1. 1 Pertemuan 01 PENDAHULUAN: Data dan Statistika Matakuliah: I0262-Statiatik Probabilitas Tahun: 2007

2. 2 Outline Materi: Peranan dan Jangkauan Statistika Diagram Dahan dan Daun Sebaran Frekuensi

3. 3 Business Basic Statistics Introduction and Data Collection

4. 4 PERANAN DAN Jangkauan Statistika Why a Manager Needs to Know About Statistics The Growth and Development of Modern Statistics Some Important Definitions Descriptive Versus Inferential Statistics

5. 5 Peranan dan Jangkauan Statistika Why Data are Needed Types of Data and Their Sources Design of Survey Research Types of Sampling Methods Types of Survey Errors (continued)

6. 6 Why a Manager Needs to Know About Statistics To Know How to Properly Present Information To Know How to Draw Conclusions about Populations Based on Sample Information To Know How to Improve Processes To Know How to Obtain Reliable Forecasts

7. 7 The Growth and Development of Modern Statistics Needs of government to collect data on its citizenry The development of the mathematics of probability theory The advent of the computer

8. 8 Some Important Definitions A Population (Universe) is the Whole Collection of Things Under Consideration A Sample is a Portion of the Population Selected for Analysis A Parameter is a Summary Measure Computed to Describe a Characteristic of the Population A Statistic is a Summary Measure Computed to Describe a Characteristic of the Sample

9. 9 Population and Sample PopulationSample Use parameters to summarize features Use statistics to summarize features Inference on the population from the sample

10. 10 Statistical Methods Descriptive Statistics –Collecting and describing data Inferential Statistics –Drawing conclusions and/or making decisions concerning a population based only on sample data

11. 11 Descriptive Statistics Collect Data –E.g., Survey Present Data –E.g., Tables and graphs Characterize Data –E.g., Sample Mean =

12. 12 Inferential Statistics Estimation –E.g., Estimate the population mean weight using the sample mean weight Hypothesis Testing –E.g., Test the claim that the population mean weight is 120 pounds Drawing conclusions and/or making decisions concerning a population based on sample results.

13. 13 Why We Need Data To Provide Input to Survey To Provide Input to Study To Measure Performance of Ongoing Service or Production Process To Evaluate Conformance to Standards To Assist in Formulating Alternative Courses of Action To Satisfy Curiosity

14. 14 Data Sources Observation Experimentation Survey Print or Electronic Data Sources

15. 15 Types of Data

16. 16 Design of Survey Research Choose an Appropriate Mode of Response –Reliable primary modes Personal interview Telephone interview Mail survey –Less reliable self-selection modes (not appropriate for making inferences about the population) Television survey Internet survey Printed survey in newspapers and magazines Product or service questionnaires

17. 17 Reasons for Drawing a Sample Less Time Consuming Than a Census Less Costly to Administer Than a Census Less Cumbersome and More Practical to Administer Than a Census of the Targeted Population

18. 18 Types of Sampling Methods Quota Samples Non-Probability Samples (Convenience) JudgementChunk Probability Samples Simple Random Systematic Stratified Cluster

19. 19 Probability Sampling Subjects of the Sample are Chosen Based on Known Probabilities Probability Samples Simple Random SystematicStratifiedCluster

20. 20 Organizing Numerical Data 2 144677 3 028 4 1 Numerical Data Ordered Array Stem and Leaf Display Frequency Distributions Cumulative Distributions Histograms Polygons Ogive Tables 41, 24, 32, 26, 27, 27, 30, 24, 38, 21 21, 24, 24, 26, 27, 27, 30, 32, 38, 41

21. 21 RawData in Raw Form (as Collected): 24, 26, 24, 21, 27, 27, 30, 41, 32, 38 Ordered ArraySmallest to LargestData in Ordered Array from Smallest to Largest: 21, 24, 24, 26, 27, 27, 30, 32, 38, 41 Stem-and-Leaf Display: Stem and Leaf Display (continued) 2 1 4 4 6 7 7 3 0 2 8 4 1

22. 22 Tabulating and Graphing Numerical Data 2 144677 3 028 4 1 Numerical Data Ordered Array Stem and Leaf Display Histograms Ogive Tables 41, 24, 32, 26, 27, 27, 30, 24, 38, 21 21, 24, 24, 26, 27, 27, 30, 32, 38, 41 Frequency Distributions Cumulative Distributions Polygons

23. 23 Tabulating Numerical Data: Frequency Distributions Sort Raw Data in Ascending Order 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 Find Range: 58 - 12 = 46 Select Number of Classes: 5 (usually between 5 and 15) Compute Class Interval (Width): 10 (46/5 then round up) Determine Class Boundaries (Limits): 10, 20, 30, 40, 50, 60 Compute Class Midpoints: 15, 25, 35, 45, 55 Count Observations & Assign to Classes

24. 24 Frequency Distributions, Relative Frequency Distributions and Percentage Distributions Class Frequency 10 but under 20 3.15 15 20 but under 30 6.30 30 30 but under 40 5.25 25 40 but under 50 4.20 20 50 but under 60 2.10 10 Total 20 1 100 Relative Frequency Percentage Data in Ordered Array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

25. 25 Graphing Numerical Data: The Histogram Data in Ordered Array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58 No Gaps Between Bars Class Midpoints Class Boundaries

26. 26 Graphing Numerical Data: The Frequency Polygon Class Midpoints Data in Ordered Array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

27. 27 Tabulating Numerical Data: Cumulative Frequency Lower Cumulative Cumulative Limit Frequency % Frequency 10 0 0 20 3 15 30 9 45 40 14 70 50 18 90 60 20 100 Data in Ordered Array: 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

28. 28 Graphing Numerical Data: The Ogive (Cumulative % Polygon) Class Boundaries (Not Midpoints) Data in Ordered Array : 12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

29. 29 Graphing Bivariate Numerical Data (Scatter Plot)

30. 30 Tabulating and Graphing Univariate Categorical Data Categorical Data Tabulating Data The Summary Table Graphing Data Pie Charts Pareto Diagram Bar Charts

31. 31 Graphing Univariate Categorical Data Categorical Data Tabulating Data The Summary Table Graphing Data Pie Charts Pareto Diagram Bar Charts

32. 32 Bar Chart (for an Investor’s Portfolio)

33. 33 Pie Chart (for an Investor’s Portfolio) Percentages are rounded to the nearest percent Amount Invested in K$ Savings 15% CD 14% Bonds 29% Stocks 42%

34. 34 Pareto Diagram Axis for line graph shows cumulative % invested Axis for bar chart shows % invested in each category