Chapter 1 Data and Statistics.ppt

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1、Chapter 1 Data and Statistics,Applications in Business and Economics Data Data Sources Descriptive Statistics Statistical Inference,Applications in Business and Economics,AccountingPublic accounting firms use statistical sampling procedures when conducting audits for their clients. FinanceFinancial

2、analysts use a variety of statistical information, including price-earnings ratios and dividend yields, to guide their investment recommendations. MarketingElectronic point-of-sale scanners at retail checkout counters are being used to collect data for a variety of marketing research applications.,P

3、roductionA variety of statistical quality control charts are used to monitor the output of a production process. EconomicsEconomists use statistical information in making forecasts about the future of the economy or some aspect of it.,Applications in Business and Economics,Data,Elements, Variables,

4、and Observations Scales of Measurement Qualitative and Quantitative Data Cross-Sectional and Time Series Data,Data and Data Sets,Data are the facts and figures that are collected, summarized, analyzed, and interpreted. The data collected in a particular study are referred to as the data set.,Element

5、s, 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 e

6、lements multiplied by the number of variables.,Data, Data Sets, Elements, Variables, and Observations,Elements,Variables,Data Set,Datum,Observation,Stock Annual Earn/Company Exchange Sales($M) Sh.($)Dataram AMEX 73.10 0.86 EnergySouth OTC 74.00 1.67Keystone NYSE 365.70 0.86 LandCare NYSE 111.40 0.33

7、Psychemedics AMEX 17.60 0.13,Scales of Measurement,Scales of measurement include: Nominal Ordinal Interval Ratio The scale determines the amount of information contained in the data. The scale indicates the data summarization and statistical analyses that are most appropriate.,Scales of Measurement,

8、Nominal Data are labels or names used to identify an attribute of the element. A nonnumeric label or a numeric code may be used.,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, Humanitie

9、s, 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,Ordinal The data have the properties of nominal data and the order or rank of the data is meaningful. A n

10、onnumeric label or a numeric code may be used.,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.

11、g. 1 denotes Freshman, 2 denotes Sophomore, and so on).,Scales of Measurement,Interval The data have the properties of ordinal data and the interval between observations is expressed in terms of a fixed unit of measure. Interval data are always numeric.,Scales of Measurement,Interval Example:Melissa

12、 has an SAT score of 1205, while Kevin has an SAT score of 1090. Melissa scored 115 points more than Kevin.,Scales of Measurement,Ratio 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 scal

13、e. This scale must contain a zero value that indicates that nothing exists for the variable at the zero point.,Scales of Measurement,Ratio Example:Melissas college record shows 36 credit hours earned, while Kevins record shows 72 credit hours earned. Kevin has twice as many credit hours earned as Me

14、lissa.,Qualitative and Quantitative Data,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. In general, there are more alternatives for statistical analysis wh

15、en the data are quantitative.,Qualitative Data,Qualitative data are labels or names used to identify an attribute of each element. Qualitative data use either the nominal or ordinal scale of measurement. Qualitative data can be either numeric or nonnumeric. The statistical analysis for qualitative d

16、ata are rather limited.,Quantitative Data,Quantitative data indicate either how many or how much. Quantitative data that measure how many are discrete. Quantitative data that measure how much are continuous because there is no separation between the possible values for the data Quantitative data are

17、 always numeric. Ordinary arithmetic operations are meaningful only with quantitative data.,Cross-Sectional and Time Series 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 2000 in each

18、of the counties of Texas Time series data are collected over several time periods. Example: data detailing the number of building permits issued in Travis County, Texas in each of the last 36 months,Data Sources,Existing Sources Data needed for a particular application might already exist within a f

19、irm. Detailed information is often kept on customers, suppliers, and employees for example. Substantial amounts of business and economic data are available from organizations that specialize in collecting and maintaining data.,Data Sources,Existing Sources Government agencies are another important s

20、ource of data. Data are also available from a variety of industry associations and special-interest organizations.,Data Sources,Internet The Internet has become an important source of data. Most government agencies, like the Bureau of the Census (www.census.gov), make their data available through a

21、web site. More and more companies are creating web sites and providing public access to them. A number of companies now specialize in making information available over the Internet.,Statistical Studies Statistical studies can be classified as either experimental or observational. In experimental stu

22、dies 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; an example is a su

23、rvey.,Data Sources,Data Acquisition Considerations,Time Requirement Searching for information can be time consuming. Information might 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.

24、Data Errors Using any data that happens to be available or that were acquired with little care can lead to poor and misleading information.,Descriptive Statistics,Descriptive statistics are the tabular, graphical, and numerical methods used to summarize data.,Example: Hudson Auto Repair,The manager

25、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 below.,Example: Hudson Auto Repair,Tabular Summary (Frequenci

26、es and Percent Frequencies)Parts PercentCost ($) Frequency Frequency50-59 2 460-69 13 2670-79 16 3280-89 7 1490-99 7 14100-109 5 10Total 50 100,Example: Hudson Auto Repair,Graphical Summary (Histogram),Parts Cost ($),2,4,6,8,10,12,14,16,18,Frequency,50 60 70 80 90 100 110,Example: Hudson Auto Repair

27、,Numerical Descriptive Statistics The most common numerical descriptive statistic is the average (or mean). Hudsons 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).,Statistical Inference,Statistical inference is the proces

28、s of using data obtained from a small group of elements (the sample) to make estimates and test hypotheses about the characteristics of a larger group of elements (the population).,Example: Hudson Auto Repair,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 cost of $79 per tune-up.,4. The value of the sample average is used to make an estimate ofthe population average.,End of Chapter 1,