statistical analysis ppt of data analysis in the world of nitin
- 1. Source: Pearson Education, Inc. (2011) STATISTICAL ANALYSIS with Software Application BASIC CONCEPTS of STATISTICS
- 2. Contents 1. The Science of Statistics 2. Types of Statistical Applications in Business 3. Fundamental Elements of Statistics 4. Processes 5. Types of Data 6. Collecting Data 7. The Role of Statistics in Managerial Decision Making 8. Data Presentation
- 3. Learning Objectives 1. Introduce the field of statistics 2. Demonstrate how statistics applies to business 3. Establish the link between statistics and data 4. Identify the different types of data and data- collection methods 5. Differentiate between population and sample data 6. Differentiate between descriptive and inferential statistics 7. Present data in a meaningful way.
- 4. The Science of Statistics
- 5. What Is Statistics? Why? 1. Collecting Data e.g., Survey 2. Presenting Data e.g., Charts & Tables 3. Characterizing Data e.g., Average Data Analysis Decision- Making © 1984-1994 T/Maker Co. © 1984-1994 T/Maker Co.
- 6. What Is Statistics? Statistics is the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information.
- 7. Types of Statistical Applications in Business
- 8. Application Areas • Economics – Forecasting – Demographics • Sports – Individual & Team Performance • Engineering – Construction – Materials • Business – Consumer Preferences – Financial Trends
- 9. Statistics: Two Processes Describing sets of data and Drawing conclusions (making estimates, decisions, predictions, etc. about sets of data based on sampling)
- 11. Descriptive Statistics 1. Involves • Collecting Data • Presenting Data • Characterizing Data 2. Purpose • Describe Data X = 30.5 S2 = 113 0 25 50 Q1 Q2 Q3 Q4 $
- 12. 1. Involves • Estimation • Hypothesis Testing 2. Purpose • Make decisions about population characteristics Inferential Statistics Population?
- 14. Fundamental Elements 1. Experimental unit • Object upon which we collect data 2. Population • All items of interest 3. Variable • Characteristic of an individual experimental unit 4. Sample • Subset of the units of a population • P in Population & Parameter • S in Sample & Statistic
- 15. Fundamental Elements 1. Statistical Inference • Estimate or prediction or generalization about a population based on information contained in a sample 2. Measure of Reliability • Statement (usually qualified) about the degree of uncertainty associated with a statistical inference
- 16. Four Elements of Descriptive Statistical Problems 1. The population or sample of interest 2. One or more variables (characteristics of the population or sample units) that are to be investigated 3. Tables, graphs, or numerical summary tools 4. Identification of patterns in the data
- 17. Five Elements of Inferential Statistical Problems 1. The population of interest 2. One or more variables (characteristics of the population units) that are to be investigated 3. The sample of population units 4. The inference about the population based on information contained in the sample 5. A measure of reliability for the inference
- 18. Processes
- 19. © 2011 Pearson Education, Inc Process A process is a series of actions or operations that transforms inputs to outputs. A process produces or generates output over time.
- 20. Process A process whose operations or actions are unknown or unspecified is called a black box. Any set of output (object or numbers) produced by a process is called a sample.
- 21. Types of Data
- 22. Types of Data Quantitative data are measurements that are recorded on a naturally occurring numerical scale. Qualitative data are measurements that cannot be measured on a natural numerical scale; they can only be classified into one of a group of categories.
- 23. Types of Data Types of Data Quantitative Data Qualitative Data
- 24. Quantitative Data Measured on a numeric scale. • Number of defective items in a lot. • Salaries of CEOs of oil companies. • Ages of employees at a company. 3 52 71 4 8 943 120 12 21
- 25. Qualitative Data Classified into categories. • College major of each student in a class. • Gender of each employee at a company. • Method of payment (cash, check, credit card). p Credit
- 26. Collecting Data
- 27. Collecting Data 1. Data from a published source 2. Data from a designed experiment 3. Data from a survey 4. Data collected observationally
- 28. Obtaining Data Published source: book, journal, newspaper, Web site Designed experiment: researcher exerts strict control over units Survey: a group of people are surveyed and their responses are recorded Observation study: units are observed in natural setting and variables of interest are recorded
- 29. Samples A representative sample exhibits characteristics typical of those possessed by the population of interest. A random sample of n experimental units is a sample selected from the population in such a way that every different sample of size n has an equal chance of selection.
- 30. Random Sample Every sample of size n has an equal chance of selection.
- 31. The Role of Statistics in Managerial Decision Making
- 32. Statistical Thinking Statistical thinking involves applying rational thought and the science of statistics to critically assess data and inferences. Fundamental to the thought process is that variation exists in populations and process data. A random sample of n experimental units is a sample selected from the population in such a way that every different sample of size n has an equal chance of selection.
- 33. Nonrandom Sample Errors Selection bias results when a subset of the experimental units in the population is excluded so that these units have no chance of being selected for the sample. Nonresponse bias results when the researchers conducting a survey or study are unable to obtain data on all experimental units selected for the sample. Measurement error refers to inaccuracies in the values of the data recorded. In surveys, the error may be due to ambiguous or leading questions and the interviewer’s effect on the respondent.
- 35. Statistical Computer Packages 1. Typical Software • SPSS • MINITAB • Excel 2. Need Statistical Understanding • Assumptions • Limitations
- 37. Data Presentation Tabular Presentation is a means of arranging and presenting data in rows and columns so that the reader may easily, see, compare, and analyze them. Characteristics of a good table are: 1) simple in design; 2) logical in arrangement; and 3) easy to read.
- 38. Data Presentation Graphical Presentation is presenting numerical values or relationships in picture form. It is most effective and most convincing ways to present data result. Graphical method attracts attention; gives comprehensive view of quantitative data; more meaningful; essential facts are grasped quickly; and simple.
- 39. Data Presentation Line Graph is the oldest, simplest, most familiar and most widely used. The points are usually plotted with reference to arithmetic scale. The horizontal and vertical dimensions should bear a reasonable proportion to each other. Bar Graph used to show comparison of categories as chronological comparisons. Horizontal and Vertical arrangement of the individual bars are used when comparison of categories is being made. Pie or Circle Graph is a diagram of circular shape cut into division where each size of every section is indicative of the proportion of each component. It aims to show percent distribution of a whole into its component parts. A maximum of 5 or 6 sectors would be acceptable but 5 or less would be preferable.
- 40. Data Presentation Example: Total government revenues from taxes reached P1375 million in year 2000. The principal sources of these receipts in 1980 were P885 million in indirect taxes and the P374 million in direct taxes. The two other sources of revenues were property income of government and net donations from abroad to the government which shared P75 million and P41 million respectively.
- 41. Data Presentation Example-Table Form Table 1 Sources of Government Revenue for Year 2000 Sources of Revenue Revenue (P million) Indirect taxes 885 Direct taxes 374 Property Income 75 Net donations from abroad 41 TOTAL 1375
- 42. Data Presentation Example-Line Graph Figure 1 Sources of Government Revenues for Year 2000
- 43. Data Presentation Example-Pie or Circle Graph Figure 1 Sources of Government Revenues for Year 2000 Indirect taxes, P885 million Direct taxes, P374 million Property Income, P75 million Net donations from abroad, P41 million
- 44. Data Presentation Example-Vertical Bar Graph Figure 1 Sources of Government Revenues for Year 2000 0 100 200 300 400 500 600 700 800 900 1000 Indirect taxes Direct taxes Property Income Net donations from abroad Revenues (P millions) Sources of Revenue
- 45. Data Presentation Example-Horizontal Bar Graph Figure 1 Sources of Government Revenues for Year 2000 0 100 200 300 400 500 600 700 800 900 1000 Indirect taxes Direct taxes Property Income Net donations from abroad Revenues (P millions) Sources of Revenue
- 46. ASSESSMENT #1 1. Present the data in a well-organized and well-labeled statistical table. The following data appeared in the Asian Computer Yearbook 1979-1980, published by the Computer Publication Ltd. Computer growth during 1978 and 1979 in the ASEAN countries, namely, Indonesia, Malaysia, Philippines, Singapore, and Thailand, was shown in terms of the growth of the following: installations; computer manufacturers and agents; consultants programing services, and software houses; and service bureaus. Among the five countries, the Philippines had the most number of installations, having 196 in 1978 and increasing to 286 in 1979. Indonesia had 15 in 1978 and 102 in 1979;.Malaysia with 70 and 175; Singapore with 98 and 228; Thailand with 75 and 112; totaling to 454 units in 1978 and remarkably increasing to 903 units in 1979. Installations here referred to an in-house installation which could comprise one or more computers. It did not necessarily indicate a single computer. The increase in the number of computers was primarily accounted for by new installations. However in some countries such as Indonesia, the recorded increase in the number of installations as well as of companies in the computer business was also a function of the addition of entries which were not recorded in the previous year. Regarding computer manufacturers and agents, Singapore exhibited the most distinctive increase from 8 in 1978 to 23 in 1979. Indonesia had 6 in 1978 and 13 in 1979; Malaysia with 9 and 12; Philippines with 13 and 19; Thailand with 7 and 14; summing up to a total of 43 computer manufacturers and agents in 1978 and 81 in 1979. Then, consultants, programing services and software houses quadrupled during the 2-year period, that is from 8 in 1978 to 32 in 1979. For this case, Indonesia had 1 in 1978 and 8 in 1979; for Malaysia the number in 1978 could not be determined since no survey forms were returned but there were 4 in 1979; Philippines had 3 and 7; Singapore had 2 and 10, and Thailand had 2 and 3. Lastly, the Philippines again had the most number of service bureaus, 4 in 1978 and 19 in 1979. Indonesia and Malaysia had 1 and 5 each; Singapore with 3 and 5; Thailand with 2 and 3; totaling to all service bureaus in 1978 and 37 in 1979. 2. Portray the trend of “XXX” short-term debt since 2007 in the form of a graph. You may select the appropriate graph. The short-term debt of the XXX corporation for the year 2007-2018, in millions of pesos, is: Year Short-term debt (P millions) Year Short-term debt (P millions) 2007 124 2013 126 2008 2025 2014 59 2009 1841 2015 1706 2010 619 2016 2888 2011 915 2017 3456 2012 469 2018 3500
- 47. (continuation) 3. Write a brief narrative of the main features of the data portrayed in the graph. Figure 1 Total Sales of “XXX” Corporations, Year 2007-2018 4. Compare the high and the low common stock prices since 2007 in a graph of your choice for “XXX” Corporation. Write a brief interpretation of the main features of the data portrayed in the graph. The high and the low common stock prices for the “XXX” Corporation since 1983 are: Year High Low Year High Low 2007 40.85 30.90 2013 72.00 45.75 2008 40.85 30.00 2014 81.65 63.25 2009 40.85 29.25 2015 80.15 63.50 2010 48.00 34.00 2016 75.35 60.25 2011 64.75 32.00 2017 98.35 67.75 2012 51.75 39.65 2018 99.00 70.00 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 Year 2007 Year 2008 Year 2009 Year 2010 Year 2011 Year 2012 Year 2013 Year 2014 Year 2015 Year 2016 Year 2017 Year 2018 Sales (P billions)