Applied Business Statistics - PowerPoint PPT Presentation

View by Category
About This Presentation
Title:

Applied Business Statistics

Description:

Applied Business Statistics Methods and Excel-based Applications Third edition By TREVOR WEGNER – PowerPoint PPT presentation

Number of Views:410
Slides: 21
Provided by: slclawyer
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Applied Business Statistics


1
d
e
d
u
l
c
n
i
d
c
2
Applied Business Statistics Methods and
Excel-based Applications
Third edition
TREVOR WEGNER
3
Applied Business Statistics Methods and
Excel-based Applications 3e Edition - Reprint
First published 2013 Print edition first
published 2012
Juta and Company Ltd, 2013
1st Floor, Sunclare Building 21 Dreyer
Street Claremont 7708 South Africa
ISBN 978 0 7021 7774 3 (Parent) ISBN 978 0 7021
9709 3 (Web PDF)
All rights reserved. No part of this electronic
publication may be reproduced or transmitted in
any form or by any means, electronic or
mechanical, including photocopying, recording, or
any information storage or retrieval system,
without prior permission in writing from the
publisher. Subject to any applicable licensing
terms and conditions in the case of
electronically supplied publications, a person
may engage in fair dealing with a copy of this
publication for his or her personal or private
use, or his or her research or private study. See
Section 12(1)(a) of the Copyright Act 98 of 1978.
Project Manager Martie Oudkerk Editor Paul
Carter Proofreader Wendy Priilaid Typesetter
Lebone Publishing Services Cover designer
Jacques Nel
The authors and the publisher have made every
effort to obtain permission for and acknowledge
the use of copyright material. Should any
infringement of copyright have occurred, please
contact the publisher, and every effort will be
made to rectify omissions or errors, in the event
of an update or new edition.
4
Applied Business Statistics
Preface
This text is aimed at students of management who
need to have an appreciation of the role of
statistics in management decision making. The
statistical treatment of business data is
relevant in all areas of business activity and
across all management functions (i.e. marketing,
finance, human resources, operations and
logistics, accounting, information systems and
technology). Statistics provide evidence-based
information which makes them an important
decision support tool in management. This text
has two primary aims it seeks to present the
material in a non-technical manner to make it
easier for a student with basic mathematical
background to grasp the subject matter and to
develop an intuitive understanding of the
techniques by giving explanations of methods,
illustrative examples and interpretations of
solutions. Its overall purpose is to develop a
management students statistical reasoning and
statistical decision-making skills to give him
or her a competitive advantage in the
workplace. This third edition continues the theme
of using Excel as a computational tool to perform
statistical analysis, but with a more
streamlined introduction and use of Excel to
make it easier to apply. Using Excel to perform
statistical analysis in this text will allow a
student to examine more realistic business
problems with larger datasets to focus more
on the interpretation of the statistical
findings and to transfer this skill of
performing statistical analysis more easily to
the work environment. To emphasise the applied
nature and relevancy of statistical methods
in practice, each technique is illustrated
with practical examples from the South
African business environment. These worked
examples are solved manually (to explain how the
technique works) and at the end of every
chapter the way in which Excel can be used is
illustrated. Each worked example provides a
clear and valid management interpretation of the
statistical findings. Each chapter is prefaced
by a set of learning outcomes to focus the
learning process. The exercises at the end of
each chapter focus both on testing the students
understanding of key statistical concepts and on
practising problem-solving skills either manually
or by using Excel. The questions focus strongly
on the student, providing clear and valid
management interpretations of the statistical
findings.
The text is organised around the following themes
of introductory business statistics setting the
statistical scene in management (i.e. reviewing
the importance of statistical reasoning and
understanding in management practice,
introducing basic statistical concepts and
terminology, and emphasising the importance of
data quality in statistical analysis) observation
al decision making (using the tools of
exploratory data analysis) statistical
(objective) decision making (using the tools of
inferential statistics) exploring and exploiting
statistical relationships for prediction
purposes (using the tools of statistical
modelling).
v
5
Applied Business Statistics
The final chapter covers financial calculations
(interest, annuities and net present
value (NPV)) which are appropriate for many
business students, particularly those in
financial management. Finally, this text is
designed to cover the statistics syllabi of a
number of diploma courses in management at
tertiary institutions and professional
institutes. It is also suitable for a semester
course in introductory business statistics at
universities, and for delegates on general
management development programmes. The
practical, management-focused
treatment of the discipline of management.
statistics in this text makes it suitable for
all students of
Trevor Wegner September 2011
vi
6
To Shirley, Sally and Maryanne, and my parents
(Sief and Sheila)
7
Contents
Part 1 Setting the Statistical Scene
Chapter 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10
1.11
Statistics in Management .........................
............................................. Stat
istics in Management .............................
......................................... The
Language of Statistics ...........................
.........................................
Components of Statistics ........................
...............................................
Statistics and Computers ........................
...............................................
Statistical Applications in Management
...............................................
Data and Data Quality ...........................
................................................
Data Types and Measurement Scales
..................................................
.. Data Sources ................................
..................................................
....... Data Collection Methods
..................................................
..................... Data Preparation
..................................................
................................. Summary
..................................................
............................................
Exercises .......................................
..................................................
.......
2 3 5 7 8 8 9 9 13 14 17 18 19
Part 2 Exploratory Data Analysis
Chapter 2 2.1 2.2 2.3 2.4 2.5 2.6
Summarising Data Summary Tables and Graphs
................................ Introduction
..................................................
........................................
Summarising Categorical Data ...................
..........................................
Summarising Numeric Data .......................
.......................................... The
Pareto Curve .....................................
..............................................
Using Excel (2007) to Produce Summary Tables
and Charts ................ Summary
..................................................
............................................
Exercises .......................................
..................................................
....... Describing Data Numeric Descriptive
Statistics ....................................
Introduction ....................................
..................................................
.... Central Location Measures
..................................................
.................. Non-central Location Measures
..................................................
.......... Measures of Dispersion
..................................................
....................... Measure of Skewness
..................................................
.......................... The Box Plot
..................................................
........................................
Choosing Valid Descriptive Statistics Measures
..................................... Using
Excel (2007) to Compute Descriptive Statistics
........................... Summary
..................................................
............................................
Exercises .......................................
..................................................
.......
26 27 28 35 47 49 53 54 62 63 63 71 76 81 84 86 87
89 90
Chapter 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9
Part 3 The Foundation of Statistical Inference
Probability and Sampling
Chapter 4 4.1 4.2 4.3
Basic Probability Concepts .......................
............................................. Intr
oduction .........................................
.................................................
Types of Probability ...........................
..................................................
. Properties of a Probability
..................................................
..................
100 101 101 102
8
4.4 4.5 4.6 4.7 4.8 4.9
Basic Probability Concepts .......................
............................................. Calc
ulating Objective Probabilities
..................................................
...... Probability Rules .........................
..................................................
........ Probability Trees .......................
..................................................
.......... Counting Rules Permutations and
Combinations ..............................
Summary ..........................................
..................................................
.. Exercises .....................................
..................................................
......... Probability Distributions
..................................................
..................... Introduction
..................................................
........................................ Types of
Probability Distribution ........................
.................................. Discrete
Probability Distributions .......................
.................................. Binomial
Probability Distribution ........................
................................. Poisson
Probability Distribution .........................
................................... Continuous
Probability Distributions .......................
............................ Normal Probability
Distribution ....................................
....................... Standard Normal (z)
Probability Distribution .........................
.............. Using Excel (2007) to Compute
Probabilities ....................................
.... Summary .....................................
..................................................
....... Exercises ................................
..................................................
.............. Sampling and Sampling
Distributions ...................................
............... Introduction .....................
..................................................
................... Sampling and Sampling Methods
.................................................
........ The Concept of the Sampling Distribution
............................................ The
Sampling Distribution of the Sample Mean (x)
.............................. The Sampling
Distribution of the Sample Proportion (p)
...................... The Sampling Distribution
of the Difference between Two Sample Means (x1
x2) ..............................................
.......................... The Sampling
Distribution of the Difference between
Two Proportions (p1 p2) ........................
..................................................
.... Summary ....................................
..................................................
........
103 108 110 114 115 118 119 124 125 125 125 126 12
9 132 133 134 143 145 146 152 153 153 158 158 160
Chapter 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10
Chapter 6 6.1 6.2 6.3 6.4 6.5 6.6
_
_ _
162
6.7
163 165
6.8
Part 4 Making Statistical Inferences
Chapter 7 7.1 7.2 7.3 7.4
Confidence Interval Estimation ...................
.......................................... Introdu
ction ............................................
..............................................
Point Estimation ................................
..................................................
.. Confidence Interval Estimation
..................................................
........... Confidence Interval for a Single
Population Mean (µ) when the Population Standard
Deviation (s) is known ..........................
.............. The Precision of a Confidence
Interval ........................................
......... The Rationale of a Confidence Interval
.................................................
The Student t-distribution .....................
................................................
Confidence Interval for a Single Population Mean
(µ) when the Population Standard Deviation (s) is
unknown .............................
Confidence Interval for the Population
Proportion (p) ......................... Using
Excel (2007) to Compute Confidence Limits
............................... Summary
..................................................
............................................
Exercises .......................................
..................................................
.......
168 169 169 169
170 171 175 177
7.5 7.6 7.7 7.8
178 179 180 181 182
7.9 7.10 7.11
9
Chapter 8 8.1 8.2 8.3
Hypothesis Testing Single Population (Means and
Proportions) ......... Introduction
..................................................
........................................ The
Hypothesis Testing Process ......................
......................................
Hypothesis Test for a Single Population Mean (µ)
Population Standard Deviation (s) is Known
.......................................
Hypothesis Test for a Single Population Mean (µ)
.............................. Population
Standard Deviation (s) is Unknown
................................... Hypothesis
Test for a Single Population Proportion (p)
......................... The p-value Approach
to Hypothesis Testing ............................
............ Using Excel (2007) for Hypothesis
Testing ..........................................
.. Summary .....................................
..................................................
....... Exercises ...............................
..................................................
............... Hypothesis Testing Comparison
between Two Populations (Means and Proportions)
..................................................
........................................
Introduction ....................................
..................................................
.... Hypothesis Test for the Difference between
Two Means (µ1 - µ2) for Independent Samples
Assume Population Standard Deviations are Known
..................................................
.................................................
Hypothesis Test for the Difference between Two
Means (µ1 - µ2) for Independent Samples Assume
Population Standard Deviations are Unknown
..................................................
............................................
Hypothesis Test for the Difference between Two
Dependent Sample Means The Matched-Pairs t-test
(µd ) .......................................
Hypothesis Test for the Difference between Two
Proportions (p1 p2) .. The p-value in
Two-population Hypothesis Tests
.................................. Using Excel
(2007) for Two-sample Hypothesis Testing
........................ Summary
..................................................
............................................
Exercises ........................................
..................................................
...... Chi-Squared Hypothesis Tests
..................................................
............. Introduction and Rationale
..................................................
................. The Chi-Squared Test for
Independence of Association ......................
.. Hypothesis Test for Equality of Several
Proportions .............................
Chi-Squared Goodness-of-Fit Test
..................................................
........ Using Excel (2007) for Chi-Squared
Tests ............................................
. Summary ........................................
..................................................
.... Exercises ...................................
..................................................
........... Analysis of Variance Comparing Means
across Multiple Populations . Introduction and
the Concept of ANOVA .............................
................ One-factor Analysis of Variance
(One-factor ANOVA) .......................... How
ANOVA Tests for Equality of Means
..............................................
Using Excel (2007) for One-factor ANOVA
............................................
Summary ..........................................
..................................................
.. Exercises .....................................
..................................................
.........
186 187 187
195
8.4
199 203 207 210 211 213
8.5 8.6 8.7 8.8
Chapter 9
220 221
9.1 9.2
221
9.3
226
9.4
229 233 238 238 239 241 250 251 251 257 261 268 26
9 270 277 278 278 286 286 287 288
9.5 9.6 9.7 9.8
Chapter 10 10.1 10.2 10.3 10.4 10.5 10.6
Chapter 11 11.1 11.2 11.3 11.4 11.5
Part 5 Statistical Models for Forecasting and
Planning Chapter 12 Simple Linear Regression
and Correlation Analysis ........................
.....
298 299
12.1
Introduction .....................................
..................................................
...
10
12.2 12.3 12.4 12.5 12.6 12.7
Simple Linear Regression Analysis
..................................................
...... Correlation Analysis .....................
..................................................
...... The r² Coefficient .......................
..................................................
.......... Testing the Regression Model for
Significance .....................................
. Using Excel (2007) for Regression Analysis
..........................................
Summary .........................................
..................................................
... Exercises ...................................
..................................................
........... Index Numbers Measuring Business
Activity ......................................
Introduction ....................................
..................................................
.... Price Indexes ..............................
..................................................
......... Quantity Indexes ......................
..................................................
........... Problems of Index Number
Construction ....................................
......... Limitations on the Interpretation of
Index Numbers ............................
Additional Topics of Index Numbers
..................................................
... Summary .....................................
..................................................
....... Exercises ...............................
..................................................
............... Time Series Analysis A
Forecasting Tool .................................
............. Introduction ......................
..................................................
.................. The Components of a Time
Series ...........................................
.............. Decomposition of a Time Series
..................................................
.......... Trend Analysis .......................
..................................................
............. Seasonal Analysis
..................................................
................................ Uses of Time
Series Indicators ................................
............................... Using Excel
(2007) for Time Series Analysis
.........................................
Summary .........................................
..................................................
... Exercises ...................................
..................................................
........... Financial Calculations Interest,
Annuities and NPV ...........................
Introduction to Simple and Compound Interest
................................... Simple
Interest ........................................
..............................................
Compound Interest ..............................
.................................................
Nominal and Effective Rates of Interest
................................................
Introduction to Annuities ......................
...............................................
Ordinary Annuity Certain .......................
.............................................
Ordinary Annuity Due ............................
..............................................
Deferred Annuities .............................
..................................................
. Application Net Present Value Method
................................................
Using Excel (2007) for Financial Calculations
...................................... Summary
..................................................
............................................
Exercises .......................................
..................................................
.......
299 305 309 311 312 314 315 322 323 324 332 338 34
0 340 346 348 359 360 361 364 365 372 376 379 380
381 393 394 394 398 403 404 406 410 412 414 416 41
7 418 422
Chapter 13 13.1 13.2 13.3 13.4 13.5 13.6 13.7
Chapter 14 14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8
Chapter 15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8
15.9 15.10 15.11
Solutions to Exercises ...........................
..................................................
............ Appendices Appendix 1 List of
Statistical Tables ...............................
.......................................... Appendi
x 2 List of Key Formulae .....................
..................................................
...... Index ....................................
..................................................
...............................
446 453 461
11
(No Transcript)
12
PART 1
Setting the Statistical Scene
Chapter 1
Statistics in Management
13
ChAPTER 1
Statistics in Management
Outcomes This chapter describes the role of
Statistics in management decision making. It
also explains the importance of data in
statistical analysis. After studying this
chapter, you should be able to define the term
management decision support system explain the
difference between data and information explain
the basic terms and concepts of Statistics and
provide examples recognise the different symbols
used to describe statistical concepts explain
the different components of Statistics identify
some applications of statistical analysis in
business practice distinguish between
qualitative and quantitative random variables
explain and illustrate the different types of
data identify the different sources of
data discuss the advantages and disadvantages of
each form of primary data collection explain how
to prepare data for statistical analysis.
14
Chapter 1 Statistics in Management
1.1 Statistics in Management A course in
business statistics is part of every management
education programme offered today by academic
institutions, business schools and management
colleges worldwide. Why?
Management Decision Making The reason lies in
the term management decision support systems.
Decision making is central to every managers
job. Managers must decide, for example, which
advertising media are the most effective who
are the companys high-value customers which
machinery to buy whether a consignment of
goods is of acceptable quality where to locate
stores for maximum profitability and whether
females buy more of a particular product than
males.
Information To make sound business decisions, a
manager needs high-quality information.
Information must be timely, accurate, relevant,
adequate and easily accessible. However,
information to support decision making is seldom
readily available in the format, quality and
quantity required by the decision maker. More
often than not, it needs to be generated from
data.
Data What is more readily available from a
variety of sources and of varying quality and
quantity is data. Data consists of individual
values that each conveys little useful and
usable information to management. Three
examples of data are the purchase value of a
single transaction at a supermarket (e.g. R214)
the time it takes a worker to assemble a single
part (e.g. 7.35 minutes) the brand of cornflakes
that a particular consumer prefers (e.g.
Bokomo).
Statistics It is only when a large number of data
values are collected, collated, summarised,
analysed and presented in easily readable ways
that useful and usable information for
management decision making is generated. This is
the role of Statistics in management.
Statistics is therefore defined as a set of
mathematically based tools and techniques
to transform raw (unprocessed) data into a few
summary measures that represent useful and
usable information to support effective decision
making. These summary measures are used to
describe profiles (patterns) of data, test
relationships between sets of data and identify
trends in data over time.
Figure 1.1 illustrates this transformation
process from data to information.
3
15
Applied Business Statistics
Input
Process Output
Benefit
Management Data
Statistical analysis
Information decision
making
Raw values Transformation
process Statistical summary measures
Relationships, patterns, trends Management
decision support system
Figure 1.1 Statistical analysis in management
decision making
Statistical methods can be applied in any
management area where data exists (e.g.
Human Resources, Marketing, Finance and
Operations), in a decision support role.
Statistics support the decision process by
strengthening the quantifiable basis from which
a well-informed decision can be made.
Quantitative information therefore allows a
decision maker to justify a chosen course of
action more easily and with greater
confidence. Business statistics is very often
common sense translated into statistical
terminology and formulae so that these can be
replicated and applied consistently in similar
situations elsewhere. A course in Statistics for
management students serves to demonstrate this
link between the discipline and common
sense. There are further practical reasons
why managers in general should develop an
appreciation of statistical methods and
thinking. They allow a manager to recognise
situations where statistics can be applied to
enhance a decision process perform simple
statistical analyses in practice (using
Excel, for example) to extract additional
information from business data interpret,
intelligently, management reports expressed in
numerical terms critically assess the validity of
statistical findings before using them in
decision making (A good source for invalid
statistical presentations is How to Lie with
Statistics by Darrell Huff. When examining
statistical findings, also bear in mind the adage
that you get lies, damn lies and then
Statistics.) initiate research studies with an
understanding of the statistical methods involved
communicate more easily and more effectively
with statistical analysts.
An appreciation of statistical methods can result
in new insights into a decision area,
reveal opportunities to exploit, and hence
promote more informed and effective business
decision- making. This text aims to make a
manager an active participant rather than a
passive observer when interacting with
statistical findings, reports and analysts.
Understanding and using statistical methods
empowers managers with confidence and
quantitative reasoning skills that enhance their
decision-making capabilities and provide a
competitive advantage over colleagues who do not
possess them.
4
16
Chapter 1 Statistics in Management
1.2 The Language of Statistics A number of
important terms, concepts and symbols are
used extensively in Statistics. Understanding
them early in the study of Statistics will make
it easier to grasp the subject. The most
important of the terms and concepts are a random
variable and its data a sampling unit a
population and its characteristics, called
population parameters a sample and its
characteristics, called sample statistics.
A random variable is any attribute of interest on
which data is collected and analysed.
Data is the actual values (numbers) or outcomes
recorded on a random variable.
Some examples of random variables and their data
are the travel distances of delivery vehicles
(data 34 km, 13 km, 21 km) the daily occupancy
rates of hotels in Cape Town (data 45, 72,
54) the duration of machine downtime (data 14
min, 25 min, 6 min) brand of coffee preferred
(data Nescafé, Ricoffy, Frisco).
A sampling unit is the object being measured,
counted or observed with respect to the random
variable under study.
This could be a consumer, an employee, a
household, a company or a product. More than one
random variable can be defined for a given
sampling unit. For example, an employee could be
measured in terms of age, qualification and
gender.
A population is the collection of all possible
data values that exist for the random
variable under study.
For example for a study on hotel occupancy
levels (the random variable) in Cape Town only,
all hotels in Cape Town would represent the
target population to research the age, gender and
savings levels of banking clients (three random
variables being studied), the population would
be all savings account holders at all banks.
A population parameter is a measure that
describes a characteristic of a population.
A population average is a parameter, so is a
population proportion. It is called a parameter
if it uses all the population data values to
compute its value.
A sample is a subset of data values drawn from a
population. Samples are used because it is often
not possible to record every data value of the
population, mainly because of cost, time and
possibly item destruction.
5
17
Applied Business Statistics
For example a sample of 25 hotels in Cape Town
is selected to study hotel occupancy levels a
sample of 50 savings account holders from each
of four national banks is selected to study the
profile of their age, gender and savings account
balances.
A sample statistic is a measure that describes
a characteristic of a sample. The sample average
and a sample proportion are two typical sample
statistics.
For example, appropriate sample statistics
are the average hotel occupancy level for the
sample of 25 hotels surveyed the average age of
savers, the proportion of savers who are
female and the average savings account
balances of the total sample of 200 surveyed
clients.
Table 1.1 gives further illustrations of these
basic statistical terms and concepts.
Table 1.1 Examples of populations and associated
samples
Random variable
Population
Sampling unit
Sample
Size of bank overdraft Mode of daily commuter
transport to work TV programme preferences
All current accounts with Absa All commuters to
Cape Towns central business district
(CBD) All TV viewers in Gauteng
An Absa client with a current account A commuter
to Cape Towns CBD A TV viewer in Gauteng
400 randomly selected clients current
accounts 600 randomly selected commuters to
Cape Towns CBD 2 000 randomly selected TV
viewers in Gauteng
Table 1.2 lists the most commonly used
statistical terms and symbols to distinguish a
sample statistic from a population parameter for
a given statistical measure.
Table 1.2 Symbolic notation for sample and
population measures
Statistical measure
Sample statistic
Population parameter
Mean Standard deviation Variance Size Proportion
Correlation
_ x?? s s2 n p r
µ s s2 N p ?
6
18
Chapter 1 Statistics in Management
1.3 Components of Statistics Statistics
consists of three major components descriptive
statistics, inferential statistics and
statistical modelling.
Descriptive statistics condenses sample data into
a few summary descriptive measures.
When large quantities of data have been
gathered, there is a need to organise,
summarise and extract the essential
information contained within this data for
communication to management. This is the role of
descriptive statistics. These summary measures
allow a user to identify profiles, patterns,
relationships and trends within the data.
Inferential statistics generalises sample
findings to the broader population.
Descriptive statistics only describes the
behaviour of a random variable in a sample.
However, management is mainly concerned about the
behaviour and characteristics of random variables
in the population from which the sample was
drawn. They are therefore interested in
the bigger population picture. Inferential
statistics is that area of statistics that allows
managers to understand the population picture of
a random variable based on the sample evidence.
Statistical modelling builds models of
relationships between random variables.
Statistical modelling constructs equations
between variables that are related to each
other. These equations (called models) are then
used to estimate or predict values of one of
these variables based on values of related
variables. They are extremely useful in
forecasting decisions.
Figure 1.2 shows the different components of
statistics.
POPULATION Inferential statistics (to test for
genuine patterns or relationships in the
population based on sample data)
SAMPLE Descriptive statistics (to profile sample
data)
N µ s p
_
n
x
s
p
Statistical model building (for exploring
relationships)
Figure 1.2 Conceptual overview of the components
of statistics
7
19
Applied Business Statistics
The following scenario illustrates the use of
descriptive statistics and inferential statistics
in management. Management Scenario A Proposed
Flexi-hours Working Policy Study An HR manager
plans to introduce a flexi-hours working
system to improve employee productivity. She
wants to establish the level of support such a
system will enjoy amongst the 5 758 employees of
the organisation, as well as how support may
differ between male and female employees. She
randomly samples 218 employees, of whom 96 are
female and 122 are male. Each employee is asked
to complete a short questionnaire. Descriptive
statistics will summarise the attitudes of
the 218 randomly sampled employees towards
the proposed flexi-hours work system. An
illustrative sample finding could be that 64
of the sampled female employees support the
proposal, while support from the sampled male
employees is only 57. Inferential statistics
would be used to generalise the sample findings
derived from the 218 respondents to reflect the
likely views of the entire company of 5 758
employees. For example, the following two
statistical conclusions could be drawn for all
employees. There is a 95 chance that between
58 and 63 of all employees will support this
proposed flexi-hours working system. With a 1
margin of error, females are more likely than
males to support this proposal.
1.4 Statistics and Computers Today, with the
availability of user-friendly statistical
software such as Microsoft Excel, statistical
capabilities are within reach of all managers. In
addition, there are many other off-the-shelf
software packages for business use on laptops.
These include SPSS, SPlus, Minitab, NCSS,
Statgraphics, SYSTAT, EViews, UNISTAT and Stata,
to name a few. Some work as Excel add-ins. A
search of the internet will identify many other
statistical packages and list their
capabilities. All offer the techniques of
descriptive statistics, inferential analysis and
statistical modelling covered in this text.
1.5 Statistical Applications in
Management Statistical methods can be applied in
any business management area where data exists.
A few examples follow for illustrative purposes.
Finance Stock market analysts use statistical
methods to predict share price movements
financial analysts use statistical findings to
guide their investment decisions in bonds, cash,
equities, property, etc. At a company level,
statistics is used to assess the viability of
different investment projects, to project cash
flows and to analyse patterns of payment by
debtors.
Marketing Marketing research uses statistical
methods to sample and analyse a wide range of
consumer behaviour and purchasing patterns.
Market segmentation studies use statistical
techniques
8
20
Chapter 1 Statistics in Management
to identify viable market segments, and
advertising research makes use of statistics
to determine media effectiveness.
human Resources Statistics is used to analyse
human resources issues, such as training
effectiveness, patterns of absenteeism and
employee turnover, compensation planning and
manpower planning. Surveys of employee attitudes
to employment issues use similar statistical
methods to those in market research.
Operations/Logistics Production managers rely
heavily on statistical quality control methods
to monitor both product and production
processes for quality. In the area of production
planning, managers use statistical forecasts of
future demand to determine machine and labour
utilisation over the planning period.
1.6 Data and Data Quality An understanding of
the nature of data is necessary for two reasons.
It enables a user (i) to assess data quality and
(ii) to select the most appropriate statistical
method to apply to the data. Both factors affect
the validity and reliability of statistical
findings.
Data Quality Data is the raw material of
statistical analysis. If the quality of data is
poor, the quality of information derived from
statistical analysis of this data will also be
poor. Consequently, user confidence in the
statistical findings will be low. A useful
acronym to keep in mind is GIGO, which stands
for garbage in, garbage out. It is therefore
necessary to understand what influences the
quality of data needed to produce meaningful and
reliable statistical results. Data quality is
influenced by three factors the data type, the
source of data and the methods of data
collection.
Selection of Statistical Method The choice of
the most appropriate statistical method to
use depends firstly on the management problem
to be addressed and secondly on the type of
data available. Certain statistical methods are
valid for certain data types only. The incorrect
choice of statistical method for a given data
type can again produce invalid statistical
findings.
Download full version of this book here Applied
Business Statistics
9
About PowerShow.com