Regression with R and Python: Description, Prediction, and Causal Analysis

Course Content

14 courselets

	Regression with R and Python — Chapter 01: Introduction (pdf)	605.11 K
	Regression with R and Python — Chapter 01: Introduction (video)	30 min

	Regression with R and Python — Chapter 02: Covariation in Data (pdf)	967.29 K
	Regression with R and Python — Chapter 02: Covariation in Data (video)	30 min

	Regression with R and Python — Chapter 03: Basic Probability Theory and Statistical Inference (pdf)	674.92 K
	Regression with R and Python — Chapter 03: Basic Probability Theory and Statistical Inference (video)	32 min

	Regression with R and Python — Chapter 04: Correlation and Inference about a Population (pdf)	583.42 K
	Regression with R and Python — Chapter 04: Correlation and Inference about a Population (video)	29 min

	Regression with R and Python — Chapter 05: The Simple Linear Regression Model (pdf)	702.37 K
	Regression with R and Python — Chapter 05: The Simple Linear Regression Model (video)	34 min

	Regression with R and Python — Chapter 06: Multiple Linear Regression (pdf)	1.21 M
	Regression with R and Python — Chapter 06: Multiple Linear Regression (video)	36 min

	Regression with R and Python — Chapter 07: Nonlinear Functional Form (pdf)	565.9 K
	Regression with R and Python — Chapter 07: Nonlinear Functional Form (video)	27 min

	Regression with R and Python — Chapter 08: Regression Analysis with Dependent Error Terms (pdf)	497.75 K
	Regression with R and Python — Chapter 08: Regression Analysis with Dependent Error Terms (video)	28 min

	Regression with R and Python — Chapter 09: Binary Dependent Variable and GLM (pdf)	683.22 K
	Regression with R and Python — Chapter 09: Binary Dependent Variable and GLM (video)	32 min

	Regression with R and Python — Chapter 10: Prediction (pdf)	419.87 K
	Regression with R and Python — Chapter 10: Prediction (video)	32 min

	Regression with R and Python — Chapter 11: Nonparametric Regression Methods (pdf)	1.16 M
	Regression with R and Python — Chapter 11: Nonparametric Regression Methods (video)	28 min

	Regression with R and Python — Chapter 12: Time Series Analysis (pdf)	521.31 K
	Regression with R and Python — Chapter 12: Time Series Analysis (video)	34 min

	Regression with R and Python — Chapter 13: Causal Analyses (pdf)	802.72 K
	Regression with R and Python — Chapter 13: Causal Analyses (video)	40 min

	Regression with R and Python — Chapter 14: Key Concepts (pdf)	481.13 K
	Regression with R and Python — Chapter 14: Key Concepts (video)	32 min

Requirements

Introductory statistics is helpful. Learners can use R or Python for the practical exercises; the conceptual chapters can be followed without programming experience.

General Overview

Description

This course accompanies Regression with R and Python: Description, Prediction, and Causal Analysis in Social Science and Medicine by Per Johansson and Jiajing Sun. It follows the fourteen chapters of the book, moving from descriptive questions through prediction to causal analysis.

For each chapter, watch the public lecture, read the online chapter, take the original quiz, and run the R or Python examples. Teaching slides are available through the protected reader on the official companion site.