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Summary MANCOSA Advanced business statistics Guide
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MANCOSAADVANCED BUSINESS STATISTICS
Peter Osei-Sekyere
Contacts:
Email: posechere@gmail.com
076 200 0790
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,1. INTRODUCTION
IMPORTANT COMMENTS!
In the 21st century, countries, markets and organisations are increasingly
subjected to high levels of Volatility, Uncertainty, Complexity, Ambiguity, and
Disruptions (VUCAD). Therefore Business-related issues are not as simple as
they may appear at first. Managers must dig deep to ascertain the root causes of
problems. This requires some form of statistical knowledge!
Business Management is a science; thus, managers whose responsibility it is
to organise, measure, plan, decide and control various activities and tasks in the
organisations must do so based on sound scientific evidence.
This requires some form of statistical knowledge!
Fundamentally, businesses and organisations exist to solve one problem or
another. Effective Problem solving and Decision making requires some form of
statistical knowledge!
Reliance on so-called COMMON SENSE can be costly! Get it right first time.
Popular Management Refrain:
YOU CANNOT MANAGE WHAT YOU CANNOT MEASURE
What is STATISTICS?
STATISTICS is the science of collecting, organising, presenting, analysing and
interpreting data to assist in making more effective decisions (Lind, Marchal and Mason,
2002:3). [“Mathematics applied to data" (Fisher, 1925)].
The 21st century is the era of BIG DATA!!!
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, Data is everywhere: there is an incredible explosion in the amount of data, the
velocity of the data, the types of data and the sources of data. There is real-time
data from mobile devices, social networks, data sensors in machines and many
other sources.
Data is incredible important in making informed operational and strategic
decisions. Statistical techniques help us to make data meaningful.
No matter what your future line of work will entail, you will be called upon to make
decisions and judgements that involve data.
The implication is that 21st century managers, in particular those involve in IT, must be
competent in basic data analytics (i.e., have data-literacy).
2 Types of Statistics:
Descriptive Statistics: Methods of organising, summarising, and presenting data as
information. E.g., frequency distribution, charts, mean, median, mode (statistic= metric).
Inferential Statistics (Inductive Statistics): Methods used to determine metrics about
a POPULATION on the basis of data from a SAMPLE.
POPULATION: In stats, population does not necessarily refer to people, but can
include:
individuals (e.g., number of students at MANCOSA, all the students reading
MBA at GIBS, or all the patients seen at Bara Hospital in 2013; the number of
individual investors who trade in JSE-listed equities),
objects (e.g., all books in the MANCOSA library, all Toyota corollas produced at
Durban plant) (see Figure 1),
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, events (all matches of the FIFA 2010 World Cup, all matric exams written in
2013),
incidents (all road accidents in 2013, all new HIV infections in RSA in 2012), or
measurements (e.g., the weights of all boxers in the Johannesburg boxing club,
all imports into RSA during 2013).
SAMPLE: A portion, or part, of the population being studied. The process of selecting a
representative part of a population is called sampling. The findings of a research can
only be generalised and interpolated to the target population if only the selected sample
is representative of the entire population.
Sampling methods are either probabilistic or non-probabilistic. Five main techniques
are normally used to select a probability sample: simple random, systematic, stratified
random, cluster and multi-stage (Saunders et al., 2009:222).
A PARAMETER is a characteristic of a population; a STATISTIC is a characteristic of a
sample.
Figure 1, below is an illustration of the relationship between a population and a sample.
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