Session Ready
Exercise

Smelling the danger of mean imputation

One of the most popular imputation methods is the mean imputation, in which missing values in a variable are replaced with the mean of the observed values in this variable. However, in many cases this simple approach is a poor choice. Sometimes a quick look at the data can already alert you to the dangers of mean-imputing.

In this chapter, you will be working with a subsample of the Tropical Atmosphere Ocean (tao) project data. The dataset consists of atmospheric measurements taken in two different time periods at five different locations. The data comes with the VIM package.

In this exercise you will familiarize yourself with the data and perform a simple analysis that will indicate what the consequences of mean imputation could be. Let's take a look at the tao data!

Instructions 1/3
undefined XP
  • 1
  • 2
  • 3
  • Look at the first 10 rows of the tao data and get familiar with the variables.