Ken Black QA ch01

8/3/2019 Ken Black QA ch01

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Business Statistics, 5 th ed. by Ken Black

Chapter 1 Introduction

to Statistics

Discrete Distributions

PowerPoint presentations prepared by Lloyd Jaisingh, Morehead State University


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Learning Objectives

Define statistics Become aware of a wide range of

applications of statistics in business Differentiate between descriptive and

inferential statistics

Classify numbers by level of data andunderstand why doing so is important


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Statistics in Business

Accounting auditing and cost estimation Economics regional, national, and international

economic performance Finance investments and portfolio management Management human resources, compensation, and

quality management Management Information Systems performance of

systems which gather, summarize, and disseminateinformation to various managerial levels

Marketing market analysis and consumer research International Business market and demographic

analysis


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What is Statistics?

Science of gathering, analyzing,interpreting, and presenting data

Branch of mathematics Course of study Facts and figures A death

Measurement taken on a sample Type of distribution being used to analyze

data


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Population Versus Sample

Population the whole a collection of persons, objects, or items under

study Census gathering data from the entire

population Sample a portion of the whole

a subset of the population


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Population


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Population and Census Data

Identifier Color MPG

RD1 Red 12RD2 Red 10

RD3 Red 13RD4 Red 10RD5 Red 13BL1 Blue 27BL2 Blue 24GR1 Green 35GR2 Green 35GY1 Gray 15GY2 Gray 18GY3 Gray 17


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Sample and Sample Data

Identifier Color MPG

RD2 Red 10

RD5 Red 13

GR1 Green 35

GY2 Gray 18


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Descriptive vs. Inferential Statistics

Descriptive Statistics using data gatheredon a group to describe or reach conclusionsabout that same group only

Inferential Statistics using sample data toreach conclusions about the populationfrom which the sample was taken


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Parameter vs. Statistic

Parameter descriptive measure of the population Usually represented by Greek letters

Statistic descriptive measure of a sample

Usually represented by Roman letters


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Symbols for Population Parameters

2

denotes p opulation variance denotes population standard deviation

denotes population mean


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Symbols for Sample Statistics

x denotes sample mean

S denotes sample standard deviation

S 2 denotes sample variance


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Process of Inferential Statistics

Population

(parameter)

Sample

x

(statistic )

Calculateto estimate

X

Select arandom sample


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Levels of Data Measurement

Nominal Lowest level of measurement Ordinal Interval Ratio Highest level of measurement


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Nominal Level Data Numbers are used to classify or categorize

Example: Employment Classification 1 for Educator

2 for Construction Worker 3 for Manufacturing Worker

Example: Ethnicity 1 for African-American

2 for Anglo-American 3 for Hispanic-American


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Ordinal Level Data

Numbers are used to indicate rank or order Relative magnitude of numbers is meaningful Differences between numbers are not comparable

Example: Ranking productivity of employeesExample: Taste test ranking of three brands of soft drink Example: Position within an organization

1 for President

2 for Vice President 3 for Plant Manager 4 for Department Supervisor 5 for Employee


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Example of Ordinal Measurement

f i

n

is

h

1

2

3

4

5

6


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Ordinal Data

Faculty and staff should receive preferentialtreatment for parking space.

1 2 3 4 5

StronglyAgree

Agree StronglyDisagree

DisagreeNeutral


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Interval Level Data

Distances between consecutive integers areequal

Relative magnitude of numbers is meaningful Differences between numbers are comparable Location of origin, zero, is arbitrary Vertical intercept of unit of measure transform

function is not zeroExample: Fahrenheit TemperatureExample: Calendar TimeExample: Monetary Utility


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Ratio Level Data Highest level of measurement

Relative magnitude of numbers is meaningful Differences between numbers are comparable

Location of origin, zero, is absolute (natural) Vertical intercept of unit of measure transformfunction is zero

Examples: Height, Weight, and VolumeExample: Monetary Variables, such as Profit and

Loss, Revenues, and ExpensesExample: Financial ratios, such as P/E Ratio,

Inventory Turnover, and Quick Ratio .


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Usage Potential of Various

Levels of Data

Nominal

Ordinal

Interval

Ratio


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Data Level, Operations,and Statistical Methods

Data Level

Nominal

Ordinal

Interval

Ratio

Meaningful Operations

Classifying and Counting

All of the above plus Ranking

All of the above plus Addition,Subtraction, Multiplication, andDivision

All of the above

StatisticalMethods

Nonparametric

Nonparametric

Parametric

Parametric


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Copyright 2008 John Wiley & Sons, Inc.All rights reserved. Reproduction or translation

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Ken Black QA ch01