STAT 845 Statistical Methods for Research

Catalogue Description

Statistical methods as they apply to scientific research, including: Experimental design, blocking and confounding, analysis of multifactor experiments, multiple regression, and model building.

Prerequisite(s): STAT 242 or 245 or permission of the department.

List of R Demo

  1. A Quick Introduction to using R for Data Analysis
  2. Simple Linear Regression
  3. Analysis of Multiple Linear Regression Models
The source Rmarkdown files for producing the above html files are available on this Github respository.

Slides on One-drive

The Link to One-drive slides (password-protected)

Links to Other Resources

  1. STAT 845 on Canvas
  2. STAT 245: Introductory Statistics
  3. Learning Statistics with R (an online book)
  4. Multiple Linear Regression with R
  5. STAT 345: Design and Analysis of Experiments

Tentative Course Schedule 🗓️

Important: The official schedule and due dates for all assignments and tests are posted on Canvas.

Academic Week Calendar Week Date of First Monday Topics Remarks
1 36 Sep 01 Introduction Sep 04: First day of class for STAT 845
2 37 Sep 08 Unit 1: Review of Basic Statistics and Computational Tools
3 38 Sep 15 Unit 2
4 39 Sep 22 Unit 3
5 40 Sep 29 Unit 3 Sep 30: No Class
6 41 Oct 06 Unit 3
7 42 Oct 13 Unit 4 Oct 13: Thanksgiving – No class
Oct 17: Assignment 1 due
8 43 Oct 20 Unit 4 Oct 21: Midterm 1 (during class)
9 44 Oct 27 Unit 5
10 45 Nov 03 Unit 5 Nov 07: Assignment 2 due
N/A 46 Nov 10 — Fall Reading Week – No classes
11 47 Nov 17 Unit 6
12 48 Nov 24 Unit 6 Nov 27: Midterm 2 (during class)
13 49 Dec 01 Unit 6 Dec 04: Last lecture for STAT 845
Dec 05: Assignment 3 due

List of Topics

 

Part I: Regression Analysis
Unit 2: Simple Linear Regression. Model Description and Assumptions, Least Squares Formulation, Properties of the Least Squares Estimators, Hypothesis Testing, Confidence Intervals, Prediction, Model Adequacy Checking, Correlation, Regression on Transformed Variables.
Unit 3: Multiple Linear Regression. Model Description and Assumptions, Least Squares Formulation, Properties of the Least Squares Estimators, Hypothesis Testing, Confidence Intervals, Prediction, Model Adequacy Checking, Polynomial Regression Models, Categorical Regressors and Indicator Variables, Selection of Variables and Model Building, Multicollinearity.
Unit 4: Logistic Regression.

Regression with a Binary Response, Statistical Inference.

Part II: Design and Analysis of Experiments
Unit 5: Single-Factor Experiments. Designing Experiments, Completely Randomized Single-Factor Experiment, Randomized Complete Block Design.
Unit 6: Factorial Experiments.

Two-Factor Factorial Experiments, General Factorial Experiments, 2k Designs, Single Replicate of the 2k Designs, Blocking and Confounding.