2. Statistics Ⅰ Teacher ：刘 伟 公共经济管理学院 统计系 E-mail ： limitzifeng@163.com limitzifeng@163.com Office ： 2507

3. Chapter 1 Data and Statistics I need help! Applications in Business and Economics Data Data Sources Descriptive Statistics Statistical Inference Computers and Statistical Analysis

4. Applications in Business and Economics Public accounting firms use statistical sampling procedures when conducting audits for their clients. Financial advisors use price-earnings ratios and dividend yields to guide their investment recommendations. Finance （财务） n Accounting （会计）

5. Applications in Business and Economics A variety of statistical quality control charts are used to monitor the output of a production process. n Production （生产） Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications. n Marketing （市场营销）

6. Applications in Business and Economics n n Economics （经济） Economists use statistical information in making forecasts about the future of the economy or some aspect of it.

7. Data and Data Sets Data （数据） are the facts and figures collected, summarized, analyzed, and interpreted. The data collected in a particular study are referred to as the data set. （数据集）

8. The elements are the entities on which data are collected. A variable is a characteristic of interest for the elements. The set of measurements collected for a particular element is called an observation. The total number of data values in a data set is the number of elements multiplied by the number of variables. Elements （个体）, Variables （变量）, and Observations （观察值）

9. Stock Annual Earn/ Exchange Sales ($M) Share($) Data, Data Sets, Elements, Variables, and Observations Company Dataram EnergySouth Keystone LandCare Psychemedics AMEX 73.10 0.86 OTC 74.00 1.67 NYSE365.70 0.86 NYSE111.40 0.33 AMEX 17.60 0.13 Variables Element Names Data Set Observation

10. Scales of Measurement 测量尺度 The scale indicates the data summarization and statistical analyses that are most appropriate. The scale indicates the data summarization and statistical analyses that are most appropriate. The scale determines the amount of information contained in the data. The scale determines the amount of information contained in the data. Scales of measurement include: Nominal （名义尺度） Ordinal （顺序尺度） Interval （间隔尺度） Ratio （比率尺度）

11. Scales of Measurement NominalNominal A nonnumeric label or numeric code may be used. Data are labels or names used to identify an attribute of the element. Data are labels or names used to identify an attribute of the element.

12. Example: Students of a university are classified by the school in which they are enrolled using a nonnumeric label such as Business, Humanities, Education, and so on. Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and so on). Example: Students of a university are classified by the school in which they are enrolled using a nonnumeric label such as Business, Humanities, Education, and so on. Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and so on). Scales of Measurement n Nominal

13. Scales of Measurement Ordinal A nonnumeric label or numeric code may be used. The data have the properties of nominal data and the order or rank of the data is meaningful. The data have the properties of nominal data and the order or rank of the data is meaningful.

14. Scales of Measurement Ordinal Example: Students of a university are classified by their class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on). Example: Students of a university are classified by their class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on).

15. Scales of Measurement Interval Interval data are always numeric. The data have the properties of ordinal data, and the interval between observations is expressed in terms of a fixed unit of measure. The data have the properties of ordinal data, and the interval between observations is expressed in terms of a fixed unit of measure.

16. Scales of Measurement Interval Example: Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 points more than Kevin. Example: Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 points more than Kevin.

17. Scales of Measurement Ratio The data have all the properties of interval data and the ratio of two values is meaningful. The data have all the properties of interval data and the ratio of two values is meaningful. Variables such as distance, height, weight, and time use the ratio scale. Variables such as distance, height, weight, and time use the ratio scale. This scale must contain a zero value that indicates that nothing exists for the variable at the zero point. This scale must contain a zero value that indicates that nothing exists for the variable at the zero point.

18. Scales of Measurement Ratio Example: Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned as Melissa. Example: Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned as Melissa.

19. Compare

20. Practice: Indicate measurement scale of each of the following variables Annual sales Soft-drink size (small, medium, large) Employee classification ( GS1 to GS8) Earnings per share Method of payment (cash, check, credit card ) ratio, ordinal, ordinal, ratio, nominal

21. Data can be further classified as being qualitative or quantitative. Data can be further classified as being qualitative or quantitative. The statistical analysis that is appropriate depends on whether the data for the variable are qualitative or quantitative. The statistical analysis that is appropriate depends on whether the data for the variable are qualitative or quantitative. In general, there are more alternatives for statistical analysis when the data are quantitative. In general, there are more alternatives for statistical analysis when the data are quantitative. Qualitative and Quantitative Data

22. Qualitative Data Labels or names used to identify an attribute of each element Labels or names used to identify an attribute of each element Often referred to as categorical data Use either the nominal or ordinal scale of measurement Use either the nominal or ordinal scale of measurement Can be either numeric or nonnumeric Appropriate statistical analyses are rather limited

23. Quantitative Data Quantitative data indicate how many or how much: discrete, if measuring how many continuous, if measuring how much Quantitative data are always numeric. Ordinary arithmetic operations are meaningful for quantitative data. Ordinary arithmetic operations are meaningful for quantitative data.

24. Practice: State whether each of the following variables is qualitative or quantitative? Annual sales Soft-drink size (small, medium, large) Employee classification ( GS1 to GS8) Earnings per share Method of payment (cash, check, credit card ) quantitative, qualitative, qualitative,, qualitative quantitative, qualitative, qualitative, quantitative, qualitative

25. Scales of Measurement Qualitative Quantitative Numerical Non-numerical Data Nominal Ordinal Nominal Ordinal Interval Ratio

26. Cross-Sectional Data( 截面数据 ) Cross-sectional data are collected at the same or approximately the same point in time. Cross-sectional data are collected at the same or approximately the same point in time. Example: data detailing the number of building permits issued in June 2003 in each of the counties of Ohio Table 1.1 on page 5. Example: data detailing the number of building permits issued in June 2003 in each of the counties of Ohio Table 1.1 on page 5.

27. Time Series Data Time series data are collected over several time periods. Time series data are collected over several time periods. Example: data detailing the number of building permits issued in Lucas County, Ohio in each of the last 36 months Example: data detailing the number of building permits issued in Lucas County, Ohio in each of the last 36 months

28. Practice: Page 20 : excise 13 a.Quantitative - Earnings measured in billions of dollars b. Time series with 6 observations c. Volkswagen's annual earnings. d. Time series shows an increase in earnings. An increase would be expected in 2003, but it appears that the rate of increase is slowing.

29. Data Sources Existing SourcesExisting Sources Within a firm – almost any department Business database services – Dow Jones & Co. Government agencies - U.S. Department of Labor Industry associations – Travel Industry Association of America Special-interest organizations – Graduate Management Admission Council Internet – more and more firms

31. Statistical StudiesStatistical Studies Data Sources In experimental studies the variables of interest are first identified. Then one or more factors are controlled so that data can be obtained about how the factors influence the variables. In experimental studies the variables of interest are first identified. Then one or more factors are controlled so that data can be obtained about how the factors influence the variables. In observational (non-experimental) studies no attempt is made to control or influence the variables of interest. In observational (non-experimental) studies no attempt is made to control or influence the variables of interest. a survey is a good example

32. Data Acquisition Considerations Time Requirement Cost of Acquisition Data Errors Searching for information can be time consuming. Information may no longer be useful by the time it is available. Organizations often charge for information even when it is not their primary business activity. Using any data that happens to be available or that were acquired with little care can lead to poor and misleading information.

33. Descriptive Statistics Descriptive statistics are the tabular, graphical, and numerical methods used to summarize data. –Tabular –Graphical –Numerical

34. Example: Hudson Auto Repair The manager of Hudson Auto would like to have a better understanding of the cost of parts used in the engine tune-ups performed in the shop. She examines 50 customer invoices for tune-ups. The costs of parts, rounded to the nearest dollar, are listed on the next slide.

35. Example: Hudson Auto Repair n Sample of Parts Cost for 50 Tune-ups

36. Tabular Summary: Frequency and Percent Frequency 50-59 60-69 70-79 80-89 90-99 100-109 2 13 16 7 7 5 50 4 26 32 14 14 10 100 (2/50)100 Parts Cost ($) Parts Frequency Percent Frequency

37. Graphical Summary: Histogram 22 44 66 88 1010 1212 1414 16161818 Parts Parts Cost ($) Parts Parts Cost ($) FrequencyFrequency 50  59 60  69 70  79 80  89 90  99 100-110 Tune-up Parts Cost

38. Numerical Descriptive Statistics Hudson’s average cost of parts, based on the 50 tune-ups studied, is $79 (found by summing the 50 cost values and then dividing by 50). The most common numerical descriptive statistic is the average (or mean).

39. Statistical Inference Population Sample Statistical inference Census Sample survey  the set of all elements of interest in a particular study  a subset of the population  the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population  collecting data for a population  collecting data for a sample

40. Process of Statistical Inference 1. Population consists of all tune-ups. Average cost of parts is unknown. 2. A sample of 50 engine tune-ups is examined. 3. The sample data provide a sample average parts cost of $79 per tune-up. 4. The sample average is used to estimate the population average.

41. Practice: Page 20, excise 15 a. All subscribers of Business Week in North America at the time the survey was conducted. b. Quantitative c. Qualitative ( yes or no) d. Cross-sectional - all the data relate to the same time. e. Using the sample results, we could infer or estimate 59% of the population of subscribers have an annual income of $75,000 or more and 50% of the population of subscribers have an American Express credit card.

42. Computers and Statistical Analysis Statistical analysis often involves working with large amounts of data. Computer software is typically used to conduct the analysis. Statistical software packages such as Microsoft Excel and Minitab are capable of data management, analysis, and presentation. Instructions for using Excel and Minitab are provided in chapter appendices.

43. End of Chapter 1