BIOSTATS 640
Intermediate Biostatistics
Biostatistics and Epidemiology
UMass Amherst

 

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1. Review of BIOSTATS 540
2. Discrete Distributions

3. Introduction to Nonparametrics

4. Categorical Data Analysis

5. Normal Theory Regression
6. Analysis of Variance

7. Logistic Regression
8. Introduction to Survival Analysis

5. Normal Theory Regression

Scroll down for (1) BIOSTATS 640 Fall 2023 (2) Additional Readings and Videos (3) Resources for Learning R (4) Other Resources

(1) BIOSTATS 640 - Fall 2023

Lecture Notes - Fall 2023 course notes, 5. Regression and Correlation (pdf, 58 pp)
Right click to download R data:
(janka.Rdata)
(p53paper.Rdata)
(framingham_1000.Rdata)
Right click to download excel data:
(janka.xlsx)
(p53paper.xlsx)
(framingham_1000.xlsx)

As needed: From BIOSTATS 540 - Introductory Biostatistics
(source: Fall 2022) Notes 12. Simple Linear Regression and Correlation (pdf, 55 pp)
UNIT INTRODUCTION
Video (source: OpenIntroOrg.com) Introduction to Linear Regression (video, 6:48)
Video
(source: OpenIntroOrg.com) Introduction to Multiple Regression (video, 4:52)

ppt INTRODUCTION (Source: Whitlock and Schluter ) Overheads 16 - Correlation (pdf, 6 slides)
ppt INTRODUCTION (Source: Whitlock and Schluter ) Overheads 17 - Regression (pdf, 12 slides)
(Source: www.artofstat.come) WebApps > Fit Linear Regression Model (html)
FAQ 1 - Schematic of Analysis of Variance (pdf, 2 pp)
FAQ - Incremental Sum of Squares, Analysis of Variance and Partial F-Tests (pdf, 2 pp)
FAQ - Confounding and Effect Modification (pdf, 6 pp)
BMTRY 701 Lecture 12 - Multiple Linear Model: Model Building (pdf, 24 slides)
(Source: Tindall, David, Department of Sociology, University of British Columbia) Regression Diagnostics Procedures (pdf, 31 slides)
(Source: https://ademos.people.uic.edu/Chapter12.html)
Learning Statistics in R, Chapter 12. Regression: Basics, Assumptions and Diagnostics, by Allison B. Mueller (html)
(Source: https://www2.stat.duke.edu/courses/Spring20/sta210.001/slides/lec-slides/12-model-selection.html#1)
Maria Tackett, Duke University Course, STA 210.
Multiple Linear Regression: Model Selection and Diagnostics (html)

Homework 1 of 3
Due Wednesday October 18, 2023

Last date for late submission for credit (-20 points): Wednesday October 25, 2023
Questions (pdf, 3 pp)
Download (simplelinear.xlsx)


SOLUTIONS to homework 1 of 3
Art of Stat and R (pdf, 13 pp)

Homework 2 of 3
Due Wednesday October 25, 2023
Last date for late submission for credit (-20 points): Wednesday November 1, 2023
Questions (pdf, 3 pp)
Download (hersdata_small.xlsx)


SOLUTIONS to homework 2 of 3
R (pdf, 12 pp)
Homework 3 of 3
Due Wednesday November 1, 2023
Last date for late submission for credit (-20 points): Wednesday November 8, 2023
Questions (pdf, 4 pp)
Download (hersdata_small.xlsx)


SOLUTIONS to homework 3 of 3
R (pdf, 18 pp)




(2) Additional Readings and Videos
Simple Linear Regression:
VIDEO (Source: OpenIntroOrg.com) Introduction to Linear Regression (video, 6:48)
VIDEO (Source: OpenIntroOrg.com) Line Fitting, Residuals, and Correlation (video, 4:05)
VIDEO (Source: OpenIntroOrg.com) Inference for Linear Regression (video, 4:20)
(Source: BBaldi at WH Freeman, 2006) Looking at Data: Relationships (pdf, 12 pages, 23 slides)
(Source: Jeremy Balka's Statistics Channel - jbstatistics)
VIDEO: Introduction to Simple Linear Regression (video 8:09)
(Source: Jeremy Balka's Statistics Channel - jbstatistics)
VIDEO: The Least Squares Regression Line (video 7:24)
(Source: Jeremy Balka's Statistics Channel - jbstatistics)
VIDEO: Simple Linear Regression: Interpreting Model Parameters (video 5:05)
(Source: Jeremy Balka's Statistics Channel - jbstatistics)
VIDEO: Assumptions (video 3:05)
(Source: Jeremy Balka's Statistics Channel - jbstatistics)
VIDEO: Checking Assumptions with Residuals Plots (video 8:04)
 
Multiple Linear Regression:
(Source: Sauerbrei et al. Diagnostic and Prognostic Research 2020 4:3) State of the art in selection of variables and functional forms in multivariable analysis - outstanding issues (pdf, 18 pp)
(Source: https://ademos.people.uic.edu/Chapter12.html)
Learning Statistics in R, Chapter 12. Regression: Basics, Assumptions and Diagnostics, by Allison B. Mueller (html)
(Source: https://www2.stat.duke.edu/courses/Spring20/sta210.001/slides/lec-slides/12-model-selection.html#1)
Maria Tackett, Duke University Course, STA 210.
Multiple Linear Regression: Model Selection and Diagnostics (html)
VIDEO (Source: OpenIntroOrg.com) Introduction to Multiple Regression (video, 4:52)
(Source: Kari Lock Morgan, STATS 101) Multiple Regression (pdf, 41 slides)
Multiple Linear Regression
(Source: courses.washington.edu/b515/l7) Lecture 7 - Linear Regression Diagnostics (pdf, 40 slides)
Multiple Linear Regression
(Source: courses.washington.edu/b515/l8) Lecture 8 - Regression Diagnostics - continued (pdf, 28 slides)
Multiple Linear Regression
(Source: courses.washington.edu/b515/l9) Lecture 9 - Variable Selection (pdf, 38 slides)



(3) Resources for R Learners
R Users
#06 - Simple Linear Regression
(pdf, 14 pp)
Right click to download (ers.Rdata)
R Users
#07 - Introduction to Multiple Linear Regression in R
(pdf, 14 pp)
Right click to download (framingham_didactic.xlsx)
R Users
#08 - Regression Diagnostics for Normal Theory Regression in R
(pdf, 21 pp)
Right click to download (hersdata_small.xlsx)
(source: Whitlock and Schluter, The Analysis of Biological Data, Third Edition) Labs Using R: 11. Correlation and Regression (html)
Right click to download (chap02e3bGuppyFatherSonAttractiveness.csv)
(Source: www.r-tutor.com R Tutorials) Simple Linear Regression (html)
(Source: Denise Ferrari, Vanderbilt) Regression in R (pdf, 96 slides)
(Source: Steiger J. Vanderbilt University) Introduction to Multiple Regression (pdf, 54 slides)
(source: BIOSTATS 690C Fall 2020) Illustration: R for Simple Linear Regression (pdf, 11 pp)
Right click to download (ersdata.Rdata)
(source: BIOSTATS 690C Spring 2021) Illustration: R for Multiple Linear Regression (pdf, 11 pp)
Right click to download (p53data.Rdata)
(Source: https://ademos.people.uic.edu/Chapter12.html)
Learning Statistics in R, Chapter 12. Regression: Basics, Assumptions and Diagnostics, by Allison B. Mueller (html)
(Source: https://www2.stat.duke.edu/courses/Spring20/sta210.001/slides/lec-slides/12-model-selection.html#1)
Maria Tackett, Duke University Course, STA 210.
Multiple Linear Regression: Model Selection and Diagnostics (html)
(Source: https://bookdown.org/ltupper/340f21_notes/)
Lauri Tupper, Stat 340 Notes: Fall 2021
At left, scroll to find 1 ML1. MLR Fundamentals (html)
VIDEO
Simple Linear Regression

(Source: MarinStatsLectures R) Simple Linear Regression (video, 8:56)
VIDEO
Simple Linear Regression

(Source: Christoph Scherber, University of Gottingen) Statistics with R (1) - Linear Regression (video, 19:21 min)
VIDEO
Multiple Linear Regression

(Source: Mike Marin, MarinStatsLectures R)
Model Building and Variable Selection: General Comments (video, 7:05)
VIDEO
Multiple Linear Regression

(Source: Mike Marin MarinStatsLectures R)
Determining Effect Size - Collinearity (video, 15:46)
VIDEO
Multiple Linear Regression

(Source: Mike Marin MarinStatsLectures R)
Model Building and Variable Selection: Effect Size Models (video, 16:19)
VIDEO
Multiple Linear Regression

(Source: Mike Marin MarinStatsLectures R)
Multiple Linear Regression in R (video, 5:18)
VIDEO
Multiple Linear Regression

(Source: Mike Marin MarinStatsLectures R)
Including a Categorical Variable/Factor in Regression (video, 5:41)

VIDEO
Multiple Linear Regression

(Source: Mike Marin MarinStatsLectures R)

Checking Linear Assumptions in R (video, 7:50)

VIDEO
Multiple Linear Regression

(Source: Mike Marin MarinStatsLectures R)

Addressing Violations of Model Assumptions in R (video, 11:26)
Multiple Linear Regression
(source: UCLA Institute for Digital Research and Education - Statistical Consulting) Introduction to Regression in R (html)
Right click to download (elemapi2v2.csv)

Lesson 1. Simple and Multiple Regression (html)
Lesson 2. Regression Diagnostics (html)
Lesson 3. Regression with Categorical Predictors (html)
Multiple Linear Regression
(Source: www.statmethods.net) Quick R - Multiple Linear Regression (html)
(Source: Elizabeth Garret-Mayer, Medical University of South Carolina) RBMTRY 701
Lecture 3 - Simple Linear Regression: Introduction (pdf, 38 slides)
Lecture 4 - Simple Linear Regression Inferences and Diagnostics (pdf, 50 slides)
Lecture 12 - Model Building (pdf 24 slides)

Linear Regression
(Source: Statistical Tools for High Throughput Data Analysis STHTDA) Simple Linear (html)
(Source: Statistical Tools for High Throughput Data Analysis STHTDA) Multiple Linear (html)




(4) Other Resources
Stata Users
(Source: BIOSTATS 690C, Fall 2020)
9. Stata for Normal Theory Regression (pdf, 48 pp)
(Source: Stat Trek AP Statistics) Statistics Tutorial: Least Squares Linear Regression (html)
(Source: Stat Trek AP Statistics) Statistics Tutorial: Correlation and Linearity (html)
(Source: Gerstman BB. Basic Biostatistics Statistics for Public Health Practice) Chapter 15: Multiple Linear Regression (pdf, 24 slides)
(Source: Mallick BK STAT 651) Lecture 20 (pdf, 43 slides)
(Source: I'm not sure, sorry! - Biometry 755 Spring 2009) Regression Diagnostics (pdf, 24 slides)
SPSS (Source: UCLA Academic Technology Services) Introduction to Regression with SPSS (html)
SAS (Source: UCLA Academic Technology Services) SAS Web Books Regression with SAS (html)

 

 


 

 

 



  

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This is the course web site for BIOSTATS 640, Biostatistics Program, Department of Biostatistics and Epidemiology.
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Page updated: October 26
, 2023