2. 1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University

3. 2 2 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 1 Data and Statistics I need help! n Applications in Business and Economics n Data n Data Sources n Descriptive Statistics n Statistical Inference n Computers and Statistical Analysis Statistical Analysis

4. 3 3 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 1 Data and Statistics n Statistics – numerical facts such as averages, medians, percents, and index numbers that help us understand a variety of business and economic conditions n Statistics – the art and science of collecting, analyzing, presenting, and interpreting data

5. 4 4 Slide © 2008 Thomson South-Western. All Rights Reserved Applications in Business and Economics n Accounting n Economics Public accounting firms use statistical sampling procedures when conducting audits for their clients. Economists use statistical information in making forecasts about the future of the economy or some aspect of it.

6. 5 5 Slide © 2008 Thomson South-Western. All Rights Reserved Applications in Business and Economics A variety of statistical quality control charts are used to monitor the output of a production process. n Production Electronic point-of-sale scanners at retail checkout counters are used to collect data for a variety of marketing research applications. n Marketing

7. 6 6 Slide © 2008 Thomson South-Western. All Rights Reserved Applications in Business and Economics Financial advisors use price-earnings ratios and dividend yields to guide their investment recommendations. Finance Finance

8. 7 7 Slide © 2008 Thomson South-Western. All Rights Reserved Data and Data Sets n Data are the facts and figures collected, summarized, analyzed, and interpreted. analyzed, and interpreted. The data collected in a particular study are referred The data collected in a particular study are referred to as the data set. to as the data set.

9. 8 8 Slide © 2008 Thomson South-Western. All Rights Reserved The elements are the entities on which data are The elements are the entities on which data are collected. collected. A variable is a characteristic of interest for the elements. A variable is a characteristic of interest for the elements. The set of measurements collected for a particular The set of measurements collected for a particular element is called an observation. element is called an observation. The total number of data values in a complete data The total number of data values in a complete data set is the number of elements multiplied by the set is the number of elements multiplied by the number of variables. number of variables. Elements, Variables, and Observations

10. 9 9 Slide © 2008 Thomson South-Western. All Rights Reserved Stock Annual Earn/ Stock Annual Earn/ Exchange Sales($M) Share($) Data, Data Sets, Elements, Variables, and Observations Company Dataram Dataram EnergySouth EnergySouth Keystone Keystone LandCare LandCare Psychemedics Psychemedics NQ 73.10 0.86 NQ 73.10 0.86 N 74.00 1.67 N 74.00 1.67 N365.70 0.86 N365.70 0.86 NQ111.40 0.33 NQ111.40 0.33 N 17.60 0.13 N 17.60 0.13 Variables Element Names Names Data Set Observation

11. 10 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement The scale indicates the data summarization and The scale indicates the data summarization and statistical analyses that are most appropriate. statistical analyses that are most appropriate. The scale indicates the data summarization and The scale indicates the data summarization and statistical analyses that are most appropriate. statistical analyses that are most appropriate. The scale determines the amount of information The scale determines the amount of information contained in the data. contained in the data. The scale determines the amount of information The scale determines the amount of information contained in the data. contained in the data. Scales of measurement include: Scales of measurement include: Nominal Ordinal Interval Ratio

12. 11 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement n Nominal A nonnumeric label or numeric code may be used. A nonnumeric label or numeric code may be used. Data are labels or names used to identify an Data are labels or names used to identify an attribute of the element. attribute of the element. Data are labels or names used to identify an Data are labels or names used to identify an attribute of the element. attribute of the element.

13. 12 Slide © 2008 Thomson South-Western. All Rights Reserved Example: Example: Students of a university are classified by the Students of a university are classified by the school in which they are enrolled using a school in which they are enrolled using a nonnumeric label such as Business, Humanities, nonnumeric label such as Business, Humanities, Education, and so on. Education, and so on. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and 2 denotes Humanities, 3 denotes Education, and so on). so on). Example: Example: Students of a university are classified by the Students of a university are classified by the school in which they are enrolled using a school in which they are enrolled using a nonnumeric label such as Business, Humanities, nonnumeric label such as Business, Humanities, Education, and so on. Education, and so on. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the school variable (e.g. 1 denotes Business, the school variable (e.g. 1 denotes Business, 2 denotes Humanities, 3 denotes Education, and 2 denotes Humanities, 3 denotes Education, and so on). so on). Scales of Measurement n Nominal

14. 13 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement n Ordinal A nonnumeric label or numeric code may be used. A nonnumeric label or numeric code may be used. The data have the properties of nominal data and The data have the properties of nominal data and the order or rank of the data is meaningful. the order or rank of the data is meaningful. The data have the properties of nominal data and The data have the properties of nominal data and the order or rank of the data is meaningful. the order or rank of the data is meaningful.

15. 14 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement n Ordinal Example: Example: Students of a university are classified by their Students of a university are classified by their class standing using a nonnumeric label such as class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on). Freshman, 2 denotes Sophomore, and so on). Example: Example: Students of a university are classified by their Students of a university are classified by their class standing using a nonnumeric label such as class standing using a nonnumeric label such as Freshman, Sophomore, Junior, or Senior. Freshman, Sophomore, Junior, or Senior. Alternatively, a numeric code could be used for Alternatively, a numeric code could be used for the class standing variable (e.g. 1 denotes the class standing variable (e.g. 1 denotes Freshman, 2 denotes Sophomore, and so on). Freshman, 2 denotes Sophomore, and so on).

16. 15 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement n Interval Interval data are always numeric. Interval data are always numeric. The data have the properties of ordinal data, and The data have the properties of ordinal data, and the interval between observations is expressed in the interval between observations is expressed in terms of a fixed unit of measure. terms of a fixed unit of measure. The data have the properties of ordinal data, and The data have the properties of ordinal data, and the interval between observations is expressed in the interval between observations is expressed in terms of a fixed unit of measure. terms of a fixed unit of measure.

17. 16 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement n Interval Example: Example: Melissa has an SAT score of 1205, while Kevin Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 has an SAT score of 1090. Melissa scored 115 points more than Kevin. points more than Kevin. Example: Example: Melissa has an SAT score of 1205, while Kevin Melissa has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 has an SAT score of 1090. Melissa scored 115 points more than Kevin. points more than Kevin.

18. 17 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement n Ratio The data have all the properties of interval data The data have all the properties of interval data and the ratio of two values is meaningful. and the ratio of two values is meaningful. The data have all the properties of interval data The data have all the properties of interval data and the ratio of two values is meaningful. and the ratio of two values is meaningful. Variables such as distance, height, weight, and time Variables such as distance, height, weight, and time use the ratio scale. use the ratio scale. Variables such as distance, height, weight, and time Variables such as distance, height, weight, and time use the ratio scale. use the ratio scale. This scale must contain a zero value that indicates This scale must contain a zero value that indicates that nothing exists for the variable at the zero point. that nothing exists for the variable at the zero point. This scale must contain a zero value that indicates This scale must contain a zero value that indicates that nothing exists for the variable at the zero point. that nothing exists for the variable at the zero point.

19. 18 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement n Ratio Example: Example: Melissa’s college record shows 36 credit hours Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned. Kevin has twice as many credit hours earned as Melissa. hours earned as Melissa. Example: Example: Melissa’s college record shows 36 credit hours Melissa’s college record shows 36 credit hours earned, while Kevin’s record shows 72 credit earned, while Kevin’s record shows 72 credit hours earned. Kevin has twice as many credit hours earned. Kevin has twice as many credit hours earned as Melissa. hours earned as Melissa.

20. 19 Slide © 2008 Thomson South-Western. All Rights Reserved Data can be further classified as being qualitative Data can be further classified as being qualitative or quantitative. or quantitative. Data can be further classified as being qualitative Data can be further classified as being qualitative or quantitative. or quantitative. The statistical analysis that is appropriate depends The statistical analysis that is appropriate depends on whether the data for the variable are qualitative on whether the data for the variable are qualitative or quantitative. or quantitative. The statistical analysis that is appropriate depends The statistical analysis that is appropriate depends on whether the data for the variable are qualitative on whether the data for the variable are qualitative or quantitative. or quantitative. In general, there are more alternatives for statistical In general, there are more alternatives for statistical analysis when the data are quantitative. analysis when the data are quantitative. In general, there are more alternatives for statistical In general, there are more alternatives for statistical analysis when the data are quantitative. analysis when the data are quantitative. Qualitative and Quantitative Data

21. 20 Slide © 2008 Thomson South-Western. All Rights Reserved Qualitative Data Labels or names used to identify an attribute of each Labels or names used to identify an attribute of each element element Labels or names used to identify an attribute of each Labels or names used to identify an attribute of each element element Often referred to as categorical data Often referred to as categorical data Use either the nominal or ordinal scale of Use either the nominal or ordinal scale of measurement measurement Use either the nominal or ordinal scale of Use either the nominal or ordinal scale of measurement measurement Can be either numeric or nonnumeric Can be either numeric or nonnumeric Appropriate statistical analyses are rather limited Appropriate statistical analyses are rather limited

22. 21 Slide © 2008 Thomson South-Western. All Rights Reserved Quantitative Data Quantitative data indicate how many or how much: Quantitative data indicate how many or how much: discrete, if measuring how many discrete, if measuring how many continuous, if measuring how much continuous, if measuring how much Quantitative data are always numeric. Quantitative data are always numeric. Ordinary arithmetic operations are meaningful for Ordinary arithmetic operations are meaningful for quantitative data. quantitative data. Ordinary arithmetic operations are meaningful for Ordinary arithmetic operations are meaningful for quantitative data. quantitative data.

23. 22 Slide © 2008 Thomson South-Western. All Rights Reserved Scales of Measurement QualitativeQualitativeQuantitativeQuantitative NumericalNumerical NumericalNumerical Non-numericalNon-numerical DataData NominalNominalOrdinalOrdinalNominalNominalOrdinalOrdinalIntervalIntervalRatioRatio

24. 23 Slide © 2008 Thomson South-Western. All Rights Reserved Cross-Sectional Data Cross-sectional data are collected at the same or Cross-sectional data are collected at the same or approximately the same point in time. approximately the same point in time. Cross-sectional data are collected at the same or Cross-sectional data are collected at the same or approximately the same point in time. approximately the same point in time. Example: data detailing the number of building Example: data detailing the number of building permits issued in June 2007 in each of the counties permits issued in June 2007 in each of the counties of Ohio of Ohio Example: data detailing the number of building Example: data detailing the number of building permits issued in June 2007 in each of the counties permits issued in June 2007 in each of the counties of Ohio of Ohio

25. 24 Slide © 2008 Thomson South-Western. All Rights Reserved Time Series Data Time series data are collected over several time Time series data are collected over several time periods. periods. Time series data are collected over several time Time series data are collected over several time periods. periods. Example: data detailing the number of building Example: data detailing the number of building permits issued in Lucas County, Ohio in each of permits issued in Lucas County, Ohio in each of the last 36 months the last 36 months Example: data detailing the number of building Example: data detailing the number of building permits issued in Lucas County, Ohio in each of permits issued in Lucas County, Ohio in each of the last 36 months the last 36 months

26. 25 Slide © 2008 Thomson South-Western. All Rights Reserved Data Sources n 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 of America Special-interest organizations – Graduate Management Admission Council Admission Council Internet – more and more firms

27. 26 Slide © 2008 Thomson South-Western. All Rights Reserved n Statistical Studies Data Sources In experimental studies the variable of interest is first identified. Then one or more other variables are identified and controlled so that data can be obtained about how they influence the variable of interest. In experimental studies the variable of interest is first identified. Then one or more other variables are identified and controlled so that data can be obtained about how they influence the variable of interest. In observational (nonexperimental) studies no In observational (nonexperimental) studies no attempt is made to control or influence the attempt is made to control or influence the variables of interest. variables of interest. In observational (nonexperimental) studies no In observational (nonexperimental) studies no attempt is made to control or influence the attempt is made to control or influence the variables of interest. variables of interest. a survey is a good example

28. 27 Slide © 2008 Thomson South-Western. All Rights Reserved Data Acquisition Considerations Time Requirement Cost of Acquisition Data Errors Data Errors Searching for information can be time consuming. Searching for information can be time consuming. Information may no longer be useful by the time it Information may no longer be useful by the time it is available. is available. Organizations often charge for information even Organizations often charge for information even when it is not their primary business activity. when it is not their primary business activity. Using any data that happen to be available or were Using any data that happen to be available or were acquired with little care can lead to misleading acquired with little care can lead to misleading information. information.

29. 28 Slide © 2008 Thomson South-Western. All Rights Reserved Descriptive Statistics n Descriptive statistics are the tabular, graphical, and numerical methods used to summarize and present data.

30. 29 Slide © 2008 Thomson South-Western. All Rights Reserved 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.

31. 30 Slide © 2008 Thomson South-Western. All Rights Reserved Example: Hudson Auto Repair Example: Hudson Auto Repair n Sample of Parts Cost ($) for 50 Tune-ups

32. 31 Slide © 2008 Thomson South-Western. All Rights Reserved Tabular Summary: Frequency and Percent Frequency Tabular Summary: Frequency and Percent Frequency 50-59 50-59 60-69 60-69 70-79 70-79 80-89 80-89 90-99 90-99 100-109 100-109 2 13 16 7 7 5 50 4 26 32 14 14 10 100 (2/50)100(2/50)100 Parts Cost ($) Cost ($) Parts Frequency Frequency PercentFrequency

33. 32 Slide © 2008 Thomson South-Western. All Rights Reserved Graphical Summary: Histogram Graphical Summary: Histogram 2 2 4 4 6 6 8 8 10 12 14 16 18 Parts Cost ($) Parts Cost ($) Frequency 50  59 60  69 70  79 80  89 90  99 100-110 Tune-up Parts Cost

34. 33 Slide © 2008 Thomson South-Western. All Rights Reserved Numerical Descriptive Statistics Numerical Descriptive Statistics Hudson’s average cost of parts, based on the 50 Hudson’s average cost of parts, based on the 50 tune-ups studied, is $79 (found by summing the tune-ups studied, is $79 (found by summing the 50 cost values and then dividing by 50). 50 cost values and then dividing by 50). The most common numerical descriptive statistic The most common numerical descriptive statistic is the average (or mean). is the average (or mean).

35. 34 Slide © 2008 Thomson South-Western. All Rights Reserved Statistical Inference PopulationPopulation SampleSample Statistical inference CensusCensus Sample survey  the set of all elements of interest in a particular study particular study  a subset of the population  the process of using data obtained from a sample to make estimates from a sample to make estimates and test hypotheses about the and test hypotheses about the characteristics of a population characteristics of a population  collecting data for a population  collecting data for a sample

36. 35 Slide © 2008 Thomson South-Western. All Rights Reserved Process of Statistical Inference Process of Statistical Inference 1. Population 1. Population consists of all tune- ups. Average cost of parts is unknown parts is unknown. 1. Population 1. Population consists of all tune- ups. Average cost of parts is unknown parts is unknown. 2. A sample of 50 2. A sample of 50 engine tune-ups is examined. 2. A sample of 50 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. 3.The sample data provide a sample average parts cost of $79 per tune-up. 4. The sample average 4. The sample average is used to estimate the population average. 4. The sample average 4. The sample average is used to estimate the population average.

37. 36 Slide © 2008 Thomson South-Western. All Rights Reserved Computers and Statistical Analysis Statistical analysis typically involves working with Statistical analysis typically involves working with large amounts of data. large amounts of data. Computer software is typically used to conduct the Computer software is typically used to conduct the analysis. analysis. Instructions are provided in chapter appendices for Instructions are provided in chapter appendices for carrying out many of the statistical procedures carrying out many of the statistical procedures using Minitab and Excel. using Minitab and Excel.

38. 37 Slide © 2008 Thomson South-Western. All Rights Reserved End of Chapter 1