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CIMA BA2 Notes Chapter 11- Risk 1 – Summarising and analysing data
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CIMA BA2 Notes Chapter 11- Risk 1 – Summarising and analysing data
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BA 2 - Fundamentals of Management AccountingRisk 1 – Summarising and analysing
data
Chapter eleven
, 1
BA 2 - Risk 1 – Summarising and analysing data. Quantia Learning
, 2
Introduction to risk and uncertainty
Risk is an important concept in decision
making. Most decisions involve looking to the
future and estimating future costs or benefits
and these estimates will inevitably involve
uncertainties and assumptions.
For example, a company may be considering
investing in upgrading its production machinery
in order to increase its production capacity and
produce a new range of products. The level of
perceived risk involved in this investment will
have an impact on the return required from the
investment. The higher the risk, the higher the
return required to compensate for the level of
risk.
Consideration of risk is therefore important. We have to be able to reflect this risk and
uncertainty in our financial evaluations relating to these decisions.
Risk is 'condition in which there exists a quantifiable dispersion in the possible outcomes from
any activity' (CIMA Terminology).
The term 'uncertainty' is used when we do not know the possible outcomes and/or their
associated probabilities. The future cannot be predicted because there is insufficient information
about what the future outcomes might be. Decisions under conditions of uncertainty are often a
matter of guesswork.
Uncertainty is 'inability to predict the outcome from an activity due to lack of information about
the required input/output relationships or about the environment within which the activity takes
place' (CIMA Terminology).
The difference between these is therefore that risk is quantifiable while uncertainty is
unquantifiable.
BA 2 - Risk 1 – Summarising and analysing data. Quantia Learning
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Risk Uncertainty
There are a number of possible outcomes There are a number of possible outcomes
and the probability of each outcome is but the probability of each outcome is not
known. known.
For example, based on past experience of For example, the same oil company may
digging for oil in a particular area, an oil dig for oil in a previously unexplored area.
company may be able to estimate the The company knows that it is possible for
probability (% chance) of finding oil. them to either find or not find oil but it does
not know the probabilities of each of these
outcomes.
Good information makes good decisions. In order for data to become information, we must be
able to summarise and analyse it. Management accountants must be able to analyse a large
amount of data so that it becomes meaningful and useful information.
Mathematical techniques which can be used to analyse data.
1. Tabulating data (Tallying/Frequency distributions)
2. Charts and diagrams (Pie charts/Bar charts/Histograms and ogives)
3. Averaging (Arithmetic mean/Median/Mode)
4. Measures of spread (Range/Variance/Standard deviation/Coefficient of variation).
Tabulating data
This is a good technique which helps us present raw data in a way which makes it easier to
understand and therefore more useful for decision making.
Take a look at the following information of a florist, HoneyBee, monitoring the efficiency of
employees completing online order by analyzing the number of invoices left unprocessed at the
end of the day.
At the end of the first period of this check (26 working days), he has collected the following data:
BA 2 - Risk 1 – Summarising and analysing data. Quantia Learning
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