Statistics in Management Practices

This assignment delves into the application of statistics within organizational management. It utilizes various statistical tools, including ogive curves and scatter diagrams, to analyze key management functions such as inventory control and funding. Through four distinct activities, the project aims to demonstrate the crucial role of statistics in effective management decision-making.

STATISTICS FOR
MANAGEMENT

Table of Contents
INTRODUCTION...........................................................................................................................3
ACTIVITY 1....................................................................................................................................3
a) National Statistical Data..........................................................................................................3
b) Graphical and tabular representation of data acquired form Office of National Statistics.....6
c) Differences between CPI, CPIH and RPI Indices...................................................................8
d) Use of data collected form CPI subject to evaluate the annual inflation................................8
e) Importance of rate of inflation and requirement.....................................................................8
ACTIVITY 2....................................................................................................................................9
a) Use of ogive and median hourly earnings and the quartiles, mean and standard deviation. . .9
b) Mean and standard deviation for hourly earnings of London area.......................................11
c) Comparison of earning of London and Manchester area......................................................13
ACTIVITY 3..................................................................................................................................13
a) Evaluation of economic order quantity.................................................................................13
b) Assessment of requirement of re-ordering T-Shirts.............................................................15
c) The inventory policy cost......................................................................................................15
d) Current service level to the customers..................................................................................16
e) Work out for the reorder level to accomplishment of desired service level.........................16
ACTIVITY 4..................................................................................................................................16
a) The Office of the National Statistics produce line................................................................16
b) Data used form activity 2 to produce an ogive for cumulative % of staff vs hourly earnings
...................................................................................................................................................18
CONSLUSION..............................................................................................................................18
REFERENCES..............................................................................................................................19

INTRODUCTION
Statistics is a technique of analysing and presenting numerical data using tools and
techniques which can lead in effective graphical representation. Statistics is a process which is
used by managers in a business organisation to analyse and interpret the data of there various
processes such as production, selling, demand, supply and many more. Main aim of this project
report is to use statistical tools such as graphs, charts and curves to represent information
provided. In this project report, various activities are conducted to compare and analyse data.
Data of consumer price index and retail price index is procured in order to represent their
comparison. Further, the concept of economic order quantity is also been discussed by presenting
a scatter diagram using provided information. Data from Office of National Statistics is also
analysed to indicate change in CPI, CPIH and RPI. The concept of inflation is also analysed in
this report using annual inflation method. Various business management processes are used with
an integrated approach of statistics, so reliable results can be attained.
ACTIVITY 1
a) National Statistical Data
Inflation can be stated as quantitative measure of rate on which average price of a
particular set of commodities increases in a certain period of time. In general, it also indicates the
decreases in buying power of currency of nations (Bedeian, 2014). When price of products rise
then it majorly impact on cost of living of people. In this regard authorities of country like
Central Bank take necessary actions in order to control hiked price within permissible limits. It
helps in running economy more smoothly. Along with this, inflation rate can measure by various
initiatives, it includes- Consumer Price Indices (CPI), Retail price Index and CPIH.
Consumer Price Indices: CPI refers to a measure which determines weighted average of
price of particular bulk of products. It includes basic commodities like foodstuffs, transportation,
education, communication, medical care etc. In general, statisticians and organisations use CPI
method to measure inflation or deflation rate which impact on buying power of national
currency. This concept is widely used as an indicator of economy which suggests government

authorities an idea about changing price in particular period. Therefore, in order to control the
price, they can take effective decisions (Barrett and et. al., 2012). Furthermore, CPI statistics
usually include unemployed, self-employed and retired people who will face major issues when
price of basic necessities are rise.
CPIH: It includes same concept as CPI, the only difference is that CPIH includes
inflation which contains the occupancies for housing cost. This measure is used to examine the
changes in cost of living and movement of price of products like household goods, food items
and more.
Retail Price Index: RPI refers to one of the main measures of customer inflation which
is produced by national statistics. This kind of data is mostly used by government authorities for
different purposes such as wage negotiation, inflation and deflation rates, amount payable on
index-linked securities.
Statistical data in terms of CPI index
Year Average
2007 104.7
2008 108.48
2009 110.83
2010 114.48
2011 119.61
2012 123.74
2013 126.13
2014 127.97
2015 128.03
2016 128.88
2017 132.3

Statistical data in terms of RPI Index
Year Average
2007 206.55
2008 214.83
2009 213.68
2010 223.56
2011 235.18
2012 242.73
2013 249.96
2014 256.03
2015 258.54
2016 263.05
2017 272.48
b) Graphical and tabular representation of data acquired form Office of National Statistics
Graphical representation of Consumer Price Index from 2007 to 2017:
Year Average
2007 104.7
2008 108.48
2009 110.83
2010 114.48

2011 119.61
2012 123.74
2013 126.13
2014 127.97
2015 128.03
2016 128.88
2017 132.3
2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7
0
2 0
4 0
6 0
8 0
1 0 0
1 2 0
1 4 0
1 0 4 .7 1 0 8 .4 81 1 0 .8 31 1 4 .4 8
1 1 9 .6 11 2 3 .7 41 2 6 .1 31 2 7 .9 71 2 8 .0 31 2 8 .8 81 3 2 .3
A v e ra g e
Graphical representation of Retail Price Index from year 2007 to 2017:
Year Average
2007 206.55
2008 214.83
2009 213.68

2010 223.56
2011 235.18
2012 242.73
2013 249.96
2014 256.03
2015 258.54
2016 263.05
2017 272.48
2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7
0
5 0
1 0 0
1 5 0
2 0 0
2 5 0
3 0 0
2 0 6 .5 52 1 4 .8 32 1 3 .6 82 2 3 .5 6
2 3 5 .1 82 4 2 .7 32 4 9 .9 62 5 6 .0 32 5 8 .5 42 6 3 .0 52 7 2 .4 8
A v e ra g e
c) Differences between CPI, CPIH and RPI Indices
Consumer pricing index was introduced in 1996 whereas Retail pricing index was first
pinned in 1956 in United Kingdom. Main aim of CPI is to evaluate consumption of earnings
based on the salary or per capita income. But main aim of RPI is to identify the cost of living of
various individuals of a specific demography. RPI is related with government plans and policies

whereas CPI has no interference of these policies. In the case of CPI, data is sourced using
household information whereas information for RPI is used from office of national survey.
d) Use of data collected form CPI subject to evaluate the annual inflation
CPI plays an important role in measuring the inflation rate of nation’s economy. For this
process, statisticians analyse the data in appropriate manner, to create cost of living index.
Indexation is a process of ascertaining impact of inflation on society. Data which is collected of
CPI is used to evaluate annual inflation as it can help in identifying consumer pricing.
e) Importance of rate of inflation and requirement
Price rise is considered as main root of inflation which can be classified into three main
aspects. It includes Built-in, Cost-push and Demand-pull inflation, all of which majorly affects
cost of living index (Andreeva and Kianto, 2012). When demand of a particular commodity is
increased more rapidly as compared to production capacity than under this condition, it is known
as Demand-pull inflation. It also creates a large gap between supply and demand factors which
leads to increase price also. In contrast, Cost-push inflation occurs when production capacity
increases more than demand which contributes to increase cost of finished products. Other than
this, Built-in inflation covers adaptive expectations where labour demands more wages for
maintaining cost of living with increase in price of products.

ACTIVITY 2
a) Use of ogive and median hourly earnings and the quartiles, mean and standard deviation
More than O-give curve
Hourly earning in
Euro
(Class Interval)
No. of Leisure central
staff
(f)
More than O-give Cumulative frequency
Below 10 4 More than 0 50
10 but under 20 23 More than 10 46
20 but under 30 13 More than 20 23
30 but under 40 7 More than 30 10
40 but under 50 3 More than 40 3
Total 50
Less than O-give Curve
Hourly earning in No. of Leisure central Less than O-give Cumulative frequency

Euro
(Class Interval)
staff
(f)
Below 10 4 Less than 10 4
10 but under 20 23 Less than 20 27
20 but under 30 13 Less than 30 40
30 but under 40 7 Less than 40 47
40 but under 50 3 Less than 50 50
Total 50

Median determined by point of inflexion of More-than and Less-than type O-give curve:
From the mentioned graphical representation, Median in context with hourly earning for
leisure centre staff of London area is obtained as approximate £19.0.
Quartile: This kind of central tendency is used to analyse large data by fragmented into
smaller parts (Marchington and et. al., 2016). In general, it divides data into four equal parts in
terms of first quartile which covers 25% of data, Q2 includes 50%, Q3 as 75% and Q4 covers
entire data i.e. 100%. In general, only first and third quartiles are measured to measure inter-
quartile range.
Therefore, Quartiles of given can be calculated in the following manner:-
For First Quartile of deviation:-
lower limit (l) = 10, frequency (f) = 23, Class interval (h) = 10 and Total frequency (N/4)
= ∑F/4 = 12.5, cf = 4
Q1 = L + (N/4 – cf)/ f x h
= 10 + (12.5 – 4)/ 23 x 10
= 10 + 85/ 23
= 13.7
Similarly, third Quartile can be obtained by:-
Here l = 20, f = 13, h = 10 and 3N/4 = ¾ of ∑F = 37.5, cf =27
Q3 = L + (3N/4 – cf)/ f x h
= 20 + (37.5 – 27) / 13 x 10
= 20 + 105/13
= 28.07

Thus, in this regard, Inter-quartile range of given data can be calculated as:-
IQR = Q3 – Q1
= (28.07-13.7)
= 14.0 (approx)
b) Mean and standard deviation for hourly earnings of London area
In order to analyse a large data in appropriate manner, mostly statisticians use the concept
of central tendency which provides various measures (Kyriakarakos and et. al., 2013). According
to Professor Bowley, this method helps in representing the entire observations of a given data
into single form. Some major techniques include in this kind of method are mean, median, mode,
percentiles, deciles, quartiles and dispersions. As per present case study of data of London and
Manchester area, researchers have used following measures of central tendency:
Mean: It is also termed as arithmetic mean which gives average of a data by adding all
the observation then divide with total numbers. This kind of data covers entire observations
therefore, it reduces the chance of occurrence of error and gives more accurate outcomes as well.
Median: It is considered as simplest form of central tendency which divides a data into
two equal parts (Jiang and Pang, 2011). It is less affected by extremities therefore, can easily
calculated by O-give curve and frequency distribution method or simply divides a data into two
equal parts.
Standard Deviation: It can be defined as a measure of central tendency which is used to
quantify the amount of dispersions or variations of a set of values.
Mean is calculated by taking average of sum of observation as shown below:
Hourly
earning in
Euro
(Class
Interval)
No. of Leisure
central staff
(f)
Middle data
(x) (F*x)
Middle data
(x2) (F*x2)
Below 10 4 5 20 25 100

10 but under
20
23 15 345 225 5175
20 but under
30
13 25 325 625 4225
30 but under
40
7 35 245 1225 8575
40 but under
50
3 45 135 2025 6075
Total 50 1070 24150
Calculation
Mean = ∑Fx / ∑F
= 1070/50
= 21.4
Standard deviation = √ (∑Fx2 / ∑F) - (∑Fx / ∑F)2
= √(24150/50) – (21.4)2
= √483 – 457.96
= √25.04
= 5. 0 (approx)
Thus, in this regard, mean of given data is obtained as £21.4 while standard deviation as £5.0
respectively.
c) Comparison of earning of London and Manchester area
Basis of Comparison Manchester area London area
Median £14.00 £19.00
Interquartile Range £7.50 £14.00
Mean £16.50 £21.40

Standard Deviations £7.00 £5.00
ACTIVITY 3
a) Evaluation of economic order quantity
Economic order quantity which is often referred as EOQ is a measure which helps in quantify
goods which are purchased at a single time. This method is a technique of optimum lot size. In
which it is ensured that the quantity of units should be ordered in such a way that it will benefit
the organisation and will be result in future optimum utilisation of resources. This method
considers various aspects while determining economic quantity level. These aspects are demand,
rate and other quantity variables (Herrera and Schipp, 2014)
Costs which are included while calculating EOQ is ordering cost, per unit cost and
storing cost. This method is typically used as a measure in which different production levels are
identified along with ordering interval length to ascertain a units of products which should be
ordered to ensure maximum profit to the organisation. When relating with management, a
manufacturing company can attain high benefit from this kind of technique. Management can
minimise their cost of inventory and can effectively manage their resources such as
manufacturing stock. This technique is only limited with a kind of stock and that is raw material
(Haimes, 2015)
For example: If a company is engaged in producing clothing materials and then the raw
material such as threads which has to be procured by them from their vendors. In order to
ascertain units of threads that must be reordered from the vendor while considering all the factors
of business environment they can use EOQ to ensure best outcomes. EOQ can be measured using
a formula which is mentioned below:
EOQ = √[(2* demand* ordering cost) /carrying cost]
EOQ = √2SD / H

Where
S = ordering cost
These are the costs which are incurred due to the expenses made by the party while
ordering stock in an organisation. These costs can include various sub expenses such as cost in
preparation of a cost requisition, cost of stock, cost of labour required for inspection and cost of
supplier invoice. All of these costs are bought together which is related with the carrying costs.
This costs only incurs when a company orders inventory from their vendors and suppliers. It is
important to monitor these costs as they result in suitable order levels and minimisation of cost
(Lu and et. al., 2013).
C = Unit cost
Unit cost is referred to the total cost which is used in manufacturing a single unit of
product. This cost is a part or share of total cost which is incurred by an organisation in
developing their all batch. A unit cost is a collection of various costs such as variable, fixed and
mixed costs. Unit cost is a measure which helps in evaluating various aspects such as economic
order quantity. Direct material, labour and other overheads are also included in this unit cost.
This cost can be ascertained by dividing total production from total cost (Lam, 2012).
H = Holding cost
These are the cost which are related with the holding or storing of stock. There are
various sub costs involved in this cost such as warehouse cost, labour cost and maintainace cost.
This cost helps in ascertaining economic order quantity by which an organisation can ascertain
what will they incur if they order a particular units of goods. It is required to ensure sufficient
cash in the organisation to pay holding costs. These expenses are typically included in fixed costs
as they rent of warehouse usually remains fixed. This cost is denoted as 'H' while ascertaining
EOQ (Keller, 2015).
I = carrying cost
These costs includes maintenance, storage cost and other overheads. This cost is not
directly helps in ascertaining EOQ but it helps in determining percentage of carrying or holding
costs. This cost is calculated using all the fixed costs and variable costs which fluctuates

according to the units of products. A decreasing holding costs is beneficial for an organisation
unless it is influencing productivity and profitability of an organisation (Qiu, Qin and Zhou,
2016).
EOQ = √2*2000*5/2
= 100 Unit
From the above ascertainment of economic order quantity, it has been seen that quantity
which is demanded is 2000 units and the predicted holding costs for this stock is 5. By applying
the formula of EOQ it has been determined that if the company is ordering 100 units the they can
attain high profit.
b) Assessment of requirement of re-ordering T-Shirts
Reorder level is the inventory level which is required to be ordered again in order to
ensure smooth production of goods of a business organisation. Reorder level of a company or a
specific type of stock can depend on various aspects. These aspects are production cycle of the
goods, lead time, market demand of the goods and average use of those goods (Jessop, 2016).
This can be used as a measure of determining the units of stock which should be ordered again to
fulfil the demand for finished goods. From the above case scenario, it can be said that in order to
ascertain number of t shirts that are required to be ordered again a formula should be used which
is mentioned below:
Re order point = ( Delivery lead time * Average daily unit sales) + safety stock
= (28*2) + 150
= 206 units
there is a frequency asked that how often Jenny re order the T-shirts. It is calculated as
follows Frequency of Re-order = Annual consumption/ ROQ
= 2000/206 = 9.7 or 10 days
c) The inventory policy cost
Inventory policy cost is the total cost which is incurred in producing a finished unit. This
cost only includes the purchase price of raw materials which are procured for producing finished
goods. Inventory policy is the rules and regulations which are set by the company itself for

restricting their products due to some grounds. This cost is considered as fixed costs which is
calculated as follows:
Total cost = Cost of purchase + cost of order place + holding cost
Inventory Policy Cost = Purchase cost + Ordering cost + Carrying cost
= 10 + 5 + 2
= £17
From the above determination, it has been ascertained that inventory policy cost can be
calculated by using purchase cost along with ordering and carrying cost. By adding all these
expenses it has been ascertained that 17 dollars is the inventory policy cost.
d) Current service level to the customers
Service level refers to the level of service which is provided to clients by the business
organisation. From the above case scenario, it has been ascertained that the company is able to
fulfil the almost demand that is 95%. To calculate service level, formula and calculation is
developed below which states that when demand of goods are summed with available stock then
customer service level can be ascertained.
Current service level = Demand of T shirts per week * T-shirts available in stock for sale
= 40*95%
= 38 units
e) Work out for the reorder level to accomplishment of desired service level
Re-order level (ROQ) = (Lead time*daily average usage)+safety stock
= (28*2)+150
= 206 units

ACTIVITY 4
a) The Office of the National Statistics produce line
Retail price index
Year Average
2007 206.55
2008 214.83
2009 213.68
2010 223.56
2011 235.18
2012 242.73
2013 249.96
2014 256.03
2015 258.54
2016 263.05
2017 272.48

1 2 3 4 5 6 7 8 9 10 11
0
500
1000
1500
2000
2500
Year
Average
CPI (Consumer Price Index)
Year Average
2007 104.7
2008 108.48
2009 110.83
2010 114.48
2011 119.61
2012 123.74
2013 126.13
2014 127.97
2015 128.03
2016 128.88

2017 132.3
2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7
0
2 0
4 0
6 0
8 0
1 0 0
1 2 0
1 4 0
1 0 4 .7 1 0 8 .4 81 1 0 .8 31 1 4 .4 8
1 1 9 .6 11 2 3 .7 41 2 6 .1 31 2 7 .9 71 2 8 .0 31 2 8 .8 81 3 2 .3
A v e ra g e

b) Data used form activity 2 to produce an ogive for cumulative % of staff vs hourly earnings
O-give Curve:
CONSLUSION
From the above project report, it has been ascertained that Statistics is a concept which
helps in providing benefit to the organisation. By using various tools and techniques above it has
been seen that these tools can analyse and interpret various functions of management such as
inventory control and funding. This project is summarised in four activities which are using
different tools of statistics such as ogive curve, scatter diagram and many more. Four distinctive

activities are performed to build an understanding about importance of importance of statistics in
management.

REFERENCES
Books and Journals:
Andreeva, T. and Kianto, A., 2012. Does knowledge management really matter? Linking
knowledge management practices, competitiveness and economic performance. Journal of
knowledge management. 16(4). pp.617-636.
Barrett, K. C and et. al., 2012. IBM SPSS for introductory statistics: Use and interpretation.
Routledge.
Bedeian, A. G., 2014. “More than meets the eye”: A guide to interpreting the descriptive
statistics and correlation matrices reported in management research. Academy of
Management Learning & Education. 13(1). pp.121-135.
Haimes, Y. Y., 2015. Risk modeling, assessment, and management. John Wiley & Sons.
Herrera, R. and Schipp, B., 2014. Statistics of extreme events in risk management: The impact of
the subprime and global financial crisis on the German stock market. The North American
Journal of Economics and Finance. 29. pp.218-238.
Jessop, A., 2016. StatsNotes: Some Statistics for Management Problems. World Scientific Books.
Jiang, H. and Pang, Z., 2011. Network capacity management under competition. Computational
Optimization and Applications. 50(2). pp.287-326.
Keller, G., 2015. Statistics for Management and Economics, Abbreviated. Cengage Learning.
Kyriakarakos, G. and et. al., 2013. Intelligent demand side energy management system for
autonomous polygeneration microgrids. Applied Energy. 103. pp.39-51.
Lam, A. C., 2012. Women and Japanese management: discrimination and reform. Routledge.
Lu, L and et. al., 2013. A review on the key issues for lithium-ion battery management in
electric vehicles. Journal of power sources. 226. pp.272-288.
Marchington, M. and et. al., 2016. Human resource management at work. Kogan Page
Publishers.
Qiu, Z., Qin, J. and Zhou, Y., 2016. Composite Estimating Equation Method for the Accelerated
Failure Time Model with Length‐biased Sampling Data. Scandinavian Journal of Statistics.
43(2). pp.396-415.