Statistics for Management: Vodafone's Competitive Advantage Report

This report provides an in-depth analysis of how statistical methods can be applied to enhance business performance, using Vodafone as a case study. It begins with an introduction to statistics, defining key characteristics and outlining various statistical methods like central tendency and correlation. The report then discusses different types of data (primary and secondary) and their sources, such as questionnaires and interviews. It differentiates between samples and populations, and highlights the value of statistical methods in achieving business objectives. Descriptive and inferential statistics are compared, and the report includes key findings, such as financial data for Apple, Microsoft, Amazon, and Netflix, analyzed to demonstrate the practical application of statistical analysis in business decision-making. The report concludes with a list of references.

Statistics for
Management

Table of content
• Introduction
• Definition of statistics
• Key characteristics of statistics
• Overview of statistical methods
• Types of data and information
• Sources to collect statistical information

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• Difference between a sample and a population
• Value of statistical methods
• Descriptive and Inferential statistics
• Difference between descriptive and inferential statistics
• Key findings
• References

Introduction
This presentation is going to elaborate the importance
of statistical management, that a company like
Vodafone can be used to achieve its business
objectives. It provides an in-depth knowledge about
various methods that respective firm can apply, to
develop its competitive advantages.

Definition of statistics
The term ‘statistics’ can be defined as a branch of science
that mainly deals with data that can be presented numerically
and collected in systematically way. In context business,
statistics can be used in planning the activities to improve
and enhance financial performance, taking decisions to
achieve competitive positions and more.

Key characteristics of statistics
Information must gather with definite purpose: Before
collecting any business information, it is essential for a
company to determine specific and well-defined purpose, so
that relevant statistical information can be collected.
Standard of accuracy should be maintained while collecting
statistical information: Statistics deals mainly with large
number of information, therefore, accuracy of same depends
highly upon nature of enquiry, which may mislead the result,
if not taken appropriately.

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• Statistics are estimated as per reasonable standards of
accuracy: As standard of gathered information may vary
with purpose and enquiry, therefore, it is essential that
information must meet with reasonable standards.
• Statistical information must meet the comparison
criteria: In order to take effective decisions in business, it
is essential that homogenous information should be
gathered, so that comparison between previous or current
performance, or position of two companies can be made,
to take effective decisions.

Overview of statistical methods
In planning, collecting, organising and analysing the information,
a number of statistical methods can be used, as given below –
Central tendency – This method includes a number of techniques
to interpret the statistical information, such as arithmetic mean
that summarise the data by calculating average of same,
median that divides whole population into two halves, mode
that calculates high obtaining frequency. Thus, employing
these methods on business data, entire information can be
summarised more easily, to obtain accurate results.

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Correlation – It is mainly used to identifying the relationship
between two variables, where positive result shows that
variables are perfectly correlated, while negative one reflects
that extent at which one variable increases, decreases the
another.
Standard deviation – It can be used in business to measure the
volatility by measuring the risk of stock, way of quality
control, variance between expected and actual outcomes etc.

Types of data and information
Statistical data can be broadly classified into two major forms as –
Primary data: It includes raw and original information, which is
collected by applying primary methods like conducting a survey
on a small sample of population, questionnaire, personal interview
and more. To take customers feedback on quality of products, with
suggestions to improve the same, questionnaire methods can be
used by managers of Vodafone. This would help respective firm in
taking effective decisions to improve business performance.

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Secondary data: It includes past collected information which can
be gathered from government census report, business information
and more. By collecting such information, managers of Vodafone
can identify areas where company needs to make improvement, to
grab high attention and retention of loyal customers, for longer
period of time.

Sources to collect statistical information
To collect statistical information either primary or
secondary, a number of sources can be used –
Questionnaire – This source helps in collecting raw
information, whereby a firm like Vodafone can frame
questions to take feedback of its customers and other
stakeholders to achieve competitive advantages.

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Personal interviews – It is another main method of gathering
information that helps in analysing and evaluating response
of each interviewer regarding with a question.
Business data: This source provides past and current
information of business, through which future growth of
company can be forecasted. Using such data, Vodafone’s
managers can also take effective decisions to achieve high
competitive advantages as well.

Difference between a sample and a population
A population in statistics describes the whole data and
variables, on which different methods can be applied to
get result. But organising and interpreting the large
amount of data, creates difficulties in employing the
statistical tools as well. While a sample defines a small
size of data, that summarises the entire information in
brief and gives advantages, to obtain result in short
interval of time, with cost efficiency as well.

Value of statistical methods
For achievement of business objectives, it is essential for a
firm like Vodafone to analyse its current performance, by
comparing the same with other organisations that deal in
same field. As this company deals in telecommunication
field, therefore, to achieve high competitive advantages, it
needs to collect specific information.

Descriptive and Inferential statistics
Descriptive statistics is mainly used to determine the among
variables in a whole population or in a sample. It
summarises the statistical information in graphical form,
average mean, median and more, etc. so that conclusion can
be drawn more specifically. While inferential statistical
method uses a random sample of a population, by providing
more valuable result to take timely decisions, by comparing
two sets of data in easy manner. The main purpose, behind
usage of this method is to make rational decisions after
testing the hypothesis.

Difference between Descriptive and
Inferential statistics
S.No. Descriptive statistics Inferential statistics
1 It mostly focuses on introducing the
targeted population, on which
further statistical methods can be
applied.
It generalise the population by
randomly selected data to make a
sample, on which statistical methods
can be employed.
2 It helps in organising, analysing and
presenting the information in
systematic manner
It helps in predict the future
outcomes by comparing and testing
the hypothesis.
3 It describes the data which is
collected by using secondary sources
It provides the predicted information
about future growth of business, on
the basis of sample

Key findings
Net profit
Apple MSFT Amazon Netflix
2015 53,394 12,193 123 596
2016 45,687 20,539 187 2371
2017 48,351 25,489 559 3033
2018 59,531 16,571 1211 10073

Key findings of Apple Company
Mean 51189.67
Standard Error 4240.97
Median 48351
Standard Deviation 7345.58
Sample Variance 53957605.33
Skewness 1.48
Range 13844
Minimum 45687
Maximum 59531
Sum 153569
Count 3
Largest(1) 59531
Smallest(1) 45687
Confidence Level(95.0%) 18247.44

Key findings of Microsoft Company
Mean 18698
Standard Error 2833.52130749
Median 18555
Standard Deviation 5667.0426149801
Sample Variance 32115372
Kurtosis -0.7362231163
Skewness 0.1265308912
Range 13296
Minimum 12193
Maximum 25489
Sum 74792
Count 4
Largest(1) 25489
Smallest(1) 12193
Confidence Level(95.0%) 9017.5294159643

Key findings of Amazon Company
Mean 520
Standard Error 249.5829855312
Median 373
Standard Deviation 499.1659710624
Sample Variance 249166.666666667
Kurtosis 0.7348682097
Skewness 1.2355157881
Range 1088
Minimum 123
Maximum 1211
Sum 2080
Count 4
Largest(1) 1211
Smallest(1) 123
Confidence Level(95.0%) 794.2844501654

Key findings of Netflix Company
Mean 4018.25
Standard Error 2082.7854496243
Median 2702
Mode #N/A
Standard Deviation 4165.5708992486
Sample Variance 17351980.9166667
Kurtosis 2.9892051233
Skewness 1.6275421016
Range 9477
Minimum 596
Maximum 10073
Sum 16073
Count 4
Largest(1) 10073
Smallest(1) 596
Confidence Level(95.0%) 6628.3528588555

References
Ivezić, Ž., Connolly, A.J., VanderPlas, J.T. and Gray, A.,
2019. Statistics, data mining, and machine learning in
astronomy: a practical Python guide for the analysis of
survey data. Princeton University Press.
Mian, A. and Santos, J.A., 2018. Liquidity risk and maturity
management over the credit cycle. Journal of Financial
Economics, 127(2), pp.264-284.
Murdock, S.H., 2019. Applied demography: An introduction
to basic concepts, methods, and data. Routledge.

Thank you