BPP LEARNING MEDIABUSINESS ESSENTIALS1 Business Decision Making

BPP LEARNING MEDIA BUSINESS ESSENTIALS 1

BUSINESS ESSENTIALS

Business Decision Making


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 systems136

Stock (inventory) control 153

Project management tools and techniques 169

Investment appraisal 185

CONTENTS


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


— 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

Data


— 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

Types of data 1


— 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

Types of data 2


— 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

Types of data 3


— 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

Types of data 4


— 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

Types of data 5


Observation

Experimentation

Personal interviews

Telephone interviews

Postal questionnaires

Methods of obtaining sample data


— 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

Sources of secondary data 1


— 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

Sources of secondary data 2


— 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

Sampling 1


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

Sampling 2


— 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

Sampling 3


— 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

Sampling 4


— 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

Sampling 5


— 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

Sampling 6


— 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

Sampling 7


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’

Sampling 8


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

Interviews and questionnaires 1


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



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



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

Design of questionnaires 1


Avoid questions which are

— Long

— Ambiguous

— Leading

— Negative



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



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!

Data storage 1


Security risks

— Hackers

— Viruses

— Hoaxes

— Denial of service attack

Data storage 2


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

Data storage 3


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

Data storage 4


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

Data storage 5


Chapter 2

Data presentation

— Information in tables

— Information in charts

— Averages


— 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

Information in tables 1


— 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

Information in tables 2


— 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

Information in charts 1




— 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



— 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



— 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



Information in charts 6 – bar charts


Information in charts 7 – component bar charts


Information in charts 8 – % component bar charts


— 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)



— 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

Information in charts 10 – Histograms


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%




— 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




Averages 1

Averages

Mean Median Mode


— 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

Averages 2 – 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

Averages 3 – Arithmetic mean


— 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

Averages 4 – Mode


— 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

Averages 5 – Median


Averages 6 – Median

Ungrouped data

19 29 30 21 26 25 20

19 20 21 25 26 29 30

Put in order

Calculate middle rank

___n+12

Odd numbern2__Even number __

2n

+ 1&

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BPP LEARNING MEDIABUSINESS ESSENTIALS1 Business Decision Making