Slides Prepared by JueiChao Chen Fu Jen Catholic

Chapter 1 STATISTICS in PRACTICE BUSINESS WEEK • Most issues of Business Week provide an in-depth report on a topic of current interest. Often, the in-depth reports contain statistical facts and summaries that help the reader understand the business and economic information. • Business Week also uses statistics and statistical information in managing its own business. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 2

Chapter 1 Data and Statistics 1. 1 1. 2 1. 3 1. 4 1. 5 1. 6 Applications in Business and Economics Data Sources Descriptive Statistics Statistical Inference Computers and Statistical Analysis © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 3

1. 1 Applications in Business and Economics • • • Accounting Finance Marketing Production

Applications in Business and Economics • Accounting Public accounting firms use statistical sampling procedures when conducting audits for their clients. • For instance, the audit staff selects a subset of the accounts called a sample. After reviewing the accuracy of the sampled accounts, the auditors draw a conclusion as to whether the accounts receivable amount shown on the client’s balance sheet is acceptable. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 5

Applications in Business and Economics • Economics Economists use statistical information in making forecasts about the future of the economy or some aspect of it. • For instance, the analysts review a variety of financial data including price/earnings ratios and dividend yields. By comparing the information for an individual stock with information about the stock market averages, a financial analyst can begin to draw a conclusion as to whether an individual stock is over- or under priced. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 6

Applications in Business and Economics • Marketing Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications. • For example, Brand managers can review the Scanner statistics and the promotional activity statistics to gain a better understanding of the Relationship between promotional activities and sales. Such analyses often prove helpful in establishing future marketing strategies for the various products. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 7

Applications in Business and Economics • Production A variety of statistical quality control charts are used to monitor the output of a production process. • For example, that a machine fills containers with 12 ounces of a soft drink. Periodically, a production worker selects a sample of containers and computes the average number of ounces in the sample. Properly interpret the average can help determine when adjustments are necessary to correct a production process. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 8

Applications in Business and Economics • Finance Financial advisors use price-earnings ratios and dividend yields to guide their investment recommendations. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 9

1. 2 Data • • • Data and Data Sets Elements, Variables, and Observations Scales of Measurement Qualitative and Quantitative Data Cross-Sectional and Time Series Data © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 10

1. 2 Data and Data Sets • Data are the facts and figures collected, summarized, analyzed, and interpreted. • The data collected in a particular study are referred to as the data set. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 11

Elements, Variables, and Observations • 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. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 12

Data, Data Sets, Elements, Variables, and Observations • • • the data set contains 8 elements. five variables: Exchange, Ticker Symbol, Market Cap, Price/Earnings Ratio, Gross Profit Margin. observations: the first observation (De. Wolfe Companies) is AMEX, DWL, 36. 4, 8. 4, and 36. 7. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 13

Data, Data Sets, Elements, Variables, and Observations Variables Observation Element Names Company Dataram Energy. South Keystone Land. Care Psychemedics Stock Annual Earn/ Exchange Sales($M) Share($) AMEX OTC NYSE AMEX 73. 10 74. 00 365. 70 111. 40 17. 60 0. 86 1. 67 0. 86 0. 33 0. 13 Data Set © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 14

Scales of Measurement • Nominal scale When the data for a variable consist of labels or names used to identify an attribute of the element. For example, gender, ID number, “exchange variable” in Table 1. 1 nominal data can be recorded using a numeric code. We could use “ 0” for female, and “ 1” for male. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 15

Scales of Measurement • Nominal 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). © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 16

Scales of Measurement • Ordinal scale if the data exhibit the properties of nominal data and the order or rank of the data is meaningful. For example, questionnaire: a repair service rating of excellent, good, or poor. Ordinal data can be recorded using a numeric code. We could use 1 for excellent, 2 for good, and 3 for poor. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 17

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). © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 18

Scales of Measurement • Interval the data show the properties of ordinal data and the interval between values is expressed in terms of a fixed unit of measure. Example: SAT scores, temperature. Interval data are always numeric. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 19

Scales of Measurement • Interval Example: three students with SAT scores of 1120, 1050, and 970 can be ranked or ordered in terms of best performance to poorest performance. In addition, the differences between the scores are meaningful. For instance, student 1 scored 1120 – 1050 =70 points more than student 2, while student 2 scored 1050 – 970 = 80 points more than student 3. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 20

Scales of Measurement • Ratio the data have all the properties of interval data and the ratio of two values is meaningful. • Ratio scale requires that a zero value be included to indicate that nothing exists for the variable at the zero point. • For example, distance, height, weight, and time use the ratio scale of measurement. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 21

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. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 22

Qualitative and Quantitative Data • Data can be further classified as either qualitative or quantitative. • The statistical analysis appropriate for a particular variable depends upon whether the variable is qualitative or quantitative. • If the variable is qualitative, the statistical analysis is rather limited. • In general, there are more alternatives for statistical analysis when the data are quantitative. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 23

Qualitative Data • Labels or names used to identify an attribute of each element • Qualitative data are often referred to as categorical data • Use either the nominal or ordinal scale of measurement • Can be either numeric or nonnumeric • Appropriate statistical analyses are rather limited © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 24

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. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 25

Scales of Measurement Data Qualitative Numerical Nominal Ordinal Quantitative Nonnumerical Nominal Ordinal © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Numerical Interval Ratio Slide 26

Cross-Sectional Data 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 © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 27

Time Series Data 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 © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 28

Data Sources • Existing 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 © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 29

1. 3 Data Sources • Existing Sources • Statistical Studies • Data Acquisition Errors

© 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide

Data Sources • Statistical Studies 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 (nonexperimental) studies no attempt is made to control or influence the variables of interest. a survey is a good example © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 32

Data Acquisition Considerations Time Requirement • • Searching for information can be time consuming. Information may no longer be useful by the time it is available. Cost of Acquisition • Organizations often charge for information even when it is not their primary business activity. Data Errors • Using any data that happens to be available or that were acquired with little care can lead to poor and misleading information. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 33

1. 4 Descriptive Statistics • Descriptive statistics are the tabular, graphical, and numerical methods used to summarize data. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 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. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 35

Example: Hudson Auto Repair • Sample of Parts Cost for 50 Tune-ups © 2006

Tabular Summary: Frequency and Percent Frequency Parts Cost ($) 50 -59 60 -69 70 -79 80 -89 90 -99 100 -109 Parts Frequency 2 13 16 7 7 5 50 Percent Frequency 4 26 (2/50)100 32 14 14 10 100 © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 37

Graphical Summary: Histogram Tune-up Parts Cost 18 16 Frequency 14 12 10 8 6 4 2 Parts 50 -59 60 -69 70 -79 80 -89 90 -99 100 -110 Cost ($) © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 38

Numerical Descriptive Statistics • The most common numerical descriptive statistic is the average (or mean). • 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). © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 39

1. 5 Statistical Inference Population - the set of all elements of interest in a particular study Sample - a subset of the population Statistical inference - the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population Census - collecting data for a population Sample survey - collecting data for a sample © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 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. 4. The sample average is used to estimate the population average. 3. The sample data provide a sample average parts cost of $79 per tune-up. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 41

1. 6 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. © 2006 by Thomson Learning, a division of Thomson Asia Pte Ltd. . Slide 42