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8/3/2019 Ken Black QA ch01
1/23
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
of this work beyond that permitted in section 117of the 1976 United States Copyright Act without
express permission of the copyright owner isunlawful. Request for further information should be addressed to the Permissions Department,John Wiley & Sons, Inc. The purchaser maymake back-up copies for his/her own use onlyand not for distribution or resale. The Publisher assumes no responsibility for errors, omissions,or damages caused by the use of these programsor from the use of the information herein.