1. Basic terms

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

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

4. 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

5. Steps of Statistical analysis
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

6. Hibaforrások felméréseknél
Construction
Validity, specification error
Measurement
Measurement error
Responses
Process error
Prepared data
Analysis
Population
Target population
Covering error
Sampling frame
Sampling error
Sample
Nonresponse error
Respondents
Feldolgozási hiba: könyv, 9999 kód, gépelési hiba, ellentmondó adatok,
Tárki 2013 euval nem szimpatizálók aránya
Lefedettség: hinyzó egyed, duplikátum, rossz sztártum, többlet, nem cél. Csop tag, csak netes felm.
Mintavételi hiba:

7. 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

8. Registers List of the individuals For instance Economic units
Administrative units

9. 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, …

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

11. Nominal Ordinal Interval scale Ratio scale Levels of Measurement
Data can be only differentiated
Data can be differentiated and arranged
Differences can be calculated and are meaningful
Differences and ratios can be calculated and are meaningful
The choice of a statistical techniuqe is based on the measurement level.

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

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

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

15. Aim: Compress the information
Tables and charts
Aim: Compress the information
Tables
Charts

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

17. Charts Scatter Line Pictogram Cartogram
Bar
Pie
Pictogram
Cartogram
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.
A bar chart can be used to depict any of the levels of measurement (nominal, ordinal, interval, or ratio).

18. Data visualization
Presentation of data in a pictorial or graphical format.

19. History

20. Issues trenDS Small area Vitalize data
Correct but understandable charts
Suitable charts
No redundant information
Suitable design
New platforms, mobile devices, open source solutions
Interactive, real time solutions 3D
Geodata
New datasources

21. Line

22. Radar

23. Proportional
Perceive

25. Source: http://www.datapine.com/blog/misleading-statistics-and-data/

28. Pie charts (1) Source: https://en.wikipedia.org/wiki/Misleading_graph
In the misleading pie chart, Item C appears to be at least as large as Item A, whereas in actuality, it is less than half as large.
Source:

30. pICTOGRAMS Source: https://en.wikipedia.org/wiki/Misleading_graph
When using pictograms in bar graphs, they should not be scaled uniformly, as this creates a perceptually misleading comparison.[12]
The area of the pictogram is interpreted instead of only its height or width.[13] This causes the scaling to make the difference appear to be squared.[13] In the improperly scaled pictogram bar graph, the image for B is actually 9 times as large as A.
Source:

31. Wordcloud
A greedy algorithm is an algorithmic paradigm that follows the problem solving heuristic of making the locally optimal choice at each stage with the hope of finding a global optimum.
Illusion
preception
Source :
Source: Steele & Noah: Beautiful Visualization, O Reilly, 2010
Source :