Statistics for Management Report: HND Business, 2019-2020

This report provides a comprehensive overview of statistics for management. It begins with an introduction to the subject, defining statistics and outlining its key characteristics. The report then delves into various statistical methods, types and sources of data, and the difference between a sample and a population. The value of statistical methods in a business context is highlighted, with a specific focus on a case study involving Samsung. The report further differentiates between descriptive and inferential statistics, and presents key findings from statistical analyses, concluding with references. The report emphasizes the importance of statistical techniques for informed decision-making in business, using Samsung as a case study.

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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