1. SESSION 1 & 2 Last Update 15 th February 2011 Introduction to Statistics

2. Lecturer:Florian Boehlandt University:University of Stellenbosch Business School Domain:http://www.hedge-fund-analysis.net

3. Learning Unit 1 (10 Sessions) Give a description of statistical techniques Construct a frequency distribution table Represent data in tabular or graphical form Distinguish between different graphical representation forms

4. Session 1 & 2 Concepts and Definitions Terminology Data types Graphical representations

5. Definitions Statistics is the name given to the science of collecting facts, typically in numerical form, and studying or analysing them. The facts, or data, can cover a wide range of subjects. The science of statistics deals with the methods used in the collection, presentation, analysis and interpretation of data.

6. Definitions cont. Statistics is a way to get information from data.

7. Descriptive Statistics Methods of organizing, summarizing and presenting data in a convenient and informative way. Numerical techniques to summarize data: Measure of Central location or Measure of Variability.

8. Inferential Statistics Body of methods used to draw conclusions or inferences about characteristics of a population based on sample data. “Estimation”

9. Statistical Concepts The Population is Group of all items of interest to the statistical practitioner. The Sample is a set of data drawn from the population. A descriptive measure of the sample is called a statistic. Statistical Inference is the process of making an estimate, prediction, or decision about a population based on sample data.

10. Statistical Concepts A Variable is some characteristic of a population or sample. The values of the variable are the possible observations of the variable. Data are the observed values of a variable.

11. Example Stock ConceptExample VariableAnglo American PLC Closing Price ValueReal numbers (fractional) DataTime Series of all Closing Prices (Date – Closing Price) SampleJSE ALSI PopulationAll JSE-listed companies Statistical Inference

12. Example Test Marks ConceptExample VariableMark on statistic exam ValueExam Marks (0 to 100) DataTest marks of k students SampleStudents from iKapa campus PopulationAll Vega students Statistical Inference

13. Data Types Interval data are real numbers, such as heights, weights, incomes, and distance. Example stock performance in %: 1/3/2011-1.34 1/4/20110.00 …… 1/31/2011+2.05

14. Data Types The values of nominal data are categories. Nominal data is often recorded by arbitrarily assigning a number to each category Example Marital Status: Single1 Married2 Divorced3 Widowed4

15. Data Types Ordinal data appear nominal but their values are in order. Example students evaluating course: Poor1 Fair2 Good3 Very Good4 Excellent5 Codes are arbitrary. Thus, no meaningful interpretation of the results.

16. Calculations Data Types All calculations are allowed on interval data (e.g. calculating the average). Codes in nominal data are arbitrary. Averages are not meaningful; Observations can be described counting the number of each category and report the frequencies frequencies.

17. Example Frequencies Original responses: 1 2 2 2 4 1 2 2 1 3 4 4 4 3 Frequency table / Proportions: CategoryCodeFrequency Single13 Married25 Divorced32 Widowed44 Single1 Married2 Divorced3 Widowed4

18. Calculations Data Types The only permissible calculations for ordinal data are ones involving a ranking process (e.g. the median).

19. Data Collection Primary Data vs Secondary Data

20. Primary Data -Questionnaires / Surveys -Cannot be looked up elsewhere -The collection is performed by observation, survey, experimental research conducted for a part of total population under consideration - sample

21. Data Collection Discrete Data vs Continuous Data

22. Discrete Data A random variable whose observations can take on only specific values, usually only integer (whole number) values, is referred to as a discrete random variable. Example – Statistic test marks (0 to 100) – Number of students in a class room – The outcomes of tossing a die – The outcome of tossing a coin (binary)

23. Continuous Data Data that are measured on a scale, such as mass or temperature, are called continuous data. Example – Time it takes a student to complete a statistics test – The weight / height of a student – The return on a stock

24. Graphical Techniques Nominal Data: Bar charts / pie charts Interval data: Frequency distribution tables and histograms