Embedded Files
--PRESENTATION OF DATA--
--PRESENTATION OF DATA--
[BAR CHARTS]
[BAR CHARTS]
[PIE CHARTS]
[PIE CHARTS]
[INFOGRAPHICS]
[INFOGRAPHICS]
--ANALYSING DATA: 'AVERAGES'--
--ANALYSING DATA: 'AVERAGES'--
[MEAN]
[MEAN]
METHOD: Add all results and divide by the number of results.
USES: Can
ADVANTAGES: Includes ALL the data, widely used...
DISADVANTAGES: Affected by EXTREMES.
[MODE]
[MODE]
METHOD: Most frequently occurring result.
USES: Can
ADVANTAGES: Easy to observe, no calculations, easy to remember...
DISADVANTAGES: Ignores some data, can have more than one...
[MEDIAN]
[MEDIAN]
METHOD: Middle result when data is put in order.
USES: Wage negotiations...
ADVANTAGES: Extremes have minimal impact, so better than mean.
DISADVANTAGES: Have to approx if even number of results.
--ANALYSING DATA: 'SPREAD'--
--ANALYSING DATA: 'SPREAD'--
--WHAT IS SPREAD?--
--WHAT IS SPREAD?--
SPREAD refers to the degree to which the data is 'DISPERESED' from the MEAN VALUE (‘middle point’).
Clearly knowing how spread results are helps determine which range is most occurring which in a business can help in many ways such as when it comes to ORDERING STOCK or RAW MATERIALS, or for TARGETING ADVERTISING TO A CERTAIN DEMOGRAPHIC.
[METHOD #1 QUARTILES]
[METHOD #1 QUARTILES]
QUARTILES are a measure that PUTS NUMERICAL DATA IN ORDER AND SPLITS THEM INTO FOUR PARTS.
The INTER-QUARTILE RANGE is the DIFFERENCE BETWEEN THE 3RD AND THE 1ST QUARTILE, which in the example below is 51 - 26.5 = 24.5, also the MEDIAN VALUE is the MIDPOINT BETWEEN where the 2ND QUARTILE ENDS and the 3RD QUARTILE BEGINS, which = 36.
This data can be used to inform managers like this:
"25% of the time they will sell between 24 and 26 cars"
"50% of the time they will sell more than 36 cars"
and so on...
--QUANTILE TASK--
--QUANTILE TASK--
Create quartiles using the student assessment data below and find the median grade.
[METHOD #2 STANDARD DEVIATION]
[METHOD #2 STANDARD DEVIATION]
STANDARD DEVIATION (SD) indicates the SPREAD OF DATA AROUND THE 'MEAN AVERAGE' or in other words the AVERAGE DIFFERENCE FROM THE MEAN DATA POINT.
A LOW STANDARD DEVIATION indicates that data points are generally CLOSE TO THE MEAN (or the average value). A HIGH STANDARD DEVIATION indicates GREATER VARIABILITY in data points, (or higher dispersion from the mean.)
We do not need to concern ourselves with the mathematical process involved in calculating the SD value (Variance etc) but we do need to know how to interpret the results.
For example below we can see the distribution of coffees bought per month. The mean number of coffees bought is 50 per month, and the average difference from this mean result (the SD) is 10.
The special feature of a normal curve is that the proportion of results within 1, 2 or 3 SDs of the mean will always be the same, no matter what is being measured. So, in the example in Figure 25.5, 68% of consumers bought 40–60 coffees each month, 1 SD either side of the mean.
If we want to know what proportion of consumers bought more than 70 coffees per month (perhaps to calculate the cost of a discount scheme for those who consume a lot of coffee!) the graph shows us how to do that. The graph makes it clear that 95% of consumers bought between 30 and 70 coffees, so 5% bought either less than 30 or more than 70. As the curve is symmetrical, 2.5% bought more than 70 coffees so the discount scheme will be costed on the basis of only 2.5% of consumers being eligible for it.
https://www.calculatorsoup.com/calculators/statistics/standard-deviation-calculator.php
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