Contents
Unit 6: Business Decision Making
Module objectives
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Sources for data collection
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Using business models to help make decisions
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Suitable formats for decision making in a business context
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Software generated information for decision making in business
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Module 2: Business Decision Making

Techniques to effectively analyse data for business purposes - Calculation

 

Calculation

You can also use quartiles, percentiles and correlation coefficients to analyse data.

Quartiles

 

Quartiles divide the data into 4 sections. The quartiles are called Q1, Q2, Q3 and Q4.

For the data below:

2, 5, 9, 7, 2, 4, 1, 5, 8, 10, 2, 8

The total is 12 so the size of the quartiles will be 12 ÷ 4 = 3

 

1 2 2 2 4 5 5 7 8 8 9 10

 

 

The 1st quartile is the 3rd number which is 2, the 2nd quartile is 5, the third quartile is 8 and the 4th quartile is 10.

Percentiles

 

Percentiles are very similar to quartiles but divide the data into 100 equal parts.

Correlation coefficient

 

This shows how strong a relationship exists between two variables. A positive correlation (correlation coefficient 0 to 1) occurs when an increase/decrease in one causes a corresponding increase/decrease in the other, e.g. higher sales following higher marketing expenditure. A negative correlation (correlation coefficient between 0 and -1) occurs if the increase/decrease in one variable is caused by an opposite decrease/increase in the other, e.g. sales of gloves rising as the temperature decreases.

It is also possible to have a zero correlation (correlation coefficient 0) where the change in one variable has no effect on the other, e.g. the effect of raising the price of cans of Coke on tennis ball sales.

Pause for thought

Can you think of examples of variables that are likely to exhibit positive, negative and zero correlation?