STAT400 / MATH463: Statistics and Probability I, Fall 2018
Instructor: Xiaohui Chen (Office:104A Illini Hall).
Lecture (CL1): MWF 12:00pm — 12:50pm, 103 Mumford Hall.
Office Hours: M 1:00pm — 3:00pm, 104A Illini Hall.
TA and Grader:
— TA: Sayan Chakrabarty (email@example.com).
— Grader: Zihao Yang (firstname.lastname@example.org).
— CD1: W 4:00pm — 4:50pm, 1027 Lincoln Hall.
— CD2: R 4:00pm — 4:50pm, 31 Psychology B.
— CD3: R 1:00pm — 1:50pm,1027 Lincoln Hall.
— CD4: R 8:00am — 8:50am, 1065 Lincoln Hall.
TA Office Hours: M – R, 5:00pm – 6:50pm.
Course Website: Course syllabus, lecture notes and slides will be posted on the Compass 2g. Homework assignments will be posted, electronically submitted and graded through the LON-CAPA system.
Prerequisite: MATH 241 Calculus III.
— First Day of Class: Aug. 27, 2018, M.
— Last Day of Class: Dec. 12, 2018, W.
Text: Robert V. Hogg, Elliot A. Tanis, Dale L. Zimmerman. Probability and Statistical Inference. Ninth Edition. Pearson.
Topics: This is an introductory course to mathematical statistics that develops probability as needed. Topics to be covered are:
1. Probability and random variables (Ch.1)
2. Discrete and continuous distributions (Ch.2-4)
3. The central limit theorem and normal approximation (Ch.5)
4. Point estimation and confidence intervals (Ch.6-7)
5. Hypothesis testing (Ch.8)
Please see the following tentative schedule for details.
Course Plan/Progress (Tentative)
Week 1 Contents
Aug. 27 (M): Introduction
Aug. 29 (W): Probability rules
Aug. 31 (F): Conditional probability
Week 2 Contents
Sep. 3 (M): Labor Day (no class)
Sep. 5 (W): Bayes Theorem
Sep. 7 (F): Independence
Week 3 Contents
Sep. 10 (M): Enumeration
Sep. 12 (W): Discrete random variables
Sep. 14 (F): Expectation
Week 4 Contents
Sep. 17 (M): Bernoulli, Binomial
Sep. 19 (W): Geometric, negative binomial
Sep. 21 (F): Moment generating functions
Week 5 Contents
Sep. 24 (M): Poisson process
Sep. 26 (W): Poisson distribution
Sep. 28 (F): Continuous random variables
Week 6 Contents
Oct. 1 (M): Uniform, exponential
Oct. 3 (W): Midterm exam I (in class)
Oct. 5 (F): Gamma, Chi-square
Week 7 Contents
Oct. 8 (M): Gaussian/normal
Oct. 10 (W): Normal and related distributions
Oct. 12 (F): Covariance
Week 8 Contents
Oct. 15 (M): Correlation coefficients
Oct. 17 (W): Several independent random variables
Oct. 19 (F): Chebyshev inequalities and applications
Week 9 Contents
Oct. 22 (M): Central limit theorem (CLT) & normal approximation
Oct. 24 (W): Central limit theorem (CLT) & normal approximation
Oct. 26 (F): Maximum likelihood estimator (MLE)
Week 10 Contents
Oct. 29 (M): MLE
Oct. 31 (W): Method of moments estimator
Nov. 2 (F): Midterm exam II (in class)
Week 11 Contents
Nov. 5 (M): Evaluate estimators
Nov. 7 (W): Confidence intervals (CI) for means
Nov. 9 (F): Sample size calculations
Week 12 Contents
Nov. 12 (M): CI for variances
Nov. 14 (W): CI for proportions
Nov. 16 (F): Hypothesis testing
Week 13 Contents
Thanksgiving week (no class)
Week 14 Contents
Nov. 26 (M): Test for proportions
Nov. 28 (W): Test for means
Nov. 30 (F): Test for variances
Week 15 Contents
Dec. 3 (M): Test for equality of two means
Dec. 5 (W):
Dec. 7 (F):
Week 16 Contents
Dec. 10 (M):
Dec. 12 (W):
Final Exam: TBD.