STA-3032-01_SP-2026 - Statistics course syllabus - schedule and assessments

Sep 2, 2026

Statistics course schedule for STA-3032-01_SP-2026 covering core topics from R setup, descriptive statistics, exploratory data analysis, and probability through conditional probability, Bayes’ rule, and joint distributions. The plan includes midterms, quizzes, homework, and labs that progressively advance from random variables and discrete/continuous distributions to covariance, the central limit theorem, confidence intervals, and hypothesis testing. The later weeks introduce sampling distributions, normal and related distributions, and regression with diagnostics, ending with a final comprehensive exam.

LanguageEnglish

CategoryExam

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Week-by-week topics and assessments

Labs, homework, quizzes, and textbook chapters

Lab details

| Grade | A | A- | B+ | B | B- | C+ | C | D | F | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Percentage | 90% | 87% | 84% | 80% | 77% | 74% | 70% | 60% | 0% | | GPA | 4.0 | 3.67 | 3.33 | 3.0 | 2.67 | 2.33 | 2.0 | 1.0 | 0.0 | | Week | Topics | Assessments
/ Activities | Textbook(s) & Chapters | | | --- | --- | --- | --- | --- | | 1 | Intro to R,
Descriptive
Statistics | Lab 1 | Thulin Ch. 1–3, Kerns Ch. 1–3 | | | 2 | Exploratory Data
Analysis (EDA),
Basic Probability | HW 1, Quiz 1 | Thulin Ch. 2, Kerns Ch. 2 : Plots and Interpretation Pishro-Nik Ch. 1, Kerns Ch. 4, Pishro-Nik Ch.2 : Probability | | | 3 | Basic Probability,
and Counting | HW 2 | Pishro-Nik Ch. 1, Kerns Ch. 4 : Probability
Pishro-Nik Ch.2 : Counting, | | | 4 | Conditional Probability, Bayes’Rule | HW 3, Lab 2,
Quiz 2 | Pishro-Nik Ch. 1 (esp. §1 .4), Kerns Ch. 4 | | | 5 | Review + Midterm 1 Intro to Random Variables | HW 4,
Midterm 1 | Mon:
Wed:
Frid: | Review Session (EDA, Descriptive Stats, Probability, Bayes)
Midterm Exam 1
Introduction to Random Variables (Discrete vs Continuous, notation preview) -Pishro-Nik Ch. 3– Pishro-Nik Ch. 4, Kerns Ch. 5 | | 6 | Joint Distributions & Expectation, Covariance | HW 7 | Pishro-Nik Ch. 5, Kerns Ch. 7 | | | 7 | Discrete Random
Variables | HW 5, Quiz 3 | Pishro-Nik Ch. 3, Kerns Ch. 5 Uniform, Geometric, Binomial, Hypergeometric, and Poisson Distributions | | | 8 | Continuous Random Variables | HW 6, Lab 3 | Pishro-Nik Ch. 4, Kerns Ch. 6 Uniform, Exponential, Gamma, Normal, Chi2, and T Distributions | | | 9 | Review + Midterm 2 Central Limit Theorem | HW 9,
Midterm 2 | Mon:
Wed:
Frid: | Review Session (RVs, Expected Value, Variance, Covariance)
Midterm Exam 2
Central Limit Theorem-Kerns Ch. 8 | | 10 | No Class | | Enjoy your Spring Break !"$&%\# | | | 11 | Normal
Distribution,
Sampling
Distribution | HW 8, Quiz 4 | Pishro-Nik Ch. 4, Kerns Ch. 8 | | | 12 | CLT, Confidence
Intervals | HW 10, Lab 4 | Kerns Ch. 8¸Thulin Ch. 3, Kerns Ch. 9, Pishro-Nik Ch. 8 | | | 13 | Hypothesis Testing | HW 11, Quiz 5 | Thulin Ch. 3, Kerns Ch. 9, Pishro-Nik Ch. 8 | | | 14 | Review + Midterm 3 Intro to Regression | Midterm 3 | Mon:
Wed:
Frid: | Review Session (Sampling Distributions, CLT, Inference)
Midterm Exam 2
Introduction to Regression: Scatterplots, correlation,
model overview- Kerns Ch.11 | | 15 | Regression &
Diagnostics | HW 12, Lab 5,
Quiz 6 | Kerns Ch.11, Thulin Ch. 8 | | --- | --- | --- | --- | | 16 | | Lab 6 | Mon: Review
Wed, Fri: Reading Days | | 17 | Final
Comprehensive
Exam | | | | Lab | Title | Week | Description | | --- | --- | --- | --- | | Lab 1 | Getting Started with R & EDA | Week 1 | Students practice importing data, creating basic plots (histograms, boxplots, scatterplots), and computing summary statistics. Helps build R fluency from day one. | | Lab 2 | Simulating Bayes’ Rule | Week 4 | Use simulation to explore conditional probability and Bayes’ theorem with real-world examples (e.g., medical testing, spam filters) . Reinforces conceptual understanding. | | Lab 3 | Exploring Distributions in R | Week 8 | Students simulate and visualize discrete and continuous distributions (Binomial, Geometric, Uniform, Exponential, Normal) . Introduces rbinom(), rexp(), rnorm() etc. | | Lab 4 | Confidence Intervals and Hypothesis Testing | Week 11 | Simulation-based exploration of confidence intervals for means and proportions. Emphasizes interpretation and introduces the idea of bootstrapping. | | Lab 5 | Simple Linear Regression in R | Week 15 | Students fit a simple linear model, interpret coefficients, and visualize fit with residuals and diagnostics. Builds foundation for regression inference. | | Lab 6 | Tentative-Regression Diagnostics Mini-Project | Week 16 | A more open-ended lab: students apply regression to a new dataset, check assumptions, and write a short report on model quality. E

FAQs

What topics are covered in the course schedule?

The schedule covers descriptive statistics, EDA, probability, exploratory data analysis, conditional probability and Bayes’ rule, random variables, distributions, the central limit theorem, confidence intervals, hypothesis testing, and an introduction to regression and diagnostics.

What assessment activities occur throughout the semester?

Assessments include homework assignments, quizzes, labs, and midterm exams (Midterm 1, Midterm 2, Midterm 3), plus a final comprehensive exam. Review sessions precede the midterms.

What do the labs focus on?

Labs focus on building R and EDA skills, simulating Bayes’ rule, exploring distributions in R, using simulation for confidence intervals and hypothesis testing, implementing simple linear regression with residual diagnostics, and performing a mini-project on regression diagnostics with an open-ended dataset and short report.

Sep 2, 2026

Upload by Sophia Brooks