Statistics for Management: Analyzing Data and Making Informed Decisions

This report delves into the analysis and interpretation of statistical data, focusing on sales expenses and net income. It explores various statistical calculations, including descriptive statistics and control charts, to identify relationships between variables and draw meaningful conclusions. The report also examines the importance of behavioral analytics and split tests in optimizing business decisions. By analyzing real-world scenarios, this report demonstrates how statistical methods can be applied to gain valuable insights and make informed decisions in a management context.

Statistics for management
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Table of Contents
Scenario 1.........................................................................................................................3
Task 1................................................................................................................................3
Introduction......................................................................................................................3
Conclusion.......................................................................................................................4
Scenario 2.........................................................................................................................6
Task 2................................................................................................................................9
Conclusion.....................................................................................................................10
References......................................................................................................................11
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Scenario 1
Task 1
Introduction
The given report is prepared to explain the analysis and interpretation of the given fact
and information. It also depicts the various statistical calculations performed on the data
relating to sales expenses and net income. Thus, it aims to identify the relationship
between the given two variables. Also, it explains and depicts the analysis of both
quantitative as well as qualitative types of data.
Year Sales Expenses Net Income % of net
income
(£) (£) %
2009 £15,000 £20,000 75.00%
2010 £20,000 £25,000 80.00%
2011 £24,000 £30,000 80.00%
2012 £20,000 £40,000 50.00%
2013 £18,000 £35,000 51.43%
2014 £25,000 £50,000 50.00%
2015 £20,000 £45,000 44.44%
2016 £30,000 £65,000 46.15%
2017 £24,000 £45,000 53.33%
1 2 3 4 5 6 7 8 9
£0
£10,000
£20,000
£30,000
£40,000
£50,000
£60,000
£70,000
Sales Expenses (£)
Net Income (£)
There are different methods of analysis and interpretation through which data can be
correctly evaluated. This indirectly helps to take correct decisions. The above pictorial
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chart explains the graphical presentation of the tabular data and information. It can be
observed that in the beginning years the sales expenses forms a major part of the total
income. However, from the last 4-5 years, the proportion of sales expenses has
decreased indicating a positive response for the overall company.
Sales expenses Net income
Mean 21777.78 Mean 39444.44
Standard Error 1479.28 Standard Error 4598.04
Median 20000.00 Median 40000.00
Mode 20000.00 Mode 45000.00
Standard Deviation 4437.84 Standard
Deviation
13794.12
Sample Variance 19694444.44 Sample Variance 190277777.78
Kurtosis 0.31 Kurtosis 0.13
Skewness 0.42 Skewness 0.42
Range 15000.00 Range 45000.00
Minimum 15000.00 Minimum 20000.00
Maximum 30000.00 Maximum 65000.00
Sum 196000.00 Sum 355000.00
Count 9.00 Count 9.00
The above table explains the computation of descriptive statistics. As such, it explains
the most widely used statistical parameters. The given data is not showing major
fluctuations in the given data. This indicates that there is not a much wider fluctuation.
The client is also planning to outsource the courier firm for the deliveries of their
products and for this purpose it is proposing to hire Hermes UK. However, it needs to
take an honest review of the concerned company so that it can be assured of the quality
of services being provided by the Hermes UK. The Managing director has obtained
reviews from Trustpilot.co.uk and the majority of the review indicates that the existing
customers of Hermes UK are not satisfied with the services since the major drawback is
that the company is not concerned about the timely delivery of services.
Thus, it is advisable to consider another delivery service provider. Otherwise, it might
face financial crisis.
Conclusion
From the discussion throughout the report, it can be concluded that suitable and
appropriate conclusions can be drawn if the data is presented in a tabular and graphical
form since it helps and assists the reader or the user to correctly take the crucial
decisions as a result of which even non-technical users can understand and accordingly
take the decisions.
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5

Scenario 2
(a)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
15
15.2
15.4
15.6
15.8
16
16.2
16.4
16.6
Mean (CL)
UCL
LCL
Sample Mean
The above control chart depicts the control charts of four samples observed. It can be
clearly seen that about 10 points are below the mean or class limits. On the other hand,
13 points are above the class limits.
(b) Inventory management
EOQ stands for Economic Order Quantity which is the amount of product that should be
ordered to minimize the total of inventory holding costs and ordering costs. Inventory
holding cost and ordering costs tend to have a direct negative correlation, meaning it is
usually cheaper to order larger quantities of the same item
Demand for product X = 360
Holding cost = £0.8/unit/year
Ordering Cost = £100 Order
Work Days in year = 250
EOQ= √2∗A∗Cp
Ch
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Where,
A= Demand for the year
Cp = Ordering Cost
Ch = Holding cost
EOQ= √ 2∗360∗100
0.8
=300 Units
No of Orders = Annual Demand / EOQ Quantity
= 360/300
= 1.2 or 1 Orders
Expected Time between orders = No of days / No of orders
= 250/1
=250 Days
Total Cost of EOQ = Ordering cost + Carrying cost
=(1x100) + (0.8*360)
=100+288
=388
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(c) Capacity Management
Assumption
The working is done for 5 days in a week. The actual hours worked in an week are
(200/5) 4 Hrs.
Capacity utilisation = Actual level of Output / Maximum possible Output x 100
= 4 / 8 x 100
= 50%
Efficiency = Actual Output/ Effective Capacity
= 4 / 5.6 x 100
= 71.428%
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Task 2
Behavioral analytics: Next to web analytics it is common to install tools like Hotjar or
CrazyEgg to analyze the scroll, click, and mouse behavior of page visitors. Heatmaps
and visitor recordings are great to gain those insights.
Just from the web and behavioral analytics, one cannot get reliable and valid
improvements. But, he can get really good insights in how the visitor behaves and what
items he could improve.
Split tests: The next step every data analysts do is to implement these improvements
using Google Optimize or WVO. These tools allow running A/B tests and automatically
providing the result of the test under a predefined confidence interval.
If those online tools are not sufficient you can export your data to a spreadsheet or
statistical analysis software to run descriptive summaries and statistical tests.
Descriptive summary: Most researchers run descriptive statistical tests to understand
the nature of the data collected such as the range in which the data points fall into or
how the data points are distributed. The most commonly used measures are means,
medians, variance, and so on.
Comparing means: Going deeper into the statistical analysis is the end goal is to
compare the means and find a statistically significant difference (under a certain
confidence interval - i.e., 95% or 99%). Depending on the data set, there are a few tests
one can run to compare means.
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Conclusion
From the discussion throughout the report, it can be concluded that the most important
skills required for getting placed in a consulting firm is Analytical skills. It’s not unusual
to see its misuse in some news article on the internet. It can be argued that this misuse
arises due to the inherent nature of present day statistics. Also, econometrics is the
statistics that economists need to know. On a more practical note, the statistics masters
will almost certainly be more difficult.
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References
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versus epoch differences. Journal of Climate, 28(24), 9969-9976.
 Elamir, E. A. 2015. Analysis of Mean Absolute Deviation for Randomized Block Design
under Laplace Distribution. American Journal of Theoretical and Applied Statistics, 4(3),
138-149.
 Figueiredo Filho, D. B., Paranhos, R., Rocha, E. C. D., Batista, M., Silva Jr, J. A. D.,
Santos, M. L. W. D., & Marino, J. G. 2013. When is statistical significance not
significant?. Brazilian Political Science Review, 7(1), 31-55.
 Frost, J., 2013. How to Interpret Regression Analysis Results: P-values and
Coefficients. The Minitab Blog. [Online]. Also available at
http://blog.minitab.com/blog/adventures-in-statistics-2/how-to-interpret-regression-
analysis-results-p-values-and-coefficients
 Main, M. E., & Ogaz, V. L. 2016. Common Statistical Tests and Interpretation in Nursing
Research. International Journal of Faith Community Nursing, 2(3), 5.
 Nahas, J. J., 2012. Statistical Design of Experiments‐Part IV Analysis of Variance.
University of Notre dam. [Online]. Also available at
https://www3.nd.edu/~jnahas/DoE_III_ANOVA_V2.pdf.
 Schneider, J. W. 2015. Null hypothesis significance tests. A mix-up of two different
theories: the basis for widespread confusion and numerous misinterpretations. Danish
Centre for Studies in Research and Research Policy, 102(1), 411-432.
 Winter, B. 2015. The F distribution and the basic principle behind ANOVAs. Tutorial.
Also available at http://www.bodowinter.com/tutorial/bw_anova_general.pdf.
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