1. Types of data Categorical Nominal Ordinal Numeric Discrete Continuous C

2. 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.

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

4. 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.

5. Numeric Discrete Examples Suitable graphical representation Data can only have a finite number of values Number of peas in a pod, Age in years (as opposed to age) Bar Chart, pie chart, line graph, stemplot Continuous Data can assume an infinite number of values between any 2 given values. Students height may be 1.4325m 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.

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

7. Types of data 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) C

8. Pie Chart

9. Bar Chart

10. Trend Graph

11. Pictogram Brid: Aisling: Padraic: Colm: Key : represents 4 books

12. Height cm150-154155-159160-164165-169170-174175-179180-185 Frequency2312161854

13. Height cm150-154155-159160-164165-169170-174175-179180-185 Frequency2312161854

14. Areas Of Rectangles Key =1

15. Area =18 Area =12 Key =1

16. Height /cm150 -159160-164165-169170-174175-184 Frequency51216189

17. 14 15 16 17 18 19 779 0022223455567 011 1223457 012 8 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, 157, 159, 160, 160, 162, 162, 162, 162, 163, 164, 165, 165, 165, 166, 167, 168, 170, 171, 171, 171, 172, 172, 173, 174, 175, 177, 180, 181, 182. N = 30 ( Sample Size) Height in cm. 182= 182 Key/ Legend

18. 14 15 16 17 18 19 779 0022223455567 0111223457 012 8 Rotating a Stemplot gives a Histogram.

19. 14 15 16 16 5 17 18 19 150, 154, 154, 155, 157, 159, 160, 160, 160, 161, 161, 162, 162, 162, 163, 163, 164, 164, 165, 165, 166, 166, 167, 167, 168, 169, 170, 170, 173, 177. Canadian Girls ( Sample of 30) Ordered. Height in cm Back to Back Stemplot. 182= 182 Key/ Legend 779 00222234 0111223457 012 555678 Sample A. Irish girls 22111000 440975 23344 65577698 3007 Sample B. Canadian girls

20. Word Bank StatisticsData categoriesHistogramRange Data Numeric discreteFrequency densityDispersion Sample spaceNumeric continuous Stemplot/ stem & leaf diagram Clusters QuestionnaireCategorical nominal Significant/ Levels of significance outliers Fair testCategorical ordinal median Tally/FrequencyClass intervals: Equal or unequal