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Collecting Data

Collecting Data. Name Number of Siblings Preferred Football Team Star Sign Hand Span. Univariate Data. Categorical: a category is recorded when the data is collected. Examples of categorical data include gender, nationality, occupation.

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Collecting Data

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  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. TRUE FALSE The age of my car is numerical data

  5. TRUE FALSE The colour of my car is categorical data

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

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

  8. TRUE FALSE Categorical data has a specific graduated order

  9. TRUE FALSE Continuous numerical data can be measured

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

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

  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

  15. Categorical DataBar Graph / Column Graph

  16. Percentaged Segmented Bar Chart

  17. Numerical DataDot Plots • Dots plots are used with discrete data and small samples 1 2 3 4 5 Number of siblings

  18. Numerical Data

  19. Numerical DataHistogram

  20. Numerical Data

  21. Numerical data Histogram

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

  23. Numerical DataStem 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 DataStem and Leaf Plots • Stem Leaf • 20 1 2 2 5 6 • 21 0 1 2 • 22 2 3 8 • 23 • 24 0 2 24 0 represents 240

  25. Numerical DataStem and Leaf Plots • Sometimes it may be necessary to split the stems in order to obtain the required number of stems. • Consider the data 12 4 6 8 10 16 19 5

  26. Numerical DataDescribing a distribution • Shape • Generally one of three types • Symmetric • Positively Skewed • Negatively Skewed

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

  28. Numerical DataShape: Positively Skewed • Positively skewed : tails off to the right

  29. Numerical DataShape: 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

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