1. Chapter 1 Data and Statistics
Applications in Business and Economics
Data
Data Sources
Descriptive Statistics
Statistical Inference

2. Applications in Business and Economics
Accounting
Public accounting firms use statistical sampling procedures when conducting audits for their clients.
Finance
Financial analysts use a variety of statistical information, including price-earnings ratios and dividend yields, to guide their investment recommendations.
Marketing
Electronic point-of-sale scanners at retail checkout counters are being used to collect data for a variety of marketing research applications.

3. Applications in Business and Economics
Production
A variety of statistical quality control charts are used to monitor the output of a production process.
Economics
Economists use statistical information in making forecasts about the future of the economy or some aspect of it.

4. Data Elements, Variables, and Observations Scales of Measurement
Qualitative and Quantitative Data
Cross-Sectional and Time Series Data

5. Data and Data Sets
Data are the facts and figures that are collected, summarized, analyzed, and interpreted. E.g.,
IBM’s sales revenue is $100 bn.; stock price $80.
The data collected in a particular study are referred to as the data set. E.g.,
The sales revenue and stock price data for a number of firms including IBM, Dell, Apple, etc.

6. Elements, Variables, and Observations
The elements are the entities on which data are collected. E.g.,
IBM, Dell, Apple, etc. in the previous setting.
A variable is a characteristic of interest for the elements. E.g.,
Sales revenue, stock price (of a company)
The set of measurements collected for a particular element is called an observation.
Sales revenue, stock price for 2003
The total number of data values in a data set is the number of elements multiplied by the number of variables.

7. Data, Data Sets, Elements, Variables, and Observations
Stock Annual Earn/
Company Exchange Sales($M) Sh.($)
Dataram AMEX
EnergySouth OTC
Keystone NYSE
LandCare NYSE
Psychemedics AMEX
Observation
Variables
Elements
Data Set
Datum

8. 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.

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

10. 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).

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

12. 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).

13. 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.

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

15. 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 scale.
This scale must contain a zero value that indicates that nothing exists for the variable at the zero point.

16. 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.

17. 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 when the data are quantitative.

18. Qualitative Data
Qualitative data are labels or names used to identify an attribute of each element.
use either the nominal or ordinal scale of measurement.
Qualitative data can be either numeric or nonnumeric.
The statistical analysis for qualitative data are rather limited.

19. 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 always numeric.
Ordinary arithmetic operations (e.g., +, -) are meaningful only with quantitative data.

20. 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 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

21. Data Sources Existing Sources
Data needed for a particular application might already exist within a firm. 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.

22. Data Sources Existing Sources
Government agencies are another important source of data.
Data are also available from a variety of industry associations and special-interest organizations.

23. Data Sources
Internet
The Internet has become an important source of data.
Most government agencies, like the Bureau of the Census ( make their data available through a 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.

24. Data Sources Statistical Studies
Statistical studies can be classified as either experimental or observational.
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; an example is a survey.

25. 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.
Data Errors
Using any data that happens to be available or that were acquired with little care can lead to poor and misleading information.

26. Descriptive Statistics
Descriptive statistics are the tabular, graphical, and numerical methods used to summarize data.

27. 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 below.

28. Example: Hudson Auto Repair
Tabular Summary (Frequencies and Percent Frequencies)
Parts Percent
Cost ($) Frequency Frequency
Total

29. Example: Hudson Auto Repair
Graphical Summary (Histogram) 18 16 14 12
Frequency 10 8 6 4 2
Parts
Cost ($)

30. Example: Hudson Auto Repair
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).

31. Statistical Inference
Statistical inference is the process 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).

32. 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.
4. The value of the
sample average is used
to make an estimate of
the population average.
3. The sample data
provide a sample
average cost of
$79 per tune-up.

33. End of Chapter 1