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Endogeneity, Instrumental Variables, and Experimental Design

Review of Module 11 of Data Analysis for Social Scientists (MITx, edX) – Intro to Machine Learning and Data Visualisation

Endogeneity problems can occur when there is simultaneous causality (i.e. 122 more words

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Machine Learning and Data Visualisation

Review of Module 10 of Data Analysis for Social Scientists (MITx, edX) – Intro to Machine Learning and Data Visualisation

This week’s topics were very interesting. 259 more words

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Practical issues and omitted variable bias

Review of Module 9 of Data Analysis for Social Scientists (MITx, edX) – Practical Issues in Running Regressions, and Omitted Variable Bias

It has been challenging to fully understand the technical concepts taught in this course, as well as use R to complete the homework, given my intense workload.  194 more words

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Single and multivariate linear models

Review of Module 8 of Data Analysis for Social Scientists (MITx, edX) – Single and multivariate linear models

Estimating the parameters of joint distributions can be used for prediction, determining causality and just understanding the world better. 122 more words

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The Massachusetts Innovation & Technology Exchange (MITX) serves the Internet business & marketing industry, where marketing and technology converge.

Randomisation is not a substitute for thinking

Review of Module 7 of Data Analysis for Social Scientists (MITx, edX) – Causality, Analysing Randomised Experiments, and Nonparametric Regression

The lectures were very interesting and I thought I had grasped the overall concepts. 261 more words

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I'm confident!

Review of Module 6 of Data Analysis for Social Scientists (MITx, edX) – Assessing and Deriving Estimators – Confidence Intervals, and Hypothesis Testing

The material felt much easier to grasp compared to the previous weeks. 178 more words

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