Statistical Analysis of Business Data and Decision Making Report

This report delves into the application of business statistics for effective decision-making. It begins by introducing the importance of statistics for managers, emphasizing its role in analyzing data to improve operational efficiency and reduce costs. The report then examines the analysis of both qualitative and quantitative data using various statistical methods, including descriptive and inferential statistics. It provides a detailed comparison of these statistical methods, highlighting their respective applications. Furthermore, the report explores the use of charts and tables for communicating findings, emphasizing their importance in presenting data in an easily interpretable format. It also discusses the factors influencing the choice of communication methods. In conclusion, the report underscores the significance of statistics in the decision-making process, reinforcing the importance of data analysis in business management.

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Statistics for Management

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Contents
INTRODUCTION...........................................................................................................................1
LO2..................................................................................................................................................1
P3 Analysis of qualitative and quantitative raw business data using various statistical methods
................................................................................................................................................1
M2 Evaluation of application between inferential statistics, descriptive statistics and
measuring association.............................................................................................................3
LO 4.................................................................................................................................................4
P5 Use of appropriate charts/tables in order to communicate findings for a given variables.4
M4 Justification of reason for choosing the method of communication................................6
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7

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INTRODUCTION
Business statistic is a tool which is used by managers to analyse the statistics and make
effective decision to improve the business operational efficiency and reduce its cost (Keller,
2015). Statistics is important for managers as they require the previous year’s data to develop
new and effective strategies. This report contains the detail analysis of raw business data with the
help of using various different statistical methods. It also contains the findings from these data
with the help of charts and tables. This reports contains the importance of statistics for
management and how they can help in decision making process.
LO2
P3 Analysis of qualitative and quantitative raw business data using various statistical methods
Qualitative data: Qualitative data are the data which can not be converted in a numerical
form, it is a data which characterize and approximates. In order to collect these types of data
there are many various methods which includes conducting focus groups, one to one interview
and other similar methods. In this data’s are arranged in a categorical manner on the basis of its
properties and attributes.
Quantitative data: quantitative data are the data which can be expressed in the numerical
forms and can be counted in order to analyse it for effective decision making.in this type of data
every data which is collected in form of numbers are associated with an unique numerical value.
It help mangers to calculate and analyse any quantifiable data using various statistical and
mathematical methods in order to take effective decision making on the basis of the numerical
derivatives (Zyphur and Oswald, 2013). There are various steps involved in the analysis of raw
data in qualitative and quantitative data following are methods discussed:
Quantitative Data Analysis: Once the data is ready for the analysis mangers uses various
methods to analyse data and help managers to take effective decision for the organisation.
Following are the two methods:
Descriptive statistics: In this the managers try to find out the pattern in the data it is known
as the first level of analysis or statistics they are several used descriptive analysis as mentioned
below:
 Mean: It is a numerical average of a given set of values.
 Median: It is the mid point of the given set of data in numeric values.
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 Mode: It is the most repeated data in an data set.
 Percentage: It relates the data to the number of respondents in the larger group of
respondents.
 Frequency: It related to the number of times similar data is repeated in a given data
set.
 Range: It relates to the lowest and highest value in a given data set.
Descriptive statistics provides the numbers in an absolute forms. It is used by the mangers
to take effective decisions when the data provided is limited and not generalised to larger
population (Siegel, 2016).
Inferential statistics: It is used by the managers to analyse the trend and relation between
various several variables relating to the larger population. In this type of statistical method
managers usually generalize the data results to the entire population. There are various types of
analysis which are used by the managers to analyse the trends, following are some of the
methods:
 Correlation: It is type of analysis which defines the relation between two different
variables. It managers find any correlation between data it means that these two
data have some kind of relation.
 Regression: It is data analysis which also identifies any relation between the data.
Such as the guess on the profit margin of a company on the basis of its revenue.
 Analysis of variance: This is a statistical method which is used by the managers in
order to test a degree to which data is different from each other or varies in a
given experiment.
Qualitative Data Analysis methods: Once they data is collected and prepared it
becomes ready to be analysed (Ross, 2017). In order to analyse these data mangers uses various
several methods which include the following:
Content Analysis: It is one of the most commonly used methods of data analysis under
qualitative analysis. It help managers to analyse the information which is documented in the
forms of media, texts or physical items.
Narrative Analysis: In this type of methods data which is collected in the form of
qualitative is analysed by validating the content which is received from various other sources,
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which includes the observation taken from the field, interviews of different respondents or
surveys conducted by the company.
Discourse Analysis: It is also used by mangers to analyse the interactions or feedbacks
received from different customers to analyse and identify the ways in which it can improve its
quality of goods provided (Groebner and et.al., 2013).
Grounded Theory: In this of data analyses the qualitative data is used t o explain the
particular phenomenon which has happened. It is done by studying various similar cases in
various settings and this also uses data in order to derive the a casual explanation.
M2 Evaluation of application between inferential statistics, descriptive statistics and measuring
association
Descriptive Analysis Inferential Analysis
It is known as the first level of analysis which
is used by managers to find out the various
trends and patterns. It is used by managers to
calculate the average performance of the
company and make budgets and take strategic
decisions in order to improve its operational
efficiency and reduce its cost in order to
generate the revenue for the company.
It is type of analysis which is considered to a
complex analysis as it helps managers to find
out the relation between the data and
generalize its result in order to make future
predictions. This type of analysis is used by
managers to forecast company sales and take
effective decisions to achieve the desired
target and fulfil its organisational goal.
Few examples of statistical methods which
are used by managers to carry out descriptive
analysis.
Mean: average of the entire given data
Median: centre point of the data
Mode: most repeated value in a data array.
Frequency: Number of repeated outcomes
Range: Lowest and highest value in a data
Few example of statistical methods used by
managers are as follows:
Correlation: It is used by managers to
describe the relation between variable.
Regression: It is used to predict the relation
between two variables.
Variance analysis: it is used by managers to
test the extent to which data differs.
Measuring Associations: Measuring association is statistic is referred to as the various
factors which are used by managers to quantify the relation ship between variables. This is a
methods of analysis which determines the strength of association which is used by managers to
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check its dependency of characteristics of data available in each variables. Mangers uses various
types of methods such as discussed below:
 Pearson’s correlation coefficient: It is used by managers to quantify the
association which is seen two variable measured on a scale of interval.
 Spearman rank order correlation coefficient: It is designed by managers to
find out the measure of strength in a given constant direction.
 Chi square test: It is a test which is associated with the measurement of the
association between two given categorical variables.
LO 4
P5 Use of appropriate charts/tables in order to communicate findings for a given variables.
Charts and Tables: It is a graphical representation of data which is used by managers to
easily interpret the data and make it more effective to be understood by every user. This helps
managers to easily analyse the trend which is related to the sales and taste of preference of the
customer. This helps managers to understand and create effective strategies in order to improve
the operational efficiency of the company (Haltiwanger, and et.al., 2012).
Communication of findings: It is very important for a manger to communicate its data
finding from the given variable to analyse the trends and take effective decisions to improve the
financial performance of the company. As accounting data are presented in the forms of tables
and accounts it is easily interpreted by the managers to check its financial position and take
effective decisions to improve its position and gain competitive advantage over its competitors.
There are many software available to communicate data to higher level of managers to support
their decisions made for the benefit of the company. Software such as excel and SPSS are most
widely used to represent these data in the forms of graphs and charts and easily interpreted by the
higher levels of management to supports its decision making process.
It is important for the data analyst to present these data to the higher level of management
to support its decision making process (Burch, Burch and Batchelor, 2015). It also important to
present these data in a manner which can be easily interpreted by the managers to analyse the
trend and make necessary decisions Following are the various types of charts available to present
the data:
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Column, Bar and line charts in single set of data: This helps manager s to present data
give in a single set. The data which is being presented in these types of charts basically span a
number of year and time period. In this type of data charts are formed on the basis of two axis
with dependent variable showing on the y axis whereas the independent variable showing on the
x axis.
The above chart shows the total sales in a column chart which can be easily interpreted
by top level of management (ACCA GLOBAL 2018). As in the above chart shows an increasing
trend in the sales of a company. It is analysed by managers and take necessary decision to
increase the companies, sales.
Column, Line and bar chart for multiple data set: it is used by data analyst to present
the data for a multiple data set. In this type of charts analyst take multiple data set in order to
provide the numerical range of the data which is similar in nature. This type of chart help the
managers to easily find out the difference in various data set. It gives the clear picture of data
which is presented by the analyst to easily understand the raw data and take effective decisions to
improve its business efficiency.
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The above chart shows the two data set presented in a single chart it help managers to
easily understand the data and analyse the difference between two related data set (ACCA
GLOBAL 2018).
M4 Justification of reason for choosing the method of communication
It is important for a manager to properly choose the correct method of communicating the
data in the forms of charts and tables. There are various factors which affect the method of
communication of data such as discussed below:
 Nature of Message which is to be communicated
 Cost which company can incur
 Recording of communication i.e., oral or written
 Scale of organisation plays an important part in selection of method of
communication
CONCLUSION
From the above file it can be concluded that statistics plays an important part in the
process of decision making as it help managers to analyse the previous trend with the help of
data presented in a manner which can be easily interpreted by the managers and make effective
decisions. The above file also establishes the analysis of two different forms of data such as
quantitative and qualitative data and the various appropriate statistical methods and its
importance in decision making process. It also states the difference in the application of various
statistics. This report also concludes about the use of proper tables and charts in communication
of finding to higher level of management.
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REFERENCES
Books and Journals
Keller, G., 2015. Statistics for Management and Economics, Abbreviated. Cengage Learning.
Zyphur, M.J. and Oswald, F.L., 2013. Bayesian probability and statistics in management
research: A new horizon. Journal of Management, 39(1), pp.5-13.
Siegel, A., 2016. Practical business statistics. Academic Press.
Ross, J.E., 2017. Total quality management: Text, cases, and readings. Routledge.
Groebner, D. F., and et.al., 2013. Business statistics. Pearson Education UK.
Haltiwanger, J., and et.al., 2012. Business dynamics statistics briefing: Job creation, worker
churning, and wages at young businesses. Worker Churning, and Wages at Young
Businesses (November 1, 2012).
Burch, G. F., Burch, J. J., and Batchelor, J. H., 2015. An empirical investigation of the
conception focused curriculum: The importance of introducing undergraduate business
statistics students to the “real world”. Decision Sciences Journal of Innovative
Education. 13(3). pp.485-512.
Online
ACCA GLOBAL 2018 [Online] Available through:
<https://www.accaglobal.com/in/en/student/exam-support-resources/foundation-level-study-
resources/ma1/technical-articles1/effective-presentation.html>
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