Chapter 4. Dealing with Data and Numerical Issues

The recipes in this chapter are as follows:

• Clipping and filtering outliers
• Winsorizing data
• Measuring central tendency of noisy data
• Normalizing with the Box-Cox transformation
• Transforming data with the power ladder
• Transforming data with logarithms
• Rebinning data
• Applying logit() to transform proportions
• Fitting a robust linear model
• Taking variance into account with weighted least squares
• Using arbitrary precision for optimization
• Using arbitrary precision for linear algebra