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Chapter One: An Introduction to Business Statistics PowerPoint PPT Presentation


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Chapter One: An Introduction to Business Statistics. Statistics Applications in Business and Economics Basic Vocabulary Terms Populations and Samples. Applications in Business and Economics. Accounting - PowerPoint PPT Presentation

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Chapter One: An Introduction to Business Statistics

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Chapter one an introduction to business statistics l.jpg

Chapter One: An Introduction to Business Statistics

  • Statistics Applications in Business and Economics

  • Basic Vocabulary Terms

  • Populations and Samples

Dr. Constance Lightner- Fayetteville State University


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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

Dr. Constance Lightner- Fayetteville State University


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  • 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

Dr. Constance Lightner- Fayetteville State University


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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.

Dr. Constance Lightner- Fayetteville State University


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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.

Dr. Constance Lightner- Fayetteville State University


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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.

Dr. Constance Lightner- Fayetteville State University


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Quantitative and Qualitative Data

A qualitative variable is a variable with qualitative data

A quantitative variable is a variable with quantitative data.


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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.

Dr. Constance Lightner- Fayetteville State University


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Example

Stock Annual Earn/

Company Exchange Sales($M) Sh.($)

DataramAMEX73.10 0.86

EnergySouth OTC74.00 1.67

Keystone NYSE 365.70 0.86

LandCare NYSE 111.40 0.33

PsychemedicsAMEX17.60 0.13

Observation

Variables

From Anderson, Sweeney and Williams

Elements

Data Set

Datum

Dr. Constance Lightner- Fayetteville State University


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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.

Dr. Constance Lightner- Fayetteville State University


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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.

Dr. Constance Lightner- Fayetteville State University


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Populations and Samples

Population

Sample

From Anderson, Sweeney and Williams

Dr. Constance Lightner- Fayetteville State University


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Descriptive Statistics and Statistical Inference

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

Dr. Constance Lightner- Fayetteville State University


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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

Dr. Constance Lightner- Fayetteville State University


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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).

From Anderson, Sweeney and Williams

Dr. Constance Lightner- Fayetteville State University


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Statistical Inference is the process of using information obtained from analyzing a sample to make estimates about characteristics of the entire population.

Dr. Constance Lightner- Fayetteville State University


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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.

Dr. Constance Lightner- Fayetteville State University


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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.

Dr. Constance Lightner- Fayetteville State University


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END OF

Chapter 1

Dr. Constance Lightner- Fayetteville State University