Health Professional Wage & EOQ Analysis

This assignment delves into the comparison of hourly rates for health professionals in different regions, specifically southeast and northeast, utilizing statistical analysis to highlight potential discrepancies. The study also tackles a practical application by calculating the Economic Order Quantity (EOQ) for olive oil bottles at a local supermarket. Finally, the research findings are presented through effective communication strategies.

STATISTICAL ANALYSIS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
TASK 1............................................................................................................................................1
Change in gross annual earning in public and private sector......................................................1
Gap between the gross annual earnings of male and female......................................................2
TASK 2............................................................................................................................................3
Evaluation and analysis of difference in hourly pay rates (South East).....................................3
Analysis and evaluation of weekly turnover...............................................................................8
TASK3...........................................................................................................................................10
Understanding the Supply of a local supermarket....................................................................10
TASK4...........................................................................................................................................12
CONCLUSION..............................................................................................................................13
REFERENCES..............................................................................................................................15

INTRODUCTION
Statistical analysis is one of the data analytics tool. Statistical analysis includes collection of
data and scruitinizing of data using statistical and mathematical models. A sample is drawn from
the statistical data and then statistical tests are applied thereon. Importance of statistical analysis
is all pervasive i.e. in economy as a whole or in the organisational world. Information that cannot
be determined using financial analysis can be determined using statistical analysis (Little and
Rubin, 2014). These information is then used by managers and supervisors in making decisions
and forecasting the future. In the present report, statistical tests have been applied to the various
data. This includes calculation of percentage change in the earning capacity of different segments
along with the interpretation, statistical calculations like mean, median standard deviation and
coefficient of correlation is also present in this report along with the required tables and charts.
This report also includes calculation of economic order quantity with the understanding of
supply of local supermarket.
TASK 1
Change in gross annual earning in public and private sector
Particulars 2009 2010 2011 2012 2013 2014 2015 2016
private sector 46913 46532 46798 48018 48899 46456 50284 51930
change -381 266 1220 881 -2443 3828 1646
% change -1.00% 1.00% 3.00% 2.00% -4.00% 8.00% 3.00%
public sector 55862 57377 57850 58452 59879 60583 61585 62064
change 1515 473 602 1427 704 1002 479
% change 3.00% 1.00% 1.00% 2.00% 1.00% 2.00% 1.00%
In the calculation of above data, approximated percentages have been considered. From
the above data it can be seen that in case of private sector, there is more variation in the growth
of annual earnings in comparison to that of public sector. In the years like 2010 and 2014, the
gross annual earning in private sector declined drastically by 4%. The reasons behind this
downfall could be regulations of government, decline in the profit of private sector,

discontinuation of private companies, etc., (Mandel and Semyonov, 2016). However, in 2015,
gross annual earnings in private sector has shown a high jump to 8%. This much percentage
increase in 2015 is a good indication of the profit earning capacity of the private sector. In case
of public sector, percentage change in the combined annual earnings can be seen as constant.
There is the variation by just 1%. This means in public sector, the rotation of employees and
their salaries is constant along with the total earning capacity of the sector. The reason behind
this could be harsh recruiting process in the public sector with the impact of corruption.
However, like private sector, public sector has not shown negative earning in any year. The
highest percentage of growth in public sector was seen in 2010 i.e. 3%. In most of the following
years, change can be seen as just 1%.
Gap between the gross annual earnings of male and female
The gap between the gross annual earnings of male and female can be determined
separately in public sector in private sector.
Public sector 2009 2010 2011 2012 2013 2014 2015 2016
male 30638 31264 31380 31816 32541 32878 33685 34011
female 25225 26113 26470 26636 27338 27705 27900 28053
gap between male
and female 5413 5151 4910 5180 5203 5173 5785 5958
private sector 2009 2010 2011 2012 2013 2014 2015 2016
male 27362 27000 27233 27705 28201 28442 28881 29679
female 19551 19532 19565 20313 20698 21017 21403 22251
gap between male
and female 7811 7468 7668 7392 7503 7425 7478 7428
In case of both the sectors i.e. public sector and private sector, gross annual earnings of
male are high in all the years. It can't be seen in any year that the annual earnings of female are
more from the annual earning of male in any of the sector. When talking about the gap between

the earnings, in case of public sector gap between the gross annual earnings of male and female
is less than that of private sector (Bradley, Postel-Vinay and Turon, 2017). Reason behind this
could be that the share of employment of women in the public sector than the share of women in
private sector. There are fewer women working in private sector than compared to public sector.
The another reason could be that the remuneration of women in public sector is high than that of
women in private sector. Or, the variation between the remuneration of male and female is
higher than the variation in the remuneration of male and female in private sector. The gap in all
the years in public sector as well as private sector is constant. That means the reason behind the
gap is also constant in all the years and no initiatives have been made to identify the reason and
taking corrective measures.
TASK 2
Evaluation and analysis of difference in hourly pay rates (South East)
 Ogive charts for estimating median hourly earnings and quartiles
Hourly earnings Frequency Cumulative frequency
0 – 10
10 – 20
20 – 30
30 – 40
40 – 50
8
46
26
14
6
8
54
80
94
100

Median: Median refers to the average or middle number of the values in the given list of
numbers (Busk and Marascuilo, 2015). For the purpose of calculating median, the given numbers
must be arranged in an ascending order.
Calculation of median:
Median = L1+ (N/2) – c/f*i
here, L1= lower limit
N= total frequency
c/f= cumulative frequency of previous class-interval
i= class-interval
m= (N/2) i.e. 100/2= 50, this falls in the cumulative frequency of 54 and class interval (10-20)
L1= 10; N=100; c/f= 8; and i= 10
M= 10+ [(50-8)/46]*100

M= £19.13
Quartile: Median divides the given set of numbers in half, while quartile divides the set of
number in quarters (DiMaggio, 2013). That means the given set of numbers are arranged in an
ascending form and then are divided in four segments.
Calculation of quartile
q1= 100/4 =25 that falls in the cumulative frequency of 24 and class interval (10-20)
Q1= 10+ (25-8)/46*10
Q1= £13.69
q3= N/4= 3(100/4) = 75, that falls in the cumulative frequency of 80 and class interval (20-30)
Q3=20 + (75 – 54)/26*10
Q3= £28.07
As per the above calculation, the median hourly earnings of health professionals are determined
to be 19.13. That means the wage rate is nearly to 19 per hour. From the above calculation it has
been identified that half of the health professional are generating their hourly salary rate at below
19 and half of the health professional are generating their salary rate at above 19 (Glied, Ma and
Pearlstein, 2015). In short, the average hourly salary of health professionals is 19. As per the
calculation of quartile values i.e. Q1 and Q3, 25% of the health professional are being paid to the
equal or below 14 and 75% of the health professionals are being paid to the equal or below of 28.
 Mean and standard deviation of hourly earnings
Calculation of mean
Class interval frequency mid-value FX
0 – 10 8 5 40
10 – 20 46 15 690
20 – 30 26 25 650
30 – 40 14 35 490

40 - 50 6 45 270
100 2140
Mean = ∑FX / ∑F
= 2140/ 100
= £21.4
Calculation of standard deviation
Class
interval
frequency Mid-value
(X)
FX DX
(X-A)
FDX FDX2
0 – 10 8 5 40 -20 -160 3200
10 – 20 46 15 690 -10 -460 4600
20 – 30 26 25 650 0 0 0
30 – 40 14 35 490 10 140 1400
40 - 50 6 45 270 20 120 2400
100 2140 11600
Assumed mean is considered as 25
Standard deviation: √∑Fdx2/N – (∑Fdx/N)2
= √11,600/100-(-360/100)^2
= √116- (-360/100)^2
= √116 – 129,600/10,000
= √116 – 12.96
= √103.04
= £10.15

 Comparison of the answers of (a) with the answers of (b)
Statistical measurement North East South East
Mean £16.75 £21.4
median £14.55 £19.3
Standard deviation £7.40 £10.15
As per the calculations, and the results of north east region, it is clearly visible that the
results of both the regions are different. Gross hourly earnings of south east region are much
better than that of North East region in all the perspective i.e. mean, median and standard
deviation. This means that the average hourly earnings of the health professional in South East
Region is more than the average hourly earnings of health professional in North east region
(Leigh and Blakely, 2016). which is £19.13 of the South East health candidates; on the other
hand, North East health candidates have £14.55 which is comparatively lower. Along with this,
deviation of series and frequency is also low of North East health candidates to £7.40 which
demonstrates less spreaders or variability of the employee’s wages rate to average wages.
However, hourly earnings of heath professional in South east region show standard deviation of
£10.15, which is high, this indicates high variability.

Analysis and evaluation of weekly turnover
a) Scatter diagram
As the floor area increases, the average weekly turnover also increases. This means in
order to increasing the turnover, outlets in the airport terminal should be increased.
b) Line of best fit

c) Estimation of turnover if a new outlet of 30sqm is built
Y = 0.157x + 0.202
= 0.157 (30 sqm) + 0.202
= 4.912
d) Calculation of correlation of coefficient
e) Statistical validity and factors that can affect weekly turnover
Statistical validity defines the extent of accurateness and reliableness of the conclusions
that are drawn from the application of statistical tests (Kawada, 2017). In order to increase the
statistical validity, adequate sample size must be considered and correct test should be applied
for the purpose of analysing the data. Following two factors can have a significant impact to the
average weekly turnover:
 Variation in the number of passengers.
 Enforcement of government regulations.
TASK3
Understanding the Supply of a local supermarket
a) Number of delivery in a current year

As per the given case study:
 Delivery of olive oil is done in 12 days
 Cost of delivery = £20
 Demand of olive oil in last year = 450,000
 Storage cost= 25% of the cost price
 Cost price = £2
 Number of days in a year: 365 days
 Supermarket closed = 5 days
 Therefore, total operating days of supermarket = 365 - 5 = 360 days
Number of deliveries = Number of days/ Number of delivery days
= 360 days /12 days
= 30 deliveries
b) Delivery of number of bottles in each delivery
Total demand = 450,000 bottles
Number of deliveries = 30
= 450,000/30
= 15,000 bottles are delivered in each delivery
c) Calculation of economic order quantity (EOQ)
With the help of economic order quantity, a firm can determine the economic quantity of
its products to be supplied in each order to maintain the minimum cost level (Economic Order
Quantity, 2011).
EOQ= √2AO/ C
Here,

A = Annual demand
O = Ordering cost per order
C = Carrying/holding cost per unit
=√(2*450,000 units *£20)/£2
= 3000 bottles
d) Current operating model and economic order quantity
In case of current situation:
Units of current order = 15,000
Ordering cost (OC) = Number of order *per cost of order
= (450,000/15,000) *£20
= 30 orders * £20
= £600
Carrying cost (CC) = Average inventory * cost of holding per unit
= (0 + 15,000)/2 * £2
= 7,500 units * £2
= £15,000
Total cost = Ordering cost (OC) + carrying cost (CC)
= £600 + £15,000 = £15,600
In case of economic order quantity:
Economic order quantity = 3,000 units
Ordering cost (OC) = Number of order * cost per order
= (450,000/3,000) *£20
= 150 orders * £20
= £3000
Carrying cost (CC)= Average inventory * cost of holding per unit
= (0 + 3,000)/2 * £2
= 1500 units *£2

= £3,000
Total cost = ordering cost (OC) + carrying cost (CC)
£3,000 + £3,000 = £6,000
It is clearly visible from the above working that total cost in the current situation is higher
than the total cost in the case of Economic Order Quantity (Rushton, Croucher and Baker, 2014).
If the supplier will continue at its present situation, the profit from his business will be less.
Switching to the level of EOQ, will increase the profitability of the supplier.
TASK4
a) Scatter diagram
From the data it can be concluded that the average weekly turnover of the outlets in
airport terminal increases with the increase in the area of the outlets. More the area of the outlets
more will be the turnover (Zentes, Morschett and Schramm-Klein, 2017). In this study area of
the outlets is the main factor that affects the turnover of the outlets. In some cases, turnover
remains constant and even declined in the case of increase in area, the reason behind this could
be the variation in the number of passengers or the number of flights.
b) Line chart

From the study of gross annual earnings and the information of this chart, it can be
concluded that the gross annual earnings of male in public sector increases with the years
(Ehrenberg and Smith, 2016). There can be two reasons behind this, firstly there is increase in
the employment of males in the public sector or secondly, the remuneration of males in the
public sector has been increased or there could be the combined impact of both the reasons.
CONCLUSION
From the above study it has been concluded that, application of statistical test is necessary in
all the areas. Information derived from application of statistical test helps the management in
making the economic decisions for the organisation and is also important for the government in
analysing the quantitative data about the country in various sectors. From the above study it is
determined that there is more variation in the average earnings of private sector than that of
public sector. The gap between the earnings of male and female is more in the case of private
sector. In the next segment of this study difference between the hourly pay rates in South east
region and North east region are identified by application Mean, Median and standard deviation
tests. It is found that hourly rates to the health professional in south east region are better than
that of North east region. This study also includes the calculation of EOQ of olive oil bottles for
a local supermarket. And finally the findings from the above study has been communicated.

REFERENCES
Books and Journals
Bradley, J., Postel-Vinay, F. and Turon, H., 2017. Public sector wage policy and labor market
equilibrium: a structural model. Journal of the European Economic Association, 15(6),
pp.1214-1257.
Busk, P. L. and Marascuilo, L. A., 2015. Statistical Analysis in Single-Case Research. Single-
Case Research Design and Analysis (Psychology Revivals): New Directions for
Psychology and Education. pp.159.
DiMaggio, C., 2013. Introduction. In SAS for Epidemiologists (pp. 1-5). Springer New York.
Ehrenberg, R.G. and Smith, R.S., 2016. Modern labor economics: Theory and public policy.
Routledge.
Glied, S.A., Ma, S. and Pearlstein, I., 2015. Understanding pay differentials among health
professionals, nonprofessionals, and their counterparts in other sectors. Health
Affairs. 34(6). pp.929-935.
Kawada, T., 2017. Risk Factors of Frontotemporal Dementia with Special Reference to
Statistical Validity. Archives of Medical Research. 48(3). pp.303.
Leigh, N.G. and Blakely, E.J., 2016. Planning local economic development: Theory and
practice. Sage Publications.
Little, R.J. and Rubin, D.B., 2014. Statistical analysis with missing data (Vol. 333). John Wiley
& Sons.
Mandel, H. and Semyonov, M., 2016. Going back in time? Gender differences in trends and
sources of the racial pay gap, 1970 to 2010. American Sociological Review. 81(5).
pp.1039-1068.
Rushton, A., Croucher, P. and Baker, P., 2014. The handbook of logistics and distribution
management: Understanding the supply chain. Kogan Page Publishers.
Zentes, J., Morschett, D. and Schramm-Klein, H., 2017. Store-based Retailing–General
Merchandise. In Strategic Retail Management (pp. 47-70). Springer Gabler,
Wiesbaden.
Online

Economic Order Quantity. 2011. [Online] Available through
:<http://www.accountingexplanation.com/economic_order_quantity_eoq.htm >.
[Accessed on 24th October 2017]