An ORnery problem
Some time ago, I wrote about missing values and how they complicate the life of an applied statistician. A particularly tricky case concerns logical variables , and I give a more detailed explanation here. Suppose X is a variable representing whether a person is at risk for developing type-2 diabetes . Two known risk factors are ( A ) being older and ( B ) being overweight. If we had a database containing the age and weight of each person in a group, we could compute X using the following logical expression : X = A OR B . ( X , A , and B are known as logical variables, and they each take values TRUE or FALSE according to whether the corresponding condition holds.) But what happens if some ages and weights are missing from the database? Fortunately statistical software packages like R and SPSS have built-in rules that will correctly evaluate the logical expression, even if A or B (or both) are missing. The complete truth table is as follows (where T means TRUE, F means FALSE, a...