Chapter One: An Introduction to Business Statistics

1
Chapter One An Introduction to Business
Statistics

• Statistics Applications in Business and Economics
• Basic Vocabulary Terms
• Populations and Samples

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.

From Anderson, Sweeney and Williams
3

• 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.

From Anderson, Sweeney and Williams
4
Basic Vocabulary Terms

• Statistics is the art and science of collecting,
  analyzing, presenting and interpreting data
• Data are the facts and figures that are
  collected, summarized, analyzed, and interpreted.
• 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.

5
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 data are
  rather limited.

6
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 are meaningful
  only with quantitative data.

7
Quantitative and Qualitative Data

• A qualitative variable is a variable with
  qualitative data
• A quantitative variable is a variable with
  quantitative data.

8
Additional Terms

• The elements are the entities on which data are
  collected.
• The set of measurements collected for a
  particular element is called an observation.
• A variable is a characteristic of interest for
  the elements.

9
Example
Stock Annual Earn/ Company
Exchange Sales(M) Sh.() Dataram A
MEX 73.10 0.86 EnergySouth OTC 74.00
1.67 Keystone NYSE 365.70 0.86
LandCare NYSE 111.40
0.33 Psychemedics AMEX 17.60 0.13
Observation
Variables
From Anderson, Sweeney and Williams
Elements
Data Set
Datum
10
Short Exercise

• In the previous example, determine which
  variables are qualitative and which are
  quantitative.

Ans Stock exchange is qualitative. Annual Sales
and Earn/Shares is quantitative.
11
Populations and Samples

• The population is the set of all elements of
  interest in a particular study.
• A sample is a subset of the population.

12
Populations and Samples

Population
Sample
From Anderson, Sweeney and Williams
13
Descriptive Statistics and Statistical Inference

• Descriptive Statistics is tabular, graphical,
  and numerical methods used to summarize data.

14
Example Hudson Auto Repair Descriptive
Statistics
Graphical Summary (Histogram)
18
16
14
12
Frequency
10
8
From Anderson, Sweeney and Williams
6
4
2
Parts Cost ()
50 60 70 80 90 100
110
15

• 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).

From Anderson, Sweeney and Williams
16
Statistical Inference is the process of using
information obtained from analyzing a sample to
make estimates about characteristics of the
entire population.
17
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.
From Anderson, Sweeney and Williams
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.
18

• Random Sampling
• A procedure for selecting a subset of the
  population units in such a way that every unit in
  the population has an equal chance of selection.
  Since the validity of all statistical results
  depend upon the original sampling process, it is
  essential that this process is blind. This
  implies that every element in the population is
  equally likely to be selected for the sample
  without bias.

19
END OF Chapter 1