S520: Introduction To Statistics
This course introduces the basic concepts of statistical inference through a careful study of several important procedures. Topics include 1- and 2-sample location problems, the one-way analysis of variance, and simple linear regression. Most assignments involve applying probability models and/or statistical methods to practical situations and/or actual data sets.
Semester(s) Offered: Spring
Class time: Tuesday, Thursday : 4-5:15 pm
Instructor: Brad Luen - email@example.com
Other Contact(s): Brad Luen - firstname.lastname@example.org
Sequence: No specific sequence. For students new to statistics, S520 is the preferred gateway to more advanced courses offered by the statistics department.
Prerequisites: The course catalog lists a year of calculus (MATH-M 211-212) as a formal prerequisite, but it should be noted that S520 is NOT a calculus-based course. No previous knowledge of calculus is required to complete S520, but the course alludes to several fundamental concepts from calculus (limit, area under curve). The purpose of the prerequisite is to screen out students who are afraid of formulas and mathematical notation, as such students typically find S520 too challenging.
Algebra Required?: None.
Calculus Required?: A few concepts only.
Recommended follow-up classes: STAT-S 631-632 (Applied Linear Models I & II). Students interested in learning more about statistical theory should follow S520 with MATH-M 463 (Introduction to Probability Theory), then STAT-S 620 (Introduction to Statistical Theory).
Substantive Orientation: Designed to accommodate students from a variety of disciplines.
Statistical Orientation: This course emphasizes a frequentist perspective. It covers conditional probability and Bayes theorem, but not Bayesian inference.
Books used: An Introduction to Statistical Inference and Its Applications with R, by Michael Trosset. Chapman & Hall/CRC, 2009. Here is the web page for the text: http://mypage.iu.edu/~mtrosset/StatInfeR.html
Applied/Theoretical: Intermediate. S520 emphasizes applications of statistical inference, but tries to communicate a deeper understanding of fundamental principles than does a typical introductory statistics text. For this reason, S520 initially strikes some students as somewhat theoretical. It emphasizes the assumptions that underlie common statistical practices. For example, Student's 2-sample t-test assumes equal population variances, whereas Welch's approximate t-test does not. Is that fact an obscure bit of statistical theory or a useful guide to good statistical practice?
Software Used: R
How the software is used: A distinctive feature of S520 is interactive computing
Problem Sets: Weekly, for 25% of the semester average.
Data Analysis: Yes, typically involving real data sets
Exams: Historically, two midterm tests and a final exam.
Keywords: statistical inference, tests, confidence intervals, location problems, association, regression