# 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.

## Slides, videos, and application exercises

### Modelling data

### Classification and model building

### Model validation

### Uncertainty quantification

## 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

## Homework assignments

**HW 7: Bike rentals in DC**

Exploratory data analysis and fitting and interpreting models