Title: Chapter 1 Data and Statistics
1Chapter 1 Data and Statistics
- Applications in Business and Economics
- Data
- Data Sources
- Descriptive Statistics
- Statistical Inference
2Applications in Business and Economics
- Statistics is the process of data collection,
organizing, analyzing the data, interpertation
and make decisions. - 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
- Point-of-sale scanners at retail checkout
counters are being used to collect data for a
variety of marketing research applications.
3Applications 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.
4Why Study Statistics?
- Numerical information is everywhere
- Statistical techniques are used to make decisions
that affect our daily lives - The knowledge of statistical methods will help
you to understand how decisions are made and give
you a better understanding of how they affect
you. - No matter what line of work you select, you will
find yourself faced with decisions where an
understanding of data analysis is helpful. - Some examples of the need for data collection.
- Research analysts evaluate many facets of a
particular stock before making a buy or sell
recommendation. - The marketing department Managers must make
decisions about the quality of their product or
service.
5What is Meant by Statistics?
- In the more common usage, statistics refers to
numerical information. - Examples the average starting salary of college
graduates, the number of deaths due to alcoholism
last year etc. - We often present statistical information in a
graphical form for capturing reader attention.
6Types of Statistics
- Descriptive Statistics - methods of organizing,
summarizing, and presenting data in an
informative way. - Inferential Statistics A decision, estimate,
prediction, or generalization about a population,
based on a sample.
7Population versus Sample
- A population is a collection of all possible
individuals, objects, or measurements of
interest. - A sample is a portion, or part, of the population
of interest
8Data
- Elements, Variables, and Observations
- Scales of Measurement
- Qualitative and Quantitative Data
- Cross-Sectional and Time Series Data
9Data and Data Sets
- Data are the facts and figures that are
collected, summarized, analyzed, and interpreted.
E.g., - IBMs 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. -
10Elements, 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
11Scales 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.
12Scales of Measurement
- Nominal
- data that is classified into categories and
cannot be arranged in any particular order. A
numeric code may be used. The Nominal Scales
Categorize Individuals or Groups And This Scale
Measure The Percentage Response E.G. Male-
Female, Pakistani-American - 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).
13Scales of Measurement
- Ordinal
- similar to the nominal level, with the additional
property that meaningful amounts of differences
between data values can be determined. It
categorizes and ranks the variables according to
the preferences e.g. from best to worst, first to
last, a numeric code may be used. - e.g. rank job characteristics
- Example
- Students of a university are classified by
their class standing using a nonnumeric label
such as Freshman, Junior, Senior. - Alternatively, a numeric code could be used
for the class standing variable (e.g. 1 denotes
Freshman, 2 denotes, Junior and so on).
14Scales of Measurement
- Interval
- The data have the properties of ordinal and
interval between observations is expressed in
terms of a fixed unit of measure. Preferences on
a 5/7 point scale. It also measures the magnitude
of the differences in the preferences among the
individuals. Interval data are always numeric. - Example
- strongly disagree, disagree, neither agree nor
disagree, agree, strongly agree etc.
15Scales of Measurement
- Ratio
- The data have all the properties of interval data
and the ratio of two values is meaningful. This
scale must contain a zero value that indicates
that nothing exists for the variable at the zero
point. - Example
- Variables such as distance, height, weight, and
time use the ratio scale.
16Scales of Measurement
- Ratio scales used when exact numbers are called
for e.g. how many orders do you operate? - Interval scale used for responses to various
items on 5/7 points use of stats measures as
ratio scale, a. mean, stand. deviation. - Ordinal scale for preference in use, stats
measures are median, range, rank order
correlations
- Nominal scale used for personal data
17Types of Variables
- A. Qualitative variable - the characteristic
being studied is nonnumeric. - EXAMPLES Gender, religious affiliation, type of
automobile owned, eye color are examples. - use either the nominal or ordinal scale of
measurement. - B. Quantitative variable - information is
reported numerically. - EXAMPLES balance in your account, minutes
remaining in class, or number of children in a
family.
18Quantitative 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. - Quantitative data are always numeric.
- Arithmetic operations (e.g., , -) are meaningful
only with quantitative data.
19Summary of Types of Variables
LO4
20Cross-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 - Time series data are collected over several time
periods. - Example Texas in each of the last 36 months
21Data 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. - Substantial amounts of business and economic data
are available from organizations that specialize
in collecting and maintaining data. - Government agencies are another important source
of data. - Data are also available from a variety of
industry associations and special-interest
organizations.
22Data 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 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.
23Data 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.
24Descriptive Statistics
- Descriptive statistics are the tabular,
graphical, and numerical methods used to
summarize data.
25Example 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.
26Example Hudson Auto Repair
- Tabular Summary (Frequencies and Percent
Frequencies) - Parts Percent
- Cost () Frequency Frequency
- 50-59 2 4
- 60-69 13 26
- 70-79 16 32
- 80-89 7 14
- 90-99 7 14
- 100-109 5 10
- Total 50 100
27Example Hudson Auto Repair
- Graphical Summary (Histogram)
18
16
14
12
Frequency
10
8
6
4
2
Parts Cost ()
50 60 70 80 90 100
110
28Example Hudson Auto Repair
- 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).
29Statistical 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).
30Example 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 of the population average.
31End of Chapter 1