favors X…”) Sent (on Slack) a data set to try to see a pie chart or a bar chart. Begin to think about how data are usually summarized graphically: visualization of a categorical variable.
• wk2, day1: Histograms - visualizing distributions for quantitative variables. (Remember, we talked about boxplots for discrete variables in week 1.) Interpreting histograms. Measures of center and beginning to think about measures of spread (e.g. range). How do you summarize “data spread”? Think about the histograms we saw in week 2. How you do detect an outlier?
• wk2, day2: Wrapping up standard deviations as a measure of variability in the data. Introduction to probability: sample spaces and events, equally likely outcomes, and basic rules of probability. Formal introduction to probability: meaning, notation, likelihood, and computation using relative frequency of an event in a sample space. Empirical computation of probability - quick overview.

HW formatting

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