POL222 - Descriptive Statistics

Types of data

Categorical, ordinal, continous

Categorical

Qualitative in nature, you cannot rank the data

Examples of categorical data

city names, regime type, colours

Ordinal

Order can be ranked, but distance between values is unclear

Examples of ordinal data

education level, room for milk in coffee

Continous

Order can be ranked and the distance between values has meaning

Examples of continuous data

Temperature, money

Changing data type: Continuous → Ordinal or Categorical

Done to simplify the data or if we only care about general levels

Changing data type: Ordinal → Categorical

Done if order is unclear or not meaningful

Mean

Sum of all values/number of all values

Median

Middle value when data is sorted

Mode

Values that appear the most often

Central tendency

Mean, median and mode

Variation (dispersion)

How spread out the values/data are (only applies to rank data)

Variation calculation methods

Inter-quartile range, standard deviation

Inter-quartile range (IQR)

The difference of the 3rd and 1st quartile

Grouped mean

The average of grouped data using midpoints

Symmetric division

Mean = median

right-skewed distribution

median < mean

left-skewed distribution

median > mean