Topics: Prediction; Model Fit; Cross Validation

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Apr 16 (Monday)

Topics:

Due

Readings

• Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer (2015) “Prediction Policy Problems” DOI:10.1257/aer.p20151023
• James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2013) An Introduction to Statistical Learning with Applications in R, Ch 5. “Resampling Methods”
• Hesterberg, Tim (2015) “What Teachers Should Know about the Resampling in the Undergraduate Statistics Curriculum“

Optional

• Shalizi, Cosma. “F-Tests, R2, and Other Distractions“
• Mullainathan, Sendhil, and Jann Spiess (2017) “Machine Learning: An Applied Econometric Approach”, Journal of Economic Perspectives DOI:10.1257/jep.31.2.87
• Shmueli, Galit (2010) “To Explain or to Predict?” Statistical Science DOI:10.1214/10-STS330
• Resnick, Brian (2017) “What a nerdy debate about p-values shows about science — and how to fix it”, vox.com.
• Geoff Cumming (2014) “The New Statistics: Why and How”, Psychological Science DOI:10.1177/0956797613504966

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Apr 18 (Wednesday)

Topics:

Due

Readings

None

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Apr 20 (Friday)

Topics:

Due

Readings

None