Microsoft Excel Driving Tests Coursework

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DRIVING TESTS

INTRODUCTION

I will investigate how well 240 people perform in their driving test. There is a mixture of male and female drivers and there are four instructors that teach them. I will investigate how many minor mistakes these driver’s make in their test. The information I have been given consists of:

  • the driver’s gender
  • the number of one hour lessons they have received
  • the number of minor mistakes made during the test
  • the instructor who gave the driver lessons
  • the day they took the test
  • the time of day that they took the test

The software I will be using to store this information will be Microsoft Excel. I will house the information in spreadsheets and here I will be able to select random samples and sort my data.

I will also be using a program called Autograph. This program will enable me to draw graphs and do important calculations. Without this software it would take me a long time to draw out the diagrams I need for the investigation.

During the investigation I will be using sampling. This is where I will be taking a portion of the population to gather my results, instead of using all 240 people. This means that the data will be easier to handle and I will have less points to plot on my graphs but still keep accurate results.

HYPOTHESIS ONE – MALES ARE BETTER DRIVERS THAN FEMALES

For my first hypothesis I will be answering the question “Are male drivers better than females.” To do this I will record the number of minor mistakes made during the driving test.

I will take a random sample of 30 males and 30 females and compare the results. The reason for this is because if I did not take a sample then I would be handling 240 pieces of data, which is too much for this investigation. The sample must also be random so that my results take into account all the different drivers, instead of just the best or worst ones.

I took random samples for 30 males but some of my data was incomplete. I could not use these incomplete pieces of data so I used ones with complete data instead.


From these groups of information I constructed two histograms. These histograms would show me how many mistakes were the most popular. Using a frequency density histogram would enable me to see where the most mistakes were taking place and this would show me who were making the more mistakes, males or females.

Male Drivers

Female Drivers

In the female histogram my blocks are denser towards the right hand side. This means that the women make more mistakes than they do fewer mistakes. There is one very large block from 28 to 35 mistakes. This area contains the greatest amount of woman. In my data statistics it shows that the mean is 19.5 mistakes for woman.

In the male histogram we see the highest concentration of men in the low values. There are several blocks that are contained in the regions between 10 and 20. The mean value for males was 14.2, this shows us that the average number of mistakes for these 30 males was 14.2, 5.3 mistakes lower than the females.

Looking at both the histograms together (below) we can clearly see that females (yellow) were making more mistakes than men (green). The females were less concentrated in the lower values than males which show us that on average males are making less mistakes in their tests than females.

The box and whisker diagram below is very useful in showing us who is the better driver. The centre line(mean) in the middle of the box shows us the average number of mistakes for each gender. The males mean is lower than the females. This shows us that on average males make less mistakes in the driving tests than females. The whiskers in the diagram show us the range of results and here we also see that males are the better drivers. The females results spread over a wider range with their best result being 2 mistakes and their worst being 37, compared to the males best result of 1 mistake and worst of 31.

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This box and whisker diagram makes it clear who are the better drivers!

From my diagrams and graphs it is clear to see that the 30 random men are better drivers than the 30 random women. Choosing 30 people from each gender randomly gives me a good amount of information to draw accurate results. I am not dealing with too many pieces of information and I can draw a conclusion for this hypothesis.

It would have been interesting to see what my histograms would have looked like if I had used all 240 pieces of ...

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