Transcript TITLE HERE SECOND LINE HERE - Professional Education And

BUSINESS ESSENTIALS
Business Decision Making
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CONTENTS
The collection and storage of data
3
Data presentation
32
Dispersion and skewness
55
Correlation and regression
75
Time series analysis
93
Producing information with spreadsheets
118
Reporting
129
Management information systems
136
Stock (inventory) control
153
Project management tools and techniques
169
Investment appraisal
185
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Chapter 1
The collection and
storage of data
— Data
— Types of data
— Methods of obtaining sample data
— Sources of secondary data
— Sampling
— Interviews and questionnaires
— Design of questionnaires
— Data storage
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Data
— Data is a scientific term for facts, figures and measurements
— Eg number of people who pass their driving test each year
— An attribute is something an object either has or doesn’t have
— Eg an individual is either male or female
— A variable is something which can be measured
— Eg the height of a person
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Types of data 1
— Quantitative data/information is capable of being expressed in
numbers
— It may be financial in nature, for example ‘profit before tax is
$5 million’
— Qualitative data/information may not be expressed easily in
terms of numbers
— It is more likely to reflect the quality of something for example
competition was fierce
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Types of data 2
— Quantitative data is data that can be measured
— Qualitative data is data that have attributes which cannot be
measured
— Primary data are data collected especially for a specific purpose
— Secondary data have already been collected for some other
purpose but which can be used for the survey being conducted
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Types of data 3
— Discrete data are data that can only be taken on a finite or
countable number of values within a given range
— Continuous data are data which can take on any value. They are
measured rather than counted
— Sample data are data arising as a result of investigating a sample.
A sample is a selection from the population
— A population is the group of people or objects of interest to a data
collector
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Types of data 4
— Data may be obtained from an internal source or an external
source
— Internal sources of data may include accounting records, payroll
records, manufacturing details
— With external data there are both primary and secondary sources
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Types of data 5
— A primary source of data is as close as you can get to the origin of
an item of data
— Primary external data is often used to draw up the ‘standard’ within
a control system. For example customer survey data may provide
the basis for sales targets
— Primary sources could include personal interviews, focus groups,
telephone interviews, telephone questionnaires
— A secondary source provides 'second-hand' data
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Methods of obtaining sample data
Observation
Experimentation
Personal interviews
Telephone interviews
Postal questionnaires
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Sources of secondary data 1
— Secondary data are data which have already been collected
elsewhere which can be used or adapted for the purpose
— Main sources are:
— Governments
— Banks
— Newspapers
— Trade journals
— Information services
— Consultancies
— Libraries
— Internet
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Sources of secondary data 2
— The main advantage of secondary data is that they are cheaply
available
— A disadvantage of secondary data is that the user will be unaware
of limitations in the data
— Also it may not be suitable for its intended use, it may be out of
date or the geographical area covered may not be appropriate
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Sampling 1
— If all members of a population are examined, the survey is called a
census
— If it is not possible to survey the entire population, a sample is
selected
— The results from the sample are used to estimate the results of
the population
— Once a certain sample size has been reached very little accuracy is
gained by examining more items
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Sampling 2
Random sampling
— A random sample is selected so that every item in the population
has an equal chance of being included
— This will require a sampling frame
— A sampling frame is a numbered list of all items in a population.
From this list a random sample can be selected using random
number tables
— Random sampling may be expensive, can produce an
unrepresentative sample or a sampling frame may not exist
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Sampling 3
— As random sampling can be expensive or impossible then there are
various methods of quasi-random sampling
— Stratified random sampling is where the population is divided into
categories from which random samples are taken
— This method means that a representative sample is selected which
reflects the population
— Inferences can be made about each category
— However, the method requires prior knowledge of each population
item
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Sampling 4
— Systematic sampling is another method of quasi-random
sampling
— This involves selecting every nth item after a random start
— Systematic sampling is easy to use and reasonably random
— However a biased sample might be chosen if the population has a
regular pattern coinciding with the sampling interval
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Sampling 5
— Multistage sampling is where the population is divided into
subpopulations from which a small random sample is selected
— Each sub-population is then divided further and then a small
sample is again selected at random
— This process can take place as many times as necessary
— This method does not require a sampling frame of the entire
population and is relatively cheap
— However it is not truly random and there is a possibility of bias
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Sampling 6
— When a sampling frame cannot be established then non-random
sampling methods must be used
— Quota sampling involves stratifying the population and restricting
the sample to a fixed number in each stratum
— This method is cheap and administratively easy and often used by
market researchers
— Much larger samples can be studied and no sampling frame is
required
— However it could result in a certain bias
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Sampling 7
— Another form of non-random sampling is cluster sampling
— This involves selecting one definable subsection of the population
as the sample that is taken to be representative
— It is inexpensive to operate
— It is a good alternative to multistage sampling if a sampling frame
does not exist
— However there is potential for considerable bias
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Sampling 8
Errors
— Sampling error – when sample is not representative of the
population
— Response error – respondents either unwilling or unable to
respond
— Non-response error – respondents refuse to respond or are ‘not
at home’
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Interviews and questionnaires 1
Postal questionnaires
— Advantage – Relatively cheap and interviewer cannot influence
participants
— Disadvantage – High refusal rates and misunderstanding
Group administered questionnaires
— Advantage – High response rate, fewer misunderstandings than
postal
Household drop-off survey
— Advantage – done at own convenience, fewer misunderstandings
than postal
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Interviews and questionnaires 2
Quantitative research
Personal interviews
— Advantage – Higher response rate and more accurate responses
— Disadvantage – Time consuming and expensive
Telephone interviews
— Advantage – Quick, cheap and wide coverage
— Disadvantage – Bias and high refusal rates
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Interviews and questionnaires 3
Qualitative research
Depth interviews
— Advantage – explores deeper levels of thought
— Disadvantage – time consuming, expensive
Focus groups
— Advantage – less intimidating, group sparks off ideas in others,
easier to observe a group
— Disadvantage – shy respondents, timing may be difficult
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Design of questionnaires 1
Questions should
— Relate directly to survey objectives
— Be answerable by every respondent
— Be phrased so all respondents interpret them the same way
— Provide answers to what you need to know
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Design of questionnaires 2
Avoid questions which are
— Long
— Ambiguous
— Leading
— Negative
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Design of questionnaires 3
Layout
— Use good quality paper
— Keep as short as possible
— Approachable (eg lines, boxes, using space)
— Short, user friendly instructions
Finally
— Guarantee confidentiality
— Thank the respondent
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Data storage 1
Privacy
— right of the individual to control the use of information about him or
her, including information on financial status, health and lifestyle.
Ethics
— is concerned with what is right and what is wrong. To act ethically
generally means to ‘do the right and fair thing’ in the eyes of society
as a whole.
— Do not leave sensitive data on the train!
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Data storage 2
Security risks
— Hackers
— Viruses
— Hoaxes
— Denial of service attack
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Data storage 3
Security controls
— Anti-virus software – new viruses may go undetected
— Firewall – may also prevent certain files being sent
— Encryption – data unscrambled at receiver’s end
— Electronic signatures – scrambled data is electronic signature
— Authentication – confirm message sent from authorised sender
— Dial back security – dialling person back before allowing access
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Data storage 4
Data Protection Act 1998
Personal data shall
— Be processed fairly and lawfully
— Be obtained for specified purpose
— Be adequate, relevant and not excessive
— Be accurate and up-to-date
— Not be kept longer than necessary
— Be processed in accordance with rights of data subject
— Be protected against unauthorised or unlawful processing
— Not be transferred to a territory without protection
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Data storage 5
Computer Misuse Act 1990
— Makes it a criminal offence to attempt to access, use or change any
computer system to which you do not have authorised access
rights
Copyright, Designs and Patents Act 1988
— Computer software is covered by copyright legislation
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Chapter 2
Data presentation
— Information in tables
— Information in charts
— Averages
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Information in tables 1
— A table is a matrix of information in rows and columns, with rows
and columns having titles
— A table is two-dimensional, set out in rows and columns, therefore
it can only represent two variables
— The table should be given a clear title
— All rows and columns should be clearly labelled
— The units being used must be clearly identified ie £ or kg etc
— Where appropriate there should be clear subtotals
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Information in tables 2
— A total column may be presented (usually the right hand column)
— A total figure is often advisable at the bottom of each column of
figures
— Information presented should be easy to read
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Information in charts 1
— A pie chart shows relative sizes of component elements of a total
Advantage
— clearly show when one element is bigger than another
Disadvantages
— only show relative sizes of elements
— involves calculating degrees of a circle
— difficult to compare sector sizes accurately by eye
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Information in charts 2
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Information in charts 3
— A bar chart is a method of presenting information in which
quantities are shown in the form of vertical bars on a chart
— The length of the bars is proportional to quantities / amounts
— There are three types of bar charts
— Simple bar charts
— Component bar charts, including percentage component bar
charts
— Multiple (or compound) bar charts
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Information in charts 4
— A simple bar chart is one in which the height of each bar indicates
the size of the corresponding information
— By comparing the heights of bars on the chart the size of each
piece of information can be compared
— A component bar chart is a bar chart that gives a breakdown of
each total into its components
— In a percentage component bar chart each bar is the same
height representing 100%
— The heights of sections of a bar vary according to the relative
sizes of the components
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Information in charts 5
— A multiple bar chart is one in which two or more separate bars are
used to present sub divisions of information
— These are also known as compound bar charts
— Multiple bar charts do not show a grand total whereas component
bar charts do
— Multiple bar charts illustrate the comparative sizes of the
components more clearly than component bar charts
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Information in charts 6 – bar charts
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Information in charts 7 – component bar charts
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Information in charts 8 – % component bar
charts
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Information in charts 9
— A frequency distribution records the number of times each value
occurs (the frequency)
— Example: Examination results – number of candidates scoring
1%, 2%, 3% … 100%
— Grouped frequency distributions: Frequency distributions in which
values are grouped into bands or classes
— Example: Examination results – number of candidates scoring
1% - 10%, 11% - 20%, 21% - 30% … 91% - 100%
— Grouped frequency distributions may be used for either discrete
variables (such as exam results) or continuous variables (such as
heights, weights, completion times)
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Information in charts 10 – Histograms
— A histogram is a way of presenting a frequency distribution in
graphical form
— Values (class intervals) are represented by a series of bar graphs
— Frequencies for each value are represented by the height of the bar
— Histogram with equal-sized class intervals
— Example: data for examination results – there may be bars for
scores of 1% - 10%, 11% to 20% … 91% - 100%
— The frequency within each class is shown by the height of the bar
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Information in charts 11 – Unequal-sized class
intervals
— The bars in a histogram may represent unequal class intervals
— Example: data for examination results – there may be bars for
scores of 1% - 30%, 31% to 40%, 41% - 45%, 46% - 50%, 51% 60%, 61% - 80%, 81% - 100%
— The height of each bar represents frequency density
— So if the frequency of items in the range 31% to 40% is 80 and the
frequency of items in the range 40% to 45% is 240 the bar for the
range 40% to 45% will be (× 6) the height of the bar for the range
31% to 40%
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Information in charts 12
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Information in charts 13
— An ogive shows the cumulative number of items with a value less
than or equal to, or greater than or equal to, a certain amount Eg
Number of rejections
Frequency
Cumulative frequency
>0≤1
5
5
>1≤2
5
10
>2≤3
3
13
>3≤4
1
14
14
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Information in charts 14
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Averages 1
Averages
Mean
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Mode
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Averages 2 – Arithmetic mean
— Mean = [Sum of value of items]/[Number of items]
= x/n
— Mean of a frequency distribution = [Sum of (value of items
multiplied by frequency)]/[Number of items]
= fx/n = fx/f
— For a grouped frequency distribution, the value of x for each class
interval is taken as the mid-point value
— For example, if a frequency distribution includes values in the
range 6 to 10,
x=8
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Averages 3 – Arithmetic mean
— Easily understood
— Easy to calculate
— It represents an average value of all items in the data set
— It is often used with measures of variability to provide statistical
analysis
BUT
— The mean of a sample or population may not correspond with an
actual value: for example the average number of children in a
family may be 2.8
— The mean may be distorted by extreme high or low values: for
example the mean of 1, 2, 3 and 100 is 26.5
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Averages 4 – Mode
— Mode = the most frequently-occurring value in a sample or
population
— As a measure of central tendency, it does not take into
consideration all the values in the sample or population
— It may therefore be unrepresentative of the sample or population
— It has limited use in statistical analysis
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Averages 5 – Median
— Median = The value of the middle item in an array
— Array = Value of each item in a sample or population, set out in
order of size (usually in order of increasing size)
— In a sample of n numbers, where n is an odd number, the median is
the value of item (n + 1)/2 in the array
— In a sample of n numbers, where n is an even number, the median
is the arithmetic mean of the values items (n/2) and (n + 2)/2 in the
array
— Median may be used as a measure of changes in ‘typical’ values
over time
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Averages 6 – Median
Ungrouped data
19 29 30 21 26 25 20
Put in order
19 20 21 25 26 29 30
Calculate middle rank
n
n
__
__
n+1
Odd number ___
Even number
+1
&
2
2
2
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