Types of data C Numeric Categorical Nominal Ordinal

Types of data C Numeric Categorical Nominal Ordinal Discrete Continuous

Categorical Data: The answer to “what colour is your hair? ” produces categorical data, which fits into the categories “black”, “brown”, “red”, “blonde”, “other”. • Nominal e. g. naming or classifying e. g. blue eyes, brown eyes, blood group types, makes of car, gender, favourite subject/sport, pets. These data cannot be organized according to any ‘natural’ order. • Ordinal – involves some order e. g. first, second, third, Jan, Feb. , March, schoolwork pressure – a lot, some, very little, none.

Categorical Nominal Examples Can be identified by particular names or Gender : female or categories, and male cannot be Hair colour: black, organized according blonde etc to any natural order. Favourite sport: Suitable graphical representation Bar Chart, Pictogram, Pie Chart soccer, rugby etc Ordinal Identified by categories which can be ordered in some way Watching TV: never, Bar Chart, Pictogram, rarely, sometimes , Pie Chart a lot

Numeric Data: Data represented by real numbers Discrete – distinct values, e. g. how many people live in each household i. e. cannot have 2. 75 people in a household Continuous – infinite number of values between any 2 given values e. g. heights, weights, lengths in the long jump, high jump.

Numeric Suitable graphical representation Discrete Examples Data can only have a finite number of values Number of peas in a pod, Bar Chart, pie chart, Age in years (as opposed line graph, to age) stemplot Continuous Data can assume an infinite number of values between any 2 given values. Students height may be 1. 4325 m Height, arm span, foot length. Histogram, line graph, stemplot In practice no scale is truly continuous because measurement is restricted by some level of accuracy.

Types of data C Primary Data (Collected by person/people who use it) Secondary Data (Used by different people than those who collected it)

Types of data C Univariate Data (Information collected about one variable from each person eg hair colour) Bivariate Data (Information collected about 2 variables from each person eg hair colour and eye colour)

Pie Chart 15 17 16

Bar Chart 40 35 30 25 20 16 15 10 17 5 15 0 1 2 3

Trend Graph 40 Car Sales 35 30 25 20 15 Dublin Sales 10 Donegal Sales 5 0 January February March April May June July

Pictogram • • Brid: Aisling: Padraic: Colm: • Key : represents 4 books

Height cm 150 -154 155 -159 160 -164 165 -169 170 -174 175 -179 180 -185 Frequency 2 3 12 16 18 5 4 Histogram 20 18 16 frequency 14 12 10 8 6 4 2 0 150 180 155 185 160 165 Height / cm 170 175

Height cm 150 -154 155 -159 160 -164 165 -169 170 -174 175 -179 180 -185 Frequency 2 3 12 16 18 5 4 Histogram 20 18 16 14 frequency 12 10 8 6 4 2 0 150 -154 155 -159 160 -164 165 -169 Height / cm 170 -174 175 -179 180 -185

Areas Of Rectangles Key =1

Key Area =18 =1 Area =18 Area =12

Height /cm 150 -159 160 -164 165 -169 170 -174 175 -184 Frequency 5 12 16 18 9 Histogram 20 18 Frequeny Density 16 14 12 10 8 6 4 2 0 155 160 165 Height / cm 170 175 180 185

Original Data ( Sample of 30) 160, 162, 170, 171, 175, 172, 165, 171, 164, 177, 171, 160, 172, 162, 159, 173, 157, 166, 165, 174, 181, 165, 168, 162, 182, 167, 157, 162, 163, 180. Ordered Data 157, 159, 160, 162, 163, 164, 165, 166, 167, 168, 170, 171, 172, 173, 174, 175, 177, 180, 181, 182. N = 30 ( Sample Size) Height in cm. 14 15 7 7 9 16 0 0 2 2 3 4 5 5 17 0 1 1 1 2 2 3 4 5 7 18 0 1 2 19 18 2 = 182 Key/ Legend 5 6 7 8

Rotating a Stemplot gives a Histogram. 14 15 7 7 9 16 0 0 2 2 3 4 5 5 5 6 7 8 17 0 1 1 1 2 2 3 4 5 7 18 0 1 2 19

Canadian Girls ( Sample of 30) Ordered. Height in cm 150, 154, 155, 157, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 173, 177. Back to Back Stemplot. Sample B. Canadian girls Sample A. Irish girls 14 4 4 3 3 2 9 7 5 4 4 0 15 7 7 9 2 2 1 1 1 0 0 0 16 0 0 2 2 9 8 7 7 6 6 5 5 165 5 6 7 8 7 3 0 0 17 0 1 1 1 2 2 18 0 1 2 19 18 2 = 182 Key/ Legend 3 4 5 7

Word Bank Statistics Data categories Histogram Range Data Numeric discrete Frequency density Dispersion Sample space Numeric continuous Stemplot/ stem & leaf Clusters diagram Questionnaire Categorical nominal Significant/ Levels of significance Fair test Categorical ordinal Tally/Frequency Class intervals: Equal or unequal median outliers