Introduction to statistics for businss students.pptx

Introduction to statistics for business student

1. Introduction to Statistics Zewdu Teferi [email protected] GBG Training Center

2. Introduction Some scholars pinpoint the origin of statistics to 1662, with the publication of " Observatons on the Bills of Mortality" by John Graunt . Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general.

3. What Is Statistics ( singular ) A mathematical science concerned with data collection, presentation, analysis, and interpretation. ( collective noun ) A systematic collection of data on measurements or observations, often related to demographic information such as population counts, incomes, population counts at different ages, etc. Some local governments have (or had) a Department of Vital Statistics.

4. Definition (cont.) Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data . It is applicable to a wide variety of academic disciplines , from the natural and social sciences to the humanities , and to government and business. http://en.wikipedia.org

5. Definition (cont.) Statistics is a collection of methods for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions. http://people.richland.edu/james/lecture/m113 A branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data. Merriam-Webster

6. Definition (cont.) Statistics : The study of the collection, organization, analysis, summarization and interpretation of data, and the drawing of inferences about a body of data when only part of the data is observed.

7. Discussion What do you understand from these definitions What key words do you observe in the above definitions

8. Predicting the outcome Effective and informed decision making Essential to paint an objective picture of a country’s economic and social condition. Allow comparisons with other countries and are the key to effective policy-making. Official statistics are essential in indicating those people and regions in greatest need, and best use of scarce resources in improving health, housing, education, etc Why is statistics useful?

9. Data A dictionary defines data as facts or figures from which conclusions may be drawn. Thus, technically, it is a collective, or plural noun. Some recent dictionaries acknowledge popular usage of the word data with a singular verb. Datum is the singular form of the noun data. Data can be classified as either numeric or nonnumeric. The raw material for information

10. Data ... data is raw material for information. It simply exists and has no significance beyond its existence. Information ... information is data that has been given meaning by way of relational connection. Knowledge ... knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process .

11. Wisdom ... wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and goes far beyond understanding itself. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad.

12. Data : symbols Information : data that are processed to be useful; provides answers to "who", "what", "where", "when", etc questions Knowledge : application of data and information; answers "how" questions Understanding : appreciation of "why" Wisdom : evaluated understanding.

13. Attributes of Information Quality

14. Time Dimension Availability/ Timeliness Information should be provided when it is needed Currency Information should be up-to-date when it is provided Frequency Information should be provided as often as needed Time Period Information can be provided about past, present, and future time periods.

15. Content Dimension Accuracy Information should be free from errors Relevance Information should be related to the information needs of a specific recipient for a specific situation Completeness All the information that is needed should be provided Conciseness Only the information that is needed should be provided Scope Information can have a broad or narrow scope, or an internal or external focus Performance Information can reveal performance by measuring activities accomplished, progress made, or resources accumulated.

16. Form Dimension: Clarity Information should be provided in a form that is easy to understand Detail Information can be provided in detail or summary form Order Information can be arranged in a predetermined sequence Presentation Information can be presented in narrative, numeric, graphic, or other forms. Media Information can be provided in the form of printed paper documents, video displays, or other media.

17. Major Roles of Information Support Strategies for Competitive Advantage Support Business Decision Making Support Business Processes and Operations

18. Variable A variable is a characteristic of the data that we want to study (e.g. age, height, eye color, gender, income, account balance, etc.). Types of Variables There are two types of variables: qualitative variables and quantitative variables discrete or continuous

19. What is a Variable? Contain data which is expected to “vary” Contain data or information that you want to use to explain or describe something; Can be manipulated in various ways to extract and use the data they contain; Also referred to as “fields” in many data base applications. Variables Age Sex Education Height Weight

20. Types of Data/Variables .

21. Categorical Variables Binary (also called dichotomous) Variables that classify data into two groups or two levels. Examples Dead/alive True/False Exposed/Unexposed Pass/Fail Male/female Yes/No

22. Categorical Variables Nominal (also called categorical) Variables that classify data by groups or categories that can be “named” and that are mutually exclusive. The order does not matter. Examples: Marital status Occupation

23. Categorical Variables Ordinal Distinct categories having a predetermined rank or order. Order matters. Examples: Birth order - 1st, 2nd, 3 rd Severity of malnutrition - mild, moderate, severe Age categories in years - 6-12, 13-18, 19-24 Letter grades -- A, B, C, D, F

24. Quantitative Variables Discrete Numbers A limited set of distinct values, such as whole numbers. Examples: trainees attend SPSS training Years of school completed The number of children in the family (cannot have a half of a child!) The number of deaths in 10 years time (cannot have a partial death!)

25. Quantitative Variables Continuous Can take on any number within a defined range. Examples: Age Height Income Area cultivated Distance

26. Choosing the Right Variable Type Knowledge of respondents Sensitivity of question (e.g., income) Speed and accuracy Qualitative responses Note: Continuous variable offers more options for analysis

27. Research Design We can not study the whole population: all farmers, all fishes, all cattle or all people. We will take a sample from the population Sampling technique Inclusion/exclusion criteria Sample size Study design Method of data collection

28. Analysis There are dozens of different methods of analysis, which makes difficult the choice of the correct method for a particular case Before worrying about particular method, it is necessary to consider the philosophy that underlies all methods of analysis: Use data from a sample to draw inference about a wider population

29. Interpretation Interpretation of results of statistical analysis is not always straightforward, but is simpler when the study has a clearer aim and when there is an appreciation of the general principles that underlie the analysis If the study has been well designed and correctly analyzed the interpretation of results can be fairly simple

30. Types of Statistics 1. Descriptive statistics : Ways of organizing &summarizing dat Methods for identifying the important features of a set of data and extracting useful information Example: tables, graphs, numerical summary measures Descriptive statistics generally characterizes or describes a set of data elements by graphically displaying the information or describing its central tendencies and how it is distributed.

31. Types of Statistics 2. Inferential statistics : Methods used for drawing conclusions about a population based on the information contained in a sample of observations drawn from that population Inferential statistics tries to infer information about a population by using information gathered by sampling. Statistics : The collection of methods used in planning an experiment and analyzing data in order to draw accurate conclusions

32. As the name suggests, descriptive statistics is one which describes the population. On the other end, Inferential statistics is used to make the generalization about the population based on the samples. So, there is a big difference between descriptive and inferential statistics, i.e. what you do with your data.

33. BASIS FOR COMPARISON DESCRIPTIVE STATISTICS INFERENTIAL STATISTICS Meaning Descriptive Statistics is that branch of statistics which is concerned with describing the population under study. Inferential Statistics is a type of statistics, that focuses on drawing conclusions about the population, on the basis of sample analysis and observation. What it does? Organize, analyze and present data in a meaningful way. Compares, test and predicts data.

34. BASIS FOR COMPARISON DESCRIPTIVE STATISTICS INFERENTIAL STATISTICS Form of final Result Charts, Graphs and Tables Probability Usage To describe a situation. To explain the chances of occurrence of an event. Function It explains the data, which is already known, to summarize sample. It attempts to reach the conclusion to learn about the population, that extends beyond the data available.

35. Sources of Data Primary : collected from the items or individual respondents directly by the researcher for the purpose of certain study. Secondary : which had been collected by certain people or agency, and statistically treated and the information contained in it is used for other purpose

36. Population Data The complete set of data elements is termed the population. There are several reasons why we don't work with populations. The two are: They are usually large, and it is often impossible to get data for every object we're studying. Sampling does not usually occur without cost, and the more items surveyed, the larger the cost.

37. Population vs Sample The population includes all objects of interest whereas the sample is only a portion of the population. Parameters are associated with populations and statistics with samples. Parameters are usually denoted using Greek letters (mu, sigma) while statistics are usually denoted using Roman letters (x, s).

38. Census and sampling are two methods of collecting survey data about the population that are used by many countries. Census refers to the quantitative research method, in which all the members of the population are enumerated. On the other hand, the sampling is the widely used method, in statistical testing, wherein a data set is selected from the large population, which represents the entire group.

39. BASIS FOR COMPARISON CENSUS SAMPLING Meaning A systematic method that collects and records the data about the members of the population is called Census. Sampling refers to a portion of the population selected to represent the entire group, in all its characteristics. Enumeration Complete Partial Study of Each and every unit of the population. Only a handful of units of the population. Time required It is a time consuming process. It is a fast process. Cost Expensive method Economical method

40. Results Reliable and accurate Less reliable and accurate, due to the margin of error in the data collected. Error Not present. Depends on the size of the population Appropriate for Population of heterogeneous nature. Population of homogeneous nature.

41. Population and Sample Target population : A collection of items that have something in common for which we wish to draw conclusions at a particular time. / whole group of interest/ Study (Sampled) Population : The subset of the target population that has at least some chance of being sampled/The specific population from which data are collected/ Sample : . A sample is a portion/ subset of a population selected for further analysis about which information is actually obtained . . The individuals who are actually measured and comprise the actual data.

42. Sample Study Population Population Target

43. Generalization is a two-stage procedure: we need to able to generalize from the sample to the study population and then from the study population to the target population We compute statistics, and use them to estimate parameters. The computation is the first part of the statistics course (Descriptive Statistics) and the estimation is the second part (Inferential Statistics)

44. Uses and Abuses of Statistics Most of the time, samples are used to infer something about the population. If the study was done cautiously and results were interpreted without bias , then conclusions would be accurate. However, occasionally the conclusions are inaccurate or inaccurately portrayed for the following reasons:

45. Sample is too small. Even a large sample may not represent the population. Unauthorized personnel are giving wrong information that the public will take as truth. A possibility is a company sponsoring a statistics research to prove that their company is better. Precise statistics or parameters may incorrectly convey a sense of high accuracy. Misleading or unclear percentages are often used. Statistics are often abused . Uses and Abuses of Statistics

46. How do we draw conclusions? By gathering information from each and every member of target population – Census By gathering information from a sub-set of target population – Sample

47. Sampling

48. Types of Sampling

49. Probability Sampling Every individual in the population of interest has a known, non-zero chance of being sampled Better at ensuring representativeness Simple Random Systematic Random Stratified Random Area/Cluster

50. Simple Random Sample The “Gold Standard” of sampling Ask yourself the following questions: Is there a sample frame for the entire population? Is the sample frame complete and up-to-date? Is the target population widely dispersed? If “yes to questions 1 and 2, and “no” to question 3, then consider simple random

51. Systematic Random Sample Similar to simple random, but used if : No list of population Your target population has defined and countable limits You have a map of all the households in an area You know how many people you are targeting for enrollment but don’t know who they are at the time of sampling Requires sample interval

52. Systematic Random Sample . sample

53. Cluster Sample Doesn’t require lists, but works best when you have an understanding of population distribution More economical if population is spread out Requires a design effect (1.5 – 2 times larger than simple random) Randomly select geographical areas (“clusters”)

54. . Project target villages/clusters Cluster Samples

55. Stratified Random Sample Can be applied to all of the above Used when you want to be able to ensure all groups are represented or to make comparisons between groups Run one of the above sampling strategies for each group

56. Project target districts Hawassa Nazareth Bahirdar 800 700 2400 130 130 130 population sample Stratified Samples

57. Thank You God Bless you Indeed!   “ Information is not knowledge ” Einstein “ Knowledge is of no value unless you put it into practice. ” Anton Chekov