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| ID | 5243 |
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| Folder | MIT OCW 14.310x Data Analysis for Social Scientists (Spring 2023) |
| Num files | 46 files [See full list] |
| Mirrors | 6 complete, 2 downloading = 8 mirror(s) total [Log in to see full list] |
MIT OCW 14.310x Data Analysis for Social Scientists (Spring 2023) (46 files)
 Lecture 23： Visualizing Data.mp4 | 217.77MB |
| Lecture 23： Visualizing Data.en.vtt | 123.34kB |
| Lecture 22： Experimental Design.mp4 | 233.18MB |
| Lecture 22： Experimental Design.en.vtt | 105.55kB |
| Lecture 21： Endogeneity and Instrument Variables.mp4 | 301.93MB |
| Lecture 21： Endogeneity and Instrument Variables.en.vtt | 105.30kB |
| Lecture 20： Omitted Variable Bias.mp4 | 255.05MB |
| Lecture 20： Omitted Variable Bias.en.vtt | 118.43kB |
| Lecture 19： Practical Issues in Running Regressions.mp4 | 378.85MB |
| Lecture 19： Practical Issues in Running Regressions.en.vtt | 111.02kB |
| Lecture 18： The Multivariate Model.mp4 | 194.52MB |
| Lecture 18： The Multivariate Model.en.vtt | 53.56kB |
| Lecture 17： The Linear Model.mp4 | 203.31MB |
| Lecture 17： The Linear Model.en.vtt | 103.36kB |
| Lecture 16： (More) Explanatory Data Analysis： Nonparametric Comparisons and Regressions.mp4 | 344.98MB |
| Lecture 16： (More) Explanatory Data Analysis： Nonparametric Comparisons and Regressions.en.vtt | 127.76kB |
| Lecture 15： Analyzing Randomized Experiments.mp4 | 256.73MB |
| Lecture 15： Analyzing Randomized Experiments.en.vtt | 110.93kB |
| Lecture 14： Causality.mp4 | 142.49MB |
| Lecture 14： Causality.en.vtt | 110.90kB |
| Lecture 13. Confidence Intervals, Hypothesis Testing, and Power Calculations.mp4 | 291.46MB |
| Lecture 13. Confidence Intervals, Hypothesis Testing, and Power Calculations.en.vtt | 105.37kB |
| Lecture 12： Assessing and Deriving Estimators.mp4 | 211.82MB |
| Lecture 12： Assessing and Deriving Estimators.en.vtt | 86.00kB |
| Lecture 11： Special Distributions, continued. The Sample Mean, Central Limit Theorem, and Estimation.mp4 | 283.13MB |
| Lecture 11： Special Distributions, continued. The Sample Mean, Central Limit Theorem, and Estimation.en.vtt | 96.77kB |
| Lecture 10： Special Distributions.mp4 | 303.25MB |
| Lecture 10： Special Distributions.en.vtt | 106.48kB |
| Lecture 09： Expectation, Variance, and Introduction to Regression.mp4 | 190.11MB |
| Lecture 09： Expectation, Variance, and Introduction to Regression.en.vtt | 87.12kB |
| Lecture 08： Moments of Distribution.mp4 | 349.02MB |
| Lecture 08： Moments of Distribution.en.vtt | 104.70kB |
| Lecture 07： Functions of Random Variables.mp4 | 379.46MB |
| Lecture 07： Functions of Random Variables.en.vtt | 102.28kB |
| Lecture 06： Joint, Marginal, and Conditional Distributions.mp4 | 155.18MB |
| Lecture 06： Joint, Marginal, and Conditional Distributions.en.vtt | 70.84kB |
| Lecture 05： Summarizing and Describing Data.mp4 | 222.62MB |
| Lecture 05： Summarizing and Describing Data.en.vtt | 99.77kB |
| Lecture 04： Gathering and Collecting Data.mp4 | 352.89MB |
| Lecture 04： Gathering and Collecting Data.en.vtt | 127.48kB |
| Lecture 03： Random Variables, Distributions, and Joint Distributions.mp4 | 251.21MB |
| Lecture 03： Random Variables, Distributions, and Joint Distributions.en.vtt | 88.26kB |
| Lecture 02： Fundamentals of Probability.mp4 | 236.03MB |
| Lecture 02： Fundamentals of Probability.en.vtt | 88.27kB |
| Lecture 01： Introduction to 14.310x Data Analysis for Social Scientists.mp4 | 142.30MB |
| Lecture 01： Introduction to 14.310x Data Analysis for Social Scientists.en.vtt | 88.05kB |
Type: Course
Tags: mit ocw
Bibtex:
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= {}
}
