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
Introduction
In the real world, data rarely matches textbook definitions and examples. We have to deal with issues such as faulty hardware, uncooperative customers, and disgruntled colleagues. It is difficult to predict what kind of issues you will run into, but it is safe to assume that they will be plentiful and challenging. In this chapter, I will sketch some common approaches to deal with noisy data, which are based more on rules of thumb than strict science. Luckily...
