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Driving Tests

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Driving Tests

Histogram analysis:

Statistics for male minor mistakes made     Statistics for female minor mistakes made

Grouped Data Statistics:Grouped Data Statistics:

Total Frequency, n:        40Total Frequency, n:        40

Mean, x:                13.125Mean, x:                13.4375

Standard Deviation, x:        8.28685Standard Deviation, x:        7.36732

Modal Class:                0-Modal Class:                10-

Lower Quartile:                6.25Lower Quartile:                8.33333

Median:                12.5Median:                13.0769

Upper Quartile:                18.3333Upper Quartile:                17.5

Semi I.Q. Range:                6.04167Semi I.Q. Range:                4.58333


My histogram displaying the number of minor mistakes made by my male sample shows quite an obvious pattern.  The first three columns are all very tall, this would imply that the majority of males in my sample completed their driving test with less than half of the possible amount of minor mistakes awarded.  Although it is difficult to say whether this majority of drivers in the first three columns made mistakes at the bottom or the top end of the described range.  This difficulty is also present if any column on its own is taken into consideration.  Starting with a tall start the histogram displays a sharp drop after the third column.  Histogram displays well that there is a large difference between the majority and the minority.  There is a large majority in the first three columns.  And so the histogram shows that there were very few male drivers that were obtaining a high number of minor mistakes.  My frequency polygon also backs this up.


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Driving Tests

Statistical Comparison:


The mean displayed for both genders in the above statistics are very close.  But the male mean is still smaller at 13.125 to the females 13.4375.  The smaller mean held by the male sample signifies that they as a sample produced a smaller average amount of minor mistakes than the female sample.

Standard deviation:

The standard deviation helps one to know how a set of data clusters or distributes around its mean.  

In the male case the standard deviation is 8.28685 this means that the majority of the results in the box plot and histogram fluctuate around the mean of 13.125 plus or minus 8.28685.  In the women’s case the standard deviation is 7.36732 and therefore fluctuates around their mean of 13.4375 plus or minus 7.36732.

This means that the majority of the male results are found between (13.125 – 8.28685, 13.125 + 8.28685) 4.83815 and 21.41185.

In the female case the majority of the female results are found between (13.4375 – 7.36732, 13.4375 + 7.36732) 6.07018 and 20.80482.

We cannot solely rely on the standard deviation to determine a conclusion as the results are quite vague, but we can see that the male lower boundary is lower than the females and therefore the majority of their results could be lower than the females.  On the females argument we can see that their upper boundary is lower than that of

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Statistical comparison:

The majority of the various statistical measures described are in favour of the males being the ones that made fewer mistakes.  Although there are one or two statistics such as the upper quartile that would reveal that the females made fewer mistakes.


From the results acquired from hypothesis one, I believe at the moment that the males are the better drivers and do make less mistakes because the large majority of diagrams and statistics so far would suggest that way, but we do not know for sure.  The females are favoured on a few occasions, but I have noticed that the values deciding who is favoured have been extremely close, and so this maybe due to rogue results or similar happenings or other factors which we have not investigated.  Most of the information points towards the males as being the better drivers than the females and so to answer the hypothesis I would say that males are better drivers than the females.  Still it is uncertain and I must continue my investigation to collect results from other factors that would help me to affirm an evidential conclusion.

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