Statistics for Business

1. Statistics for Business Instructor: Prof. Ken Tsang Room E409-R11 Email: kentsang@uic.edu.hk

2. TA information Mr. ZHOU, Min 周敏 Room E409 Tel:3620620 minzhou@uic.edu.hk

3. Web-page for this class • Watch for announcements about this class and • download lecture notes from • http://www.uic.edu.hk/~kentsang/stat2012/stat2012.htm • Or from this page: http://www.uic.edu.hk/~kentsang/ Or from Ispace

4. Tutorials • One hour each week • Time & place to be announced later (we need your input) • More explanations • More examples • More exercises

5. How is my final grade determined? • Quizzes 20% • Mid-term exam 20% • Assignments 10% • Final Examination 50%

6. Some requirements on this Course • Assignments must be handed in before the deadline. • We will tell you your scores for the mid-term test and quizzes so that you know your progress. However, for the final examination, we cannot tell you the score before the AR release the official results.

7. UIC Score System

8. Grade Distribution Guidelines

9. General Information • Textbook Business Statistics in Practice, 5th Edition, Bowerman O’Connell Murphree, McGraw Hill International Edition(2009)

10. Statistics for the Behavioral Sciences Frederick J Gravetter and Larry B. Wallnau Wadsworth Publishing; 8 edition (December 10, 2008)

11. Chapter 1 An Overview of Statistics

12. Chapter Sumary 1.1 Populations and Samples 1.2 Ratio, Interval, Ordinal, and Nominative Scales of Measurement 1.3 An Introduction to Survey Sampling

13. Whatis statistics? Statisticsis the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to gain more knowledge, make more effective decisions.

14. What is Statistics? • Statistics is the branch of mathematical science to make effective use of numerical data relating to a population (groups of individuals or experiments). • It deals with all aspects of the collection, analysis, interpretation (or explanation) and presentation of such data, as well as the planning of the collection of data (i.e. the design of surveys and experiments).

15. Data collection and statistical analysis • Once a sample that is representative of the population is determined, data is collected for the sample members in an observational or experimental setting. • This data can then be subjected to statistical analysis, serving two related purposes: • description • inference

16. Why do I have to learn Statistics? • Social policy, medical practice, and business decision all rely on the proper use of statistics. • Misuse of statistics can produce subtle, but serious errors in description and interpretation of data, which leads to wrong decision. • Even when statistics are correctly applied, the results can be difficult to interpret for those lacking expertise. • The set of basic statistical skills (and skepticism) that people need to deal with information in their everyday lives properly is referred to as statistical literacy. ?

17. Who uses statistics? Who Uses Statistics? Statistical techniques are used in many areas: • Government • Marketing • quality control • Medical • Research (sports, education, politic, psychology…)

18. Examples in business statistics • Consumer price index (CPI) is a measure estimating the average price of consumer goods and services purchased by households (a constant basket of goods and services within the same area). • Gross domestic product (GDP) is the market value of all final goods and services made within the borders of a country in a year.

19. Recent developments • There are more and more data around us, because • It is cheap to obtain & store • Computational tools are widely available. They are cheap and effective.

20. The McKinsey Global Institute:

21. How Companies Learn Your Secrets By CHARLES DUHIGG Published: February 16, 2012

22. Basic Terminology • Measurement, data • Variables, Value • Quantitative, Qualitative • Population, Sample • Census • Descriptive Statistics • Inferential Statistics

23. Measurement The process of determining the extent, quantity, or amount of the variable of interest for a particular item of the population. • Produces data • For example, collecting the starting salaries of graduates from last year’s MBA program

24. Data • Data can be viewed as the raw material from which information is obtained, just as trees are the raw material from which paper is produced. • In fact, a good definition of data is "facts or figures from which conclusions can be drawn".

25. Variables • A variable is a characteristic that may assume set of values to which a value can be assigned. • Height, age, amount of income, province or country of birth, grades obtained at school and type of housing are all examples of variables.

26. Value The result of measurements from a variable. • The specific measurement for a particular unit in the population • For example, the starting salaries of graduates from last year’s MBA Program

27. Quantitative Values that can be expressed as quantities/numbers. (For example, “how much” or “how many.”) • Annual starting salary of college graduate • Age and weight of a person

28. Qualitative A descriptive category to which the value can belong (a descriptive attribute of a population unit) • A person’s gender • A person’s hair color

29. Population A population is the set of all the individuals of interest in a particular study. • For example, if we want to know the starting salaries of all UIC graduates then the population of interest is the totality of all UIC graduates.

30. Census The procedure of systematically acquiring and recording information (taking measurements) about all the members of a given population. • Census usually too expensive, too time consuming, and too much effort for a large population

31. Sample A sample is a set of individuals selected from a population, usually intended to represent the population in a research study. • For example: 1,000,000 Chinese college students graduated in 2010 • This is too large for a census • So, we select a sample of these graduates and study their annual starting salaries

33. Population – the object of statistical study • In applying statistics to a scientific, industrial, or societal problem, it is necessary to begin with a population to be studied. • Populations can be diverse topics such as "all persons living in a city/country" or “all past and present students of UIC".

34. Parameter & Statistic A parameter is a value, usually a numerical value, that describes a population. A parameter may be obtain from a single measurement, or it may be derived from a set of measurements from the population. A statistic is a value, usually a numerical value, that describes a sample. A statistic may be obtain from a single measurement, or it may be derived from a set of measurements from the sample.

35. Sampling error • Sampling error is the discrepancy, or amount of error, that exists between a sample statistic and the corresponding population parameter.

36. Example of Sampling error

37. Descriptive Statistics are procedures to organize, summarize, and present data in an informative way. EXAMPLE 2: According to Consumer Reports, there were 2.5 problems per one copying machines reported during 2009. EXAMPLE 1: The average test score for the students in a class, to give a descriptive sense of the typical scores.

38. Descriptive statistics • Descriptive statistics summarize/characterize the population data by describing what was observed in the sample numerically (tabular) or graphically. • Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights), while frequency and percentage are more useful in terms of describing categorical data (like race, gender…).

39. Descriptive Statistics To describe the important aspects of a set of measurements. • For example, for a set of starting salaries, we want to know: • How much to expect (mean) • What is a high versus low salary • If the population is small, could take a census and make statistical inferences • But if the population is too large, then …

40. Inferential Statistics The science that allow us to study samples and then make generalizations about the population from which they were selected (i.e. to determine [in statistical sense] the population parameters from sample statistics). • For example, use a sample of starting salaries to estimate the important aspects of the population of starting salaries.

41. Inferential statistics • Inferential statistics (or inductive statistics) uses patterns in the sample data to draw inferences about the population represented. • These inferences may take the form of: • answering yes/no questions about the data (hypothesis testing), • estimating numerical characteristics of the data (estimation), • describing associations within the data (correlation), • modeling relationships within the data (regression).

42. Examples of inferential statistics Example 2: The accounting department of a large firm will select a sample of the invoices to check for accuracy for all the invoices of the company. Example 1: In each month, 1000 families were chosen at random. An popular index of TV channel are computed base from the data obtained in these family.

43. Difference between descriptive & inferential statistics • Descriptive statistics are distinguished from inferential statistics in that descriptive statistics aim to quantitatively summarize a data set, rather than being used to support inferential statements about the population that the data are thought to represent. • Descriptive statistics- get a “feel” (characterization) for the data • Inferential statistics- draw conclusions from the data

44. Example: Descriptive & Inferential statistics

46. Data and Variables • Variables are qualitative or quantitative attributes that characterize a population/ sample. • Data (plural of "datum", which is seldom used) are typically the results of measurements of a set of variables.

47. Types of Variables For a Qualitativeor Attribute Variablethe characteristic being studied is nonnumeric.

48. Types of Variables In a Quantitative Variableinformation is reported numerically. Balance in your checking account Final score for the students in a class Number of children in a family

49. Types of Variables Quantitative variables can be classified as eitherDiscrete or Continuous. Discrete Variableconsists of separate, indivisible values. There are “gaps” between possible values of the variable. Example: the number of bedrooms in a house, or the number of hammers sold at the local hardware store (1,2,3,…,etc).

50. Types of Variables A Continuous Variable can assume any value within a specified range. There are infinite number of possible values between any 2 observed values. The pressure in a tire The weight of a pork chop The height of students in a class.