MIT OCW 14.310x Data Analysis for Social Scientists (Spring 2023)
Prof. Esther Duflo and Dr. Sara Ellison

folder MIT OCW 14.310x Data Analysis for Social Scientists (Spring 2023) (46 files)
fileLecture 23: Visualizing Data.mp4 217.77MB
fileLecture 23: Visualizing Data.en.vtt 123.34kB
fileLecture 22: Experimental Design.mp4 233.18MB
fileLecture 22: Experimental Design.en.vtt 105.55kB
fileLecture 21: Endogeneity and Instrument Variables.mp4 301.93MB
fileLecture 21: Endogeneity and Instrument Variables.en.vtt 105.30kB
fileLecture 20: Omitted Variable Bias.mp4 255.05MB
fileLecture 20: Omitted Variable Bias.en.vtt 118.43kB
fileLecture 19: Practical Issues in Running Regressions.mp4 378.85MB
fileLecture 19: Practical Issues in Running Regressions.en.vtt 111.02kB
fileLecture 18: The Multivariate Model.mp4 194.52MB
fileLecture 18: The Multivariate Model.en.vtt 53.56kB
fileLecture 17: The Linear Model.mp4 203.31MB
fileLecture 17: The Linear Model.en.vtt 103.36kB
fileLecture 16: (More) Explanatory Data Analysis: Nonparametric Comparisons and Regressions.mp4 344.98MB
fileLecture 16: (More) Explanatory Data Analysis: Nonparametric Comparisons and Regressions.en.vtt 127.76kB
fileLecture 15: Analyzing Randomized Experiments.mp4 256.73MB
fileLecture 15: Analyzing Randomized Experiments.en.vtt 110.93kB
fileLecture 14: Causality.mp4 142.49MB
fileLecture 14: Causality.en.vtt 110.90kB
fileLecture 13. Confidence Intervals, Hypothesis Testing, and Power Calculations.mp4 291.46MB
fileLecture 13. Confidence Intervals, Hypothesis Testing, and Power Calculations.en.vtt 105.37kB
fileLecture 12: Assessing and Deriving Estimators.mp4 211.82MB
fileLecture 12: Assessing and Deriving Estimators.en.vtt 86.00kB
fileLecture 11: Special Distributions, continued. The Sample Mean, Central Limit Theorem, and Estimation.mp4 283.13MB
fileLecture 11: Special Distributions, continued. The Sample Mean, Central Limit Theorem, and Estimation.en.vtt 96.77kB
fileLecture 10: Special Distributions.mp4 303.25MB
fileLecture 10: Special Distributions.en.vtt 106.48kB
fileLecture 09: Expectation, Variance, and Introduction to Regression.mp4 190.11MB
fileLecture 09: Expectation, Variance, and Introduction to Regression.en.vtt 87.12kB
fileLecture 08: Moments of Distribution.mp4 349.02MB
fileLecture 08: Moments of Distribution.en.vtt 104.70kB
fileLecture 07: Functions of Random Variables.mp4 379.46MB
fileLecture 07: Functions of Random Variables.en.vtt 102.28kB
fileLecture 06: Joint, Marginal, and Conditional Distributions.mp4 155.18MB
fileLecture 06: Joint, Marginal, and Conditional Distributions.en.vtt 70.84kB
fileLecture 05: Summarizing and Describing Data.mp4 222.62MB
fileLecture 05: Summarizing and Describing Data.en.vtt 99.77kB
fileLecture 04: Gathering and Collecting Data.mp4 352.89MB
fileLecture 04: Gathering and Collecting Data.en.vtt 127.48kB
fileLecture 03: Random Variables, Distributions, and Joint Distributions.mp4 251.21MB
fileLecture 03: Random Variables, Distributions, and Joint Distributions.en.vtt 88.26kB
fileLecture 02: Fundamentals of Probability.mp4 236.03MB
fileLecture 02: Fundamentals of Probability.en.vtt 88.27kB
fileLecture 01: Introduction to 14.310x Data Analysis for Social Scientists.mp4 142.30MB
fileLecture 01: Introduction to 14.310x Data Analysis for Social Scientists.en.vtt 88.05kB
Type: Course
Tags: mit ocw

Bibtex:
@article{,
title= {MIT OCW 14.310x Data Analysis for Social Scientists (Spring 2023)},
journal= {},
author= {Prof. Esther Duflo and Dr. Sara Ellison},
year= {2023},
url= {https://ocw.mit.edu/courses/14-310x-data-analysis-for-social-scientists-spring-2023/},
abstract= {This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, and data visualization.

We will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses.},
keywords= {mit ocw},
terms= {},
license= {CC BY-NC-SA 4.0},
superseded= {}
}

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