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Making rigorous conclusions

In this part we introduce modelling and statistical inference for making data-based conclusions.
We discuss building, interpreting, and selecting models, visualizing interaction effects, and prediction and model validation.
Statistical inference is introduced from a simulation based perspective, and the Central Limit Theorem is discussed very briefly to lay the foundation for future coursework in statistics.

The RStudio Cloud workspace for Data Science Course in a Box project is here.
You can join the workspace and play around with the sample application exercises.

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Slides, videos, and application exercises

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Modelling data

**Unit 4 - Deck 1: The language of models**

**Unit 4 - Deck 2: Fitting and interpreting models**

**Unit 4 - Deck 3: Modelling nonlinear relationships**

**Unit 4 - Deck 4: Models with multiple predictors**

**Unit 4 - Deck 5: More models with multiple predictors**

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Classification and model building

**Unit 4 - Deck 6: Logistic regression**

**Unit 4 - Deck 7: Prediction and overfitting**

**Unit 4 - Deck 8: Feature engineering**

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Model validation

**Unit 4 - Deck 9: Cross validation**

**The Office + Feature engineering, Pt. 1**

**The Office + Cross validation, Pt. 2**

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Uncertainty quantification

**Unit 4 - Deck 10: Quantifying uncertainty**

**Unit 4 - Deck 11: Bootstrapping**

**Unit 4 - Deck 12: Hypothesis testing**

**Unit 4 - Deck 13: Inference overview**

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Labs

**Lab 10: Grading the professor, Pt. 1**

Fitting and interpreting simple linear regression models

**Lab 11: Grading the professor, Pt. 2**

Fitting and interpreting multiple linear regression models

**Lab 12: Smoking while pregnant**

Constructing confidence intervals, conducting hypothesis tests, and interpreting results in context of the data

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Homework assignments

**HW 7: Bike rentals in DC**

Exploratory data analysis and fitting and interpreting models

**HW 8: Exploring the GSS**

Fitting and interpreting models

**HW 9: Modelling the GSS**

Model validation and inference