Learn · Data Analysis for Social Scientists
Lecture 19 of 23Lecture Videos
Lecture 19: Practical Issues in Running Regressions
Prof. Esther Duflo and Dr. Sara Ellison · Massachusetts Institute of Technology
Credits and licence
- From
- Data Analysis for Social Scientists, Massachusetts Institute of Technology (at MIT OpenCourseWare)
- Licence
- CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only.
- Attribution
- Prof. Esther Duflo, Dr. Sara Ellison. Data Analysis for Social Scientists. Spring 2023. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
- Who can watch
- Teens profiles and older. How courses are placed
All the lectures
- Lecture 01: Introduction to 14.310x Data Analysis for Social Scientists
- Lecture 02: Fundamentals of Probability
- Lecture 03: Random Variables, Distributions, and Joint Distributions
- Lecture 04: Gathering and Collecting Data
- Lecture 05: Summarizing and Describing Data
- Lecture 06: Joint, Marginal, and Conditional Distributions
- Lecture 07: Functions of Random Variables
- Lecture 08: Moments of Distribution
- Lecture 09: Expectation, Variance, and Introduction to Regression
- Lecture 10: Special Distributions
- Lecture 11: Special Distributions, continued. The Sample Mean, Central Limit Theorem, and Estimation
- Lecture 12: Assessing and Deriving Estimators
- Lecture 13. Confidence Intervals, Hypothesis Testing, and Power Calculations
- Lecture 14: Causality
- Lecture 15: Analyzing Randomized Experiments
- Lecture 16: (More) Explanatory Data Analysis: Nonparametric Comparisons and Regressions
- Lecture 17: The Linear Model
- Lecture 18: The Multivariate Model
- Lecture 19: Practical Issues in Running Regressions
- Lecture 20: Omitted Variable Bias
- Lecture 21: Endogeneity and Instrument Variables
- Lecture 22: Experimental Design
- Lecture 23: Visualizing Data