Statistics for Management

This presentation provides an overview of statistics for management, covering topics such as the definition of statistics, key characteristics, overview of statistical methods, types of data, sources of data, and the value of statistical methods in decision-making. It also includes key findings and references. Suitable for students studying management or related subjects.

Statistics for
Management

Cover Content
 Introduction
 Definition of Statistics
 Key characteristics of Statistics
 Overview of statistical methods
 Types of data
 Sources of data
 Difference between a sample and a population
 Value of statistical methods

Cover Content
 Difference between Differential and Inferential Statistics
 Key findings
 References

INTRODUCTION
Statistics applies to a data gathering, structure, study and understanding
in numerical research. It offers a range of methods for modelling and
forecasting organizational data, which helps to make decisions about
accounting, advertising as well as other services. The current document
will gather in-depth quantitative management understanding, such as its
use to solve company difficulties. Samsung is selected as a developer,
supplier and retailer in the UK for such a research, which deals with
electronics.

Definition of Statistics
This includes various sources and information forms, allowing
businesses to track rivals' successful and unsuccessful results in
order to develop methods for enhancing their own output. To this
end, the importance of using statistical methods is also addressed
in this document in order to meet the respective company's
business goals. In addition, important distinctions among
empirical and inferential statistics are illustrated, with
consequences for business goals.

Key characteristics of Statistics
Stats are composite of truths: Information gathered by statistical
techniques rarely contains individual or independent numbers,
since it is difficult to compare these data, to draw a particular
conclusion.
Statistical data should be obtained in a logical way: the data
must always be compiled routinely, with an acceptable method,
to take financial judgments or make a sensible conclusion.

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Data should be represented in quantitative sort: even data
presented mathematically can be used as statistical techniques.
Thus subjective information such as intellect, attractiveness,
including dismissive knowledge which could not be represented in
quantitative type should not be used on same.

Overview of statistical methods
Mean: this approach makes it easier to identify a general trend of
a set of data in evaluating the estimated value of broad data.
Hypothesis testing: This process, also recognized as probability
testing that is used to check if a theory is valid for a set of data,
otherwise it will consider alternative hypothesis. Unless the
meaning quality is lower than that of the predicted meaning,
otherwise the null hypothesis must be dismissed.

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Standard deviations: It is applicable specifically to information
that is widely distributed for average, that allows to quickly
determine the distribution of datasets.
Sample size determination: It is hard to obtain reliable results if
calculating significant population data such as business data
about revenue, client base and much more. Thus, taking a test of
information with both the correct size is important in order to
achieve an accurate number.

Types of data
Primary data: This contains qualitative raw business
information, i.e. non-manufacturing data that can be gathered
from sources such as survey, questionnaire, and much more.
Primary information is 'genuine' in the context that they had not
undergone quantitative activities and are authentic
Secondary data: This comprises of data that might be subject to
empirical analysis and it can be obtained by governmental and
semi-governmental organization, newspapers as well as further
media sources.

Sources of data
Survey: This tool is also considered a survey sample used only to
measure the features of high population information. Using that
same approach allows the company to easily determine the
present and past pattern, but it may also trigger mistake. Samsung
could use this source to gain input on both the product quality
and products from consumers and other shareholders in order to
make more improvements

Census: An evaluation of data is focused for each and every community
object. The benefit of using such an approach is that data becomes more
relevant. It does, however, entail high costs and time to process and
process data.
Government and Semi-government sources: Such outlets include
information on an organization's local and national company
performance, making it easy to accomplish a goal through collection of
data.
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Difference between a sample and a population
A community contains whole items from a specific information set
about that measures such as average, mean and method are used to infer
desired outcome. But if a community comprises of large amounts of
data, then it is difficult to apply these techniques, as in such cases the
probability of failure is high. While test means larger data volume that is
extracted from either a larger population and contains each item that
needs results to be obtained. Applying statistical techniques to the test
provides the advantage of measuring more reliable results to slight or
zero errors.

Value of statistical methods
In order to accomplish highly competitive strengths in digital
industry, where rivalry is strong, it is important that Samsung gather
data about opponents ' last five years of success, their sales, and much
more. Notifying a director focused on teacher performance
improvement is one of the functions of metrics in industry. Most
businesses often obtain around - the-job information on employee
satisfaction and morale, which can be monitored not just to keep
workers engaged but to also insure that they do not move elsewhere
certain roles.

Difference between Descriptive and Inferential
Statistics
Basis of comparison Descriptive statistics Inferential statistics
Meaning This section of the data is
primarily concerned to describing
the population in which it is
possible to achieve information.
This involves making the
conclusions based on the analysis
of the test.
Usage in business This helps to describe history and
to predict a business' condition.
It explains the chance of getting
success or meeting failure of
business in an organisation
Forms of result It describes the probability of
performance or business loss in
an enterprise This approach
depicts visual data such as maps,
graphs and charts.
The results provides a possibility
of happening events.
How it works The assessment of data mainly
used by using secondary
This helps in tending the curve
towards possibility of events and

Key findings
Microsoft: SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.9728245071
R Square 0.9463875217
Adjusted R Square 0.9454631686
Standard Error 6.0702620615
Observations 60

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Apple: SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.9675382273
R Square 0.9361302212
Adjusted R Square 0.9350290181
Standard Error 9.8209074442
Observations 60

References
Siva, V. and et. al., 2016. The support of Quality Management to
sustainable development: A literature review. Journal of
Cleaner Production. 138. pp.148-157.
Kreps, D. M., 2019. Microeconomics for managers. Princeton
University Press.
Martins, J. and Isouard, G., 2015. An evidence-based framework:
competencies and skills for managers in Australian health
services. Asia Pacific Journal of Health Management.
10(2). p.8.