1. Statistics I.

2. Course materials Lecture notes Coospace http://www2.eco.u-szeged.hu/stat/

3. Contacts Room 316 Email: kovacs.peter@eco.u-szeged.hukovacs.peter@eco.u-szeged.hu Coospace kazar.klara@eco.u-szeged.hu

4. Parts of Exam Seminar: 2 computer based test Colloquium Written exam in two parts

5. Introductions, notes Statistics and other subjects IT and Statistics How can you learn? Interactive lessons

6. Aims Improve your statistical literacy In the case of a given problem Identify the applicability of statistics as a way of solution Identify the applicable statistical methods Interpretation of the data and results

7. Topics Semester 1 Descriptive statistics Comparison of data Time series Semester 2 Inferential statistics  Hypothesis, regression, etc.

8. Basic terms

9. What are the aims and objects of statistics? Where can we encounter in statistics? What is the importance of statistics?

10. Who Uses Statistics? Statistical techniques are used extensively by marketing, accounting, quality control, consumers, professional sports people, hospital administrators, educators, politicians, physicians, etc...

11. What is Meant by Statistics? Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions. Examination of mass phenomenon

12. Planing Start from a problem What is the question? Target group? Data collection Use existing data? Sampling? Check and clean the data Analysis Presentations feedbacks Steps of Statistical analysis

13. Population or sample A population is a collection of all possible individuals, objects, or measurements of interest. A sample is a portion, or part, of the population of interest

14. registers List of the individuals For instance Economic units Administrative units

16. Properties of the individuals: variables - What is the codomain? For example: Gender: male or female weight 1,2,3,…,50,….kg -10; 11-20; 21-30, …

17. Summary of Types of Variables

18. Levels of measurement Categorical Nominal Ordinal Noncategorial, quantitative (metric, scale) Interval Ratio

19. Levels of Measurement Nominal level: Data that is classified into categories and cannot be arranged in any particular order.  EXAMPLES: eye color, gender, religious affiliation.

20. Levels of Measurement Ordinal level: involves data arranged in some order, but the differences between data values cannot be determined or are meaningless.  EXAMPLE: During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4.

21. Levels of Measurement Interval level: similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point.  EXAMPLE: Temperature on the Fahrenheit scale.

22. Levels of Measurement Ratio level: the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement.  EXAMPLES: Monthly income of surgeons, or distance traveled by manufacturer’s representatives per month.

23. Types of Statistics Descriptive Statistics: Methods of organizing, summarizing, and presenting data in an informative way. Nominal level: tables, graph, mode Ordinak level: tables, graph, mode, median Quantitative variable: tables, graphs, mode, median, mean, dispersion, skewness

24. Types of Statistics Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample.

25. Comparison of data Difference Ratio Problems: percent/ percentage point

26. Tables and charts Aim: Compress the information 1. Tables 2. Charts

27. Formal requiremets title Units, titles of rows and columns sum Data source notices Order of categories?

28. Charts Scatter Line Bar Pie Pictogram Cartogram

29. Scatter Forrás: saját szerkesztés

30. Line

31. Radius

32. Bar Chart A bar chart can be used to depict any of the levels of measurement (nominal, ordinal, interval, or ratio).

33. Example3 EXAMPLE 3: Construct a bar chart for the number of unemployed per 100,000 population for selected cities during 2001

34. Bar Chart for the Unemployment Data

35. Pie Chart A pie chart is useful for displaying a relative frequency distribution. A circle is divided proportionally to the relative frequency and portions of the circle are allocated for the different groups.

36. EXAMPLE 4 continued EXAMPLE 4: A sample of 200 runners were asked to indicate their favorite type of running shoe. Draw a pie chart based on the following information.

37. Pie Chart for Running Shoes

38. Pictogram 1 unit=1000 pigs Pigs in a farm(2011)

39. Cartogram Forrás: OMSZ Heatmap of Hungary (2010.09.02. 14:00) °C

40. Forrás: Eurostat