1. Collecting Data Name Number of Siblings Preferred Football Team Star Sign Hand Span

2. Univariate Data Categorical: a category is recorded when the data is collected. Examples of categorical data include gender, nationality, occupation. Numerical: when data is collected a number is recorded.

3. Univariate Data There are two types of numerical data Discrete: the numbers recorded are distinct values, often whole numbers and usually the data comes from counting. Examples include number of students in a class, pages in a book. Continuous: any number on a continuous line is recorded; usually the data is produced by measuring to any desired level of accuracy. Examples include volume of water consumed, life of a battery.

4. The age of my car is numerical data TRUEFALSE

5. The colour of my car is categorical data TRUEFALSE

6. The number of cars in the car park would be considered numerical & continuous data. TRUEFALSE

7. If I rate my driving experience of some test cars between one and ten, this is considered numerical & discrete data. TRUEFALSE

8. Categorical data has a specific graduated order TRUEFALSE

9. Continuous numerical data can be measured TRUEFALSE

10. If 1 = satisfied, 2 = indifferent & 3 = dissatisfied, I am collecting categorical data TRUEFALSE

11. I cannot get a mean if the data is categorical TRUEFALSE

12. Univariate Data Exercise 1A – 3 & 4

13. Univariate Data Summarising data Frequency tables: may be used with both categorical and numerical data. Class intervals are used to group continuous numerical data or to group discrete data where there is a large range of values.

14. Categorical Data Favourite teamFrequency% Frequency Collingwood1212/35 * 100 = 34% Essendon514% Bulldogs1543% Carlton39% Total35100%

15. Categorical Data Bar Graph / Column Graph

16. Percentaged Segmented Bar Chart

17. Numerical Data Dot Plots Dots plots are used with discrete data and small samples Number of siblings 12345

18. Numerical Data Number of Siblings FrequencyPercentage Frequency 022/25*100 = 8% 1416% 21248% 3728% 25100%

19. Numerical Data Histogram

20. Numerical Data HandspanFrequencyPercentage Frequency 200 – 2091010/30 * 100 = 33% 210 – 2191550% 220 – 229310% 230 – 23927% 30100%

21. Numerical data Histogram

22. Mode The mode is the most commonly occurring category, value or interval.

23. Numerical Data Stem and Leaf Plots Stem and Leaf Plots display the distribution of numerical data (both discrete and continuous) as well as the actual data values. An ordered stem and leaf plot is obtained by ordering the numbers in the leaf in ascending order. A stem and leaf plot should have at least 5 numbers in the stem

24. Numerical Data Stem and Leaf Plots StemLeaf 201 2 2 5 6 210 1 2 222 3 8 23 240 2 240represents 240

25. Numerical Data Stem and Leaf Plots Sometimes it may be necessary to split the stems in order to obtain the required number of stems. Consider the data 124681016195

26. Numerical Data Describing a distribution Shape Generally one of three types Symmetric Positively Skewed Negatively Skewed

27. Numerical Data Shape Symmetric Symmetric (same shape either side of the centre)

28. Numerical Data Shape: Positively Skewed Positively skewed : tails off to the right

29. Numerical Data Shape: Negatively Skewed Negatively skewed : tails off to the left

30. Centre The centre is the value which has the same number of scores above as below.

31. Spread The maximum and minimum values should be used to calculate the range. Range = Maximum Value – Minimum Value

32. Outliers Outliers are extreme values well away from the majority of the data

33. Describe this distribution

34. Questions from Chapter One Neat Theory book Neat Practical book Exercise 1B Page 7-8 Questions 2,4,6,8 Exercise 1C Pages 14-15 Questions 1-7 Exercise 1E Page 26 Question 1 Exercise 1D Pages 19-21 Questions 1 - 4 Exercise 1E Pages 26-28 Questions 2,3,4,6,7,8 Chapter One Review Pages 30 – 34