# GGR270H1 Lecture : GGR270 Lecture #2 Sept. 19 2012.pdf

## Document Summary

Quantitative numerical e. g. number of students who : discrete (1,2,3,4 ) or continuous (1. 5, 2. 76, 3. 4 ) Multivariate (whole bunch of variables) E. g. occupation type, gender, place of birth: ordinal, stronger scale as it allows data to be ordered or ranked. 12 largest towns in a region, income by group (low, middle, high) Interval: unit distance separating numbers is important. Temperature (f or c), taxable income ($) Temperature (kelvin), income from all sources ($), population of a city. In practice, we consider interval/ratio scales together. 5 to 12 intervals or categories: 1 + 3. 3 log10 (# of observations) Must be mutually exclusive and collectively exhaustive (must fit into a category and only one category) Intervals must be the same width: example: # of classes: k = 1 + 3. 3 log10 (10) = 4. 3 rounded up to 5. Better to round upwards than downwards in this case. We round the decimal places by the ones in our original data.