Statistics for Management in Business and Economics

This assignment explores the application of statistical methods in business and economic data analysis. It covers evaluating data from various sources, analyzing qualitative and quantitative data, applying statistical methods in business planning, and communicating findings using charts and tables.

Statistics for management
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Contents
Introduction................................................................................................................................3
LO1 Evaluate business and economic data/ information obtained from published sources......4
P1 Evaluate the nature and process of business and economic data/ information from a
range of different published sources......................................................................................4
P2 Evaluate data from a variety of sources using different methods of analysis...................5
M1: Critically evaluate the methods of analysis used to present business and economic
data/ information from a range of different published sources..............................................6
LO2 Analyse and evaluate qualitative and quantitative raw business data from a range of
examples using appropriate statistical methods.........................................................................8
P3 Analyse and evaluate qualitative and quantitative raw business data from a range of
examples using appropriate statistical methods.....................................................................8
M2: evaluate the differences in application between descriptive statistics, inferential
statistics and measuring association.....................................................................................11
D1 Critically evaluates the differences in application between methods of descriptive,
exploratory and confirmatory analysis of business and economic data...............................12
LO3 Apply statistical methods in business planning...........................................................13
P4 Apply a range of statistical methods used in business planning for quality, inventory
and capacity management....................................................................................................13
M3 evaluate and justify the use of appropriate statistical methods supported by specific
organisational examples.......................................................................................................15
D2 Make valid recommendations and judgement for improving business planning through
the application of statistical methods...................................................................................22
LO4 Communicate findings using appropriate charts/ table....................................................23
P5 using appropriate charts/ tables communicate findings for a number of given variables
..............................................................................................................................................23
M4 Justify the rationale for choosing the method of communication.................................25
D3 Critically evaluate the use of different types of charts and tables for communicating
given variables.....................................................................................................................26
Conclusion................................................................................................................................27
References................................................................................................................................28
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Introduction:
This assignment is prepared to understand the usefulness of statistical methods in defining
business and economic information. There are varied sources for collection of business
information such as business statistics information, publicised information. Over this
information, statistical analysis tools are applied for extracting some useful detailed
information. The techniques mostly used are exploratory technique, descriptive analysis, and
exploratory analysis. The assignment provides in-depth detail regarding statistical techniques
for effective and proper analysis of information. There is also analysis of the communicating
methods which are used by businesses to independently define their business figures.
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LO1 Evaluate business and economic data/ information obtained from published
sources:
P1 Evaluate the nature and process of business and economic data/ information from a
range of different published sources
Nature: Business and economic data are the numerical data of the economic process which is
used for further economic statistics. Economic statistics contains a number of the
phenomenon for development of concepts and classifications which are useful for
understanding economic state. The business and economic data which are interpreted through
economic statistics defines economic behavior, economic movements, economic policies and
economic business decisions. The knowledge of business and economic data are extracted
through a number of sources which are available economy facts and figures which are
developed through in the ICT (Information and communication technologies) (George, et. al.,
2014).
Process: business or economic data process can be understood as a mechanism which
consists of entire channels from extraction to production to the utilization of available
resources for ultimate human consumption. The process defines the total process of
converting raw material into final product (George, et. al., 2014).
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P2 Evaluate data from a variety of sources using different methods of analysis
The business/ economic data which can are used for making propositions and analysis are
taken from mainly from two sources namely internal and external source. An organization
can internally get information about its business trends and business financial information
such as revenues, costs, and profitability and thus, the data is mainly quantitative. Also, the
data can be extracted from external sources which mainly relates to the wider perspective and
are associated with sectors like political, economic, social, technological and competitive
information. For instance: if a Volkswagen needs to know the economic possibility of sales
in near future, it would need to collect and interpret the social data (for knowing the trend).
The data can be further classified into primary data and secondary data (Peirson, et. al.,
2014).
The different kinds of data are collected through a number of sources such as published
statistics by a number of agencies, formal/ informal interviews conducted at a different
interval of time, self/ team investigation regarding a subject, and through questionnaire
technique (Peirson, et. al., 2014).
Published statistics: various organizations such as an office for national statistics, Eurostat,
newspaper authorities like financial times etc. publish data. The data can be further
implemented through statistical tools and techniques and interpretation can be made (Peirson,
et. al., 2014).
Formal/ informal interviews: Interviews are done for developing and extracting new data/
information.
Self-investigation: Self-investigation can also be done through questionnaire technique
which is to formulate and interpret the result (Peirson, et. al., 2014).
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M1: Critically evaluate the methods of analysis used to present business and economic
data/ information from a range of different published sources
The varied methods of analysis are descriptive statistical analysis, confirmatory data analysis,
and exploratory data analysis.
The descriptive statistical analysis is a method which includes series of actions like
collection, summarization, and interpretation of financial data/ information through charts
and tables. This explanation is restricted to the sample part and might or might not apply to
the entire population. Though, it quite useful as it presents the raw data into meaningful
information. The main tools of descriptive analysis are measures of central tendency and
measures of spread. The former is used for describing the central position for a data while the
latter describes the spread of data (Collis, and Hussey, 2013).
Confirmatory data analysis technique is useful for making interpretation through the
establishment of facts, hypothesis and implementing traditional tools like inference,
confidence, and inference, also through precision, variance, and regression. It is quite useful
as this technique makes use of findings for trial test and use tools like regression analysis,
variance analysis, hypothesis tests etc. It also has certain limitations such as it makes focus
only on hypothesis and its analysis is also restricted to that point (Collis, and Hussey, 2013).
Exploratory data analysis is a different segment. It makes use of presumptions, assumptions
and another hypothesis. It applies tools like quantitative tools and visuals/ graphic tools for
presenting the data. It also establishes uses confidence interval and margin of error
techniques (Collis, and Hussey, 2013).
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LO2 Analyse and evaluate qualitative and quantitative raw business data from a range
of examples using appropriate statistical methods.
Introduction:
This report presents usefulness of the business economic data and implementation of
statistical methods for analyzing the data and inferring data. Being a business data analyst,
the qualitative and quantitative raw data has been converted. The quantitative data includes
sales and net income and the tools applied are measures of central tendency.
P3 Analyse and evaluate qualitative and quantitative raw business data from a range of
examples using appropriate statistical methods.
There are mainly two types of data namely quantitative data and qualitative data. Qualitative
data can be understood as the data which cannot be measured numerically but can be tested
through the implementation of hypothesis and assumptions. Quantitative data can be
presented as the data which can be measured numerically and also can be mathematically
solved (Houghton, 2013).
Analyse and interpretation of quantitative raw data:
Year Sales Net income
2009 15,000 20,000
2010 20,000 25,000
2011 24,000 30,000
2012 20,000 40,000
2013 18,000 35,000
2014 25,000 50,000
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2015 20,000 45,000
2016 30,000 65,000
2017 24,000 45,000
Mean 21,778 39,444
Median 20,000 40,000
Mode 20000 45000
The organization data of sales and net income between years 2009-2017 are provided; the
application of descriptive data (mean, median and mode) summarises the business
information taken from its financial accounts. The mean or average presents that the
organization average sales for the last 9 years have been £21,778 while the average income
has been around £39,444. Median presents the middle value which is £20,000 for sales. This
middle point represents average only an average net income in accordance with median is
£40,000. Mode represents the value which arrives maximum time. The average sales are
£20,000 which represents that it arrives maximum time and average net income is £45,000.
The above averages show that the company must have above described sales and income
(Houghton, 2013).
Analyse and interpretation of qualitative raw data:
The qualitative raw data defines varied information from varied published sources. The data
includes feedback data of organization from a number of sources (Houghton, 2013).
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The hypothesis from the data which is to be tested is “The Company is focusing more on
increasing the number of customers rather than customer retention”. The hypothesis is tested
through correlation method (Houghton, 2013).
Particulars Number of
customers arrives in
one week
Number of
customers retains in
one week
1st week 10 3
2nd week 9 2
3rd week 8 3
4th week 5 2
Correlation coefficient 0.53
Standard deviation 2.16 0.58
The data above is 4 weeks describing two sections, namely, customer’s arrival in one week
and customer’s retention number in one week. The correlation coefficient has arrived at 0.53.
It is positive which shows that there is a positive relationship between both the data i.e.
number of customer’s arrival and a number of customers who retains. The positive
correlation shows that with the decreasing number of customer’s arrival, number of retain
customers also decreased (Houghton, 2013).
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M2: evaluate the differences in application between descriptive statistics, inferential
statistics and measuring association.
Differences in the application of descriptive and inferential statistics:
Descriptive statistics: This segment of statistics mainly uses the procedure of collecting,
summarising and presenting the data. It describes characteristics of a data and it mainly deals
with evaluating the frequency, distribution, and spread of a given sample. The main tools of
descriptive statistics are mean, median, and mode. All these tools deal with a given sample
and present a conclusion for the sample data only. Lastly, it can be analyzed that it deals with
summarising and presenting the data (Wildemuth, 2016).
Descriptive statistics describe the data through summarization and classification of data. It
analyses the data through classification and further implementation of varied measures such
as mean, median, and mode. These techniques are implemented over the data and frequency
and spread are measured. This technique is very popular and quite useful in case of analyzing
a sample from a given set of population’s data. It provides an average figure for analyzing the
sample and making a conclusion for the entire population (Wildemuth, 2016).
Inferential statistics: In this statistical tool, the sample data is analyzed for hypothesis
testing, formulating predictions and extracting inferences from the existing data. This
segment is used when a data has to be inferred and analyzed.
Inferential statistics is mainly used for establishing a relationship between two or more
variables. It sets out certain hypothesis and tests it accordingly. This statistics is quite useful
in describing the relationship between variables (Wildemuth, 2016).
Exploratory statistics: It is implemented to ascertain or development of relationship among
different variables through setting varied variables. This statistics is useful in terms of stating
relationship through different patterns (Wildemuth, 2016).
Conclusion:
From this report, it can be concluded that the different set of statistics such as descriptive,
exploratory and inferential statistics. Different statistics segments are used at the
implementation of statistical tools and define varied statistics patterns.
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D1 Critically evaluates the differences in application between methods of descriptive,
exploratory and confirmatory analysis of business and economic data.
The three statistical methods namely descriptive, exploratory and confirmatory are quite
different from each other.
Descriptive is the most common and simplest to apply to a range of data. This method mainly
applies to a selected sample extracted from a sample. Its major tools are measures of central
tendency and measures of spread. These tools provide central and deviation point from
average and it provides the simplest interpretation of data which can shorten a large set of
population and also advice for future decision making (Jackson, 2015).
On the other hand, confirmatory data analysis is a tool in which researcher makes their
findings and put their arguments for testing. It uses hypothesis for evaluation and
interpretation. It uses tools like variance analysis, regression analysis etc. The difference lies
that the descriptive is restricted to interpretation of sample (and the population) while the
confirmatory restricts the research to its assumption and argument put over testing and trial.
Exploratory analysis is on another hand, puts up few assumptions and further works for
formation of hypothesis and assumptions and it uses tools like visual tools, quantitative
methods such as of descriptive tools only. But since it is a statistical thinking, it might not
provide accurate answers (Jackson, 2015).
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LO3 Apply statistical methods in business planning
P4 Apply a range of statistical methods used in business planning for quality, inventory
and capacity management
The different range of statistical methods which are to be applied to the business planning
including inventory and capacity management, quality management are as follows:
Measures of variability: the measures for variability assess the varying degree from the
central position. The organization faces issues with respect to the difference in customer
services, inventory management, capacity etc. the techniques which can be used are a range,
inter-quartile range, variance and standard deviation for measuring changes (Weiss, and
Weiss, 2012).
Measures of probability: there are large probability measures which are applied to an
organization for data distribution in business processes. There is applicability of measures
like normal distribution curve, Poisson distribution curve, binomial distribution curve and
also makes use of inferences through setting confidence limits and margin error (Weiss, and
Weiss, 2012).
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Range
Inter-quartile
range
Variance
Standard
deviation

Figure: describing normal distribution curve
(Source: howmed.net, 2012)
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M3 evaluate and justify the use of appropriate statistical methods supported by specific
organizational examples:
There is a number of statistical methods which are used by companies/ organizations to
improve and evaluate their performances. The number of methods implemented by it is
quality management, inventory management and for knowing the capacity utilization. The
varied techniques are mean, mode, median, control chart, distribution curve, EOQ, capacity
utilization technique etc. the implementation of the techniques over organization examples
are provided as below through aid of tabular and graphical representation (Weiss, and Weiss,
2012).
Quality management:
S. No. Sample mean Mean
CL
UCL LCL
1 15.91 15.95 16.3672 15.5272
2 15.99 15.95 16.3672 15.5272
3 15.92 15.95 16.3672 15.5272
4 15.93 15.95 16.3672 15.5272
5 15.98 15.95 16.3672 15.5272
6 16.03 15.95 16.3672 15.5272
7 15.96 15.95 16.3672 15.5272
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8 15.93 15.95 16.3672 15.5272
9 15.96 15.95 16.3672 15.5272
10 15.83 15.95 16.3672 15.5272
11 15.99 15.95 16.3672 15.5272
12 15.96 15.95 16.3672 15.5272
13 15.83 15.95 16.3672 15.5272
14 15.91 15.95 16.3672 15.5272
15 16.05 15.95 16.3672 15.5272
16 15.99 15.95 16.3672 15.5272
17 15.86 15.95 16.3672 15.5272
18 16.01 15.95 16.3672 15.5272
19 15.98 15.95 16.3672 15.5272
20 16.02 15.95 16.3672 15.5272
21 16 15.95 16.3672 15.5272
22 15.9 15.95 16.3672 15.5272
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23 15.86 15.95 16.3672 15.5272
24 15.94 15.95 16.3672 15.5272
25 15.94 15.95 16.3672 15.5272
Average mean= 15.95
Standard deviation= 0.14
1
3
5
7
9
11
13
15
17
19
21
23
25
15
15.2
15.4
15.6
15.8
16
16.2
16.4
16.6
Sample mean
Mean CL
UCL
LCL
Distribution curve:
7.95 0
8.95 0
9.95 0
10.95 3.0287E-
277
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11.95 1.555E-177
12.95 5.5503E-
100
13.95 1.37727E-
44
14.95 2.37597E-
11
15.95 2.84958771
7
16.95 2.37597E-
11
17.95 1.37727E-
44
18.95 5.5503E-
100
19.95 1.555E-177
20.95 3.0287E-
277
21.95 0
22.95 0
23.95 0
24.95 0
25.95 0
26.95 0
27.95 0
28.95 0
29.95 0
30.95 0
31.95 0
5.00 10.00 15.00 20.00 25.00 30.00 35.00
0
0.5
1
1.5
2
2.5
3
Series2
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b) Inventory management
For inventory management, an organization makes use of techniques like economic order
quantity.
EOQ:
X (number of units): 360
Holding cost: £0.8/ per unit/ year
Ordering cost: £100 per order
EOQ:
= 2*360*100/0.8
=90,000 (it’s under root)
= 300 units per order
Number of orders:
Annual consumption/ Quantity in one order
=360 units /300 units
=1.2 orders or 2 orders
Time between orders:
=Days per year/ number of order
=250/2
125 days
c)
Capacity management:
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Capacity utilisation of the team is as follows
Actual output= 200 hours
Design capacity = 10 persons*7 days *7hours
= 490 hours
Utilisation= 200 hours/ 490 hours
= 40.81%
Efficiency: it is calculated by dividing
Efficiency measures through “Actual output/ effective capacity”
Actual output= 200 hours
Effective capacity= 10 persons*7 days* 5.6
=200 hours/ 392 hours
= 51.02%
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D2 Make valid recommendations and judgment for improving business planning
through the application of statistical methods;
Business planning makes use of a number of statistical tools for maintaining its business data
and effective working operations. The following are the major recommendations for
implementing statistical tools and operations:
 It is recommended to implement techniques like EOQ and capacity utilization for
proper inventory management. Inventory is the most crucial technique for
investigating. These provide data for proper implementation of statistical techniques
and thus, provide a proper answer.
 The service sector also requires implementation of techniques like modeling,
permutation and combination and many other such techniques (Cramér, 2016).
 The proper reporting of business planning ensures the development of blueprints
which predicts relevant and accurate answers and thus, ensures effective decision
making.
 The implementation of statistical tools must be further communicated through
communication channels like oral communication, written communication, and
graphical method (Plichta, et. al., 2013).
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LO4 Communicate findings using appropriate charts/ table:
P5 using appropriate charts/ tables communicate findings for a number of given
variables:
Communication is indeed important for sharing information with all the persons available in
the organization. Businesses have to make use of different types of methods of
communication for effective and relevant discussions. The most common method for
presentation of data is through the tabular presentation, graphical presentation. These are
simple and also are understandable to a layman. The following are two examples of
presentation for a different set of variables:
For instance: The inventory data for an organization includes annual consumption is 360
units, inventory holding costs is £0.8 per unit per cost, and ordering cost is £100 per order.
The implementation of statistical tools has provided answer such as EOQ is 300 units, a
number of orders are 2 orders and the time between orders are 125 days. It is tabulated as
below:
Tabular presentation of data:
Inventory management
EOQ 300 units
Number of orders 2 orders
Time between orders 125 days
The following is the graphical presentation of control chart for an organization data. A
control chart is formed with the purpose to analyze the business movements over a period of
time. It consists of an average line, upper control line, and below control line. The upper limit
and lower limits are formed by taking into consideration 3 times of standard deviation. The
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warning comes in the graph when dots go beyond 2 times of the standard deviation.
Considering an organizational example, a control chart is prepared. The chart clearly presents
that the organization activities are well controlled in a span period of time.
Graphical presentation of data:
Control chart
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M4 Justify the rationale for choosing the method of communication
The method for communicating information is a tabular presentation and graphical
presentation. These are selected because of the following reasons:
 It simplifies the data is quite an appropriate manner to be clearly understandable by a
layman.
 The graphical method is quite useful for analyzing the increasing or decreasing level
of any data.
 These are immensely and widely used for presenting business and economic
information. Businesses are supposedly required to present their financial information
through annual report/ annual statements and hence, it is their requisite to use these
techniques and communicate with all the stakeholders.
 Both the methods are the most simple and useful technique for presenting data.
 The graphical presentation includes a number of presentation through pie-charts, line
graphs, bar graphs etc. these are quite attractive which seeks attention and provide
large information in small parts.
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D3 Critically evaluates the use of different types of charts and tables for communicating
given variables.
For presenting the data in an attractive manner and for communicating information, different
types of charts which can be used are tables, graphs, charts etc. The graph is a graphical
presentation which describes the plotting of variables at different points. This can be made
even more attractive by inserting colors and shading. There are different types of graphs like
line, smooth curve; plotting of dots etc. these are formulated in accordance with data and
simplification in presenting data. It takes time to prepare graphs over Microsoft excel and
also need professional expertise for preparing such graphs.
Tabular presentation of data is another format for simplifying the data and easier to
understand. It can be well prepared in Microsoft Excel and makes the presentation simpler to
understand. Though tabular presentation requires proper implementation of time and efforts
and also require numerical figure which can be separated from the boxes and statistical tools
can also be implemented.
Overall, it can be ascertained that different communication methods are highly helpful and
interactive, even simple for analyzing by a layman. But there must be relevant professional
expertise in making such graphs and tabular presentation.
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Conclusion:
From this assignment, it can be concluded that statistical methods are quite useful tools for
simplifying business information to a large extent. The different statistical analysis namely
descriptive, exploratory and confirmatory analysis tool is quite interesting. The descriptive
tool makes use of techniques like measures of central tendency and method for dispersion.
The exploratory technique makes varied assumptions and hypothesis which provide analysis
through implementation of techniques like regression analysis, variance analysis etc.
Confirmatory analysis technique is useful as it makes ascertainment through assumptions and
hypothesis technique. These techniques are highly useful and interactive for ascertaining
business information. Further, there is also the implementation of communication tools like
graphs, tabular presentation etc.
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References:
 Collis, J. and Hussey, R., 2013. Business research: A practical guide for
undergraduate and postgraduate students. Palgrave macmillan.
 Cramér, H., 2016. Mathematical methods of statistics (PMS-9)(Vol. 9). Princeton
university press.
 George, G., Haas, M.R. and Pentland, A., 2014. Big data and management. Academy
of management Journal, 57(2), pp.321-326.
 Houghton, J.W., 2013. Economic implications of alternative scholarly publishing
models. In A Handbook of Digital Library Economics (pp. 125-141).
 Howmed.net, 2012. Normal Distribution Curve. [Online] howmed.net. Available at:
http://howmed.net/community-medicine/normal-distribution-curve/. Accessed as on:
23rd February, 2018.
 Jackson, S.L., 2015. Research methods and statistics: A critical thinking approach.
Cengage Learning.
 Peirson, G., Brown, R., Easton, S. and Howard, P., 2014. Business finance. McGraw-
Hill Education Australia.
 Plichta, S.B., Kelvin, E.A. and Munro, B.H., 2013. Munro's statistical methods for
health care research. Wolters Kluwer Health/Lippincott Williams & Wilkins,.
 Weiss, N.A. and Weiss, C.A., 2012. Introductory statistics. London: Pearson
Education.
 Wildemuth, B.M. ed., 2016. Applications of social research methods to questions in
information and library science. ABC-CLIO.
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