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Multiple Regression
Multiple Regression
  • Syllabus
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Class sessions
  • 1: Context for and Use of Regression Modelling
  • 2: Fitting Regression Models
  • 3: Slope Inference
  • 4: ANOVA Table
  • 5 SLR Review
  • 6 Take Home Exam 1
  • 7 MLR Intro
  • 8 MLR Intro 2
  • 9 Parallel & Non-Parallel Slopes
  • 10 Multicollinearity & Nested F
  • 11 Outliers, Leverage & Influence
  • 12 Bootstrap for Inference
  • 13 MLR & IPUMS - Data Wrangling
  • 14 MLR & IPUMS - Modeling
  • 15 Transformations
  • 16 Odds, Prob, ORs
  • 17 Simple Logistic - 1
  • 18 Simple Logistic 2
  • 19 Multiple Logistic - 1
  • 20 Multiple Logistic - 2
  • 21 Logistic Data Visualization
  • 22 Logistic Data Visualization
  • 23 Logistic Regression Review
  • 24 Open Review
Group Project Extra Sessions
  • Workshop 1 - GitHub
  • Workshop 2 - dplyr
  • Workshop 3 - ggplot

Simple Logistic Regression - 2

Materials from class on Wednesday, April 8, 2020
  • In-class Activities
  • Extra Practice

In-class Activities

  • Slides

Extra Practice

  • Lab (raw .rmd here)
    • Answers: Complete .pdf or raw .rmd

Last updated on April 9, 2020

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SDS 291: Multiple Regression (Spring 2020)
Smith College    Program on Statistical & Data Sciences

Dr. Benjamin Capistrant    bcapistrant@smith.edu

Monday & Wednesday    TBD
Zoom

All content licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Content 2020 Ben Capistrant

A tremendous thanks to Andrew Heiss whose course sites served as the basis for the format and structure of this site. They employ the Academic theme in blogdown and built with Hugo.

View the source at GitHub.

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