This may not be an accurate graph as there are a few anomalous results that may have caused the trend line to be that gradient. I will now crate cumulative frequency graphs to see if they can help me to draw conclusions.

On the graph without the anomalous result there is clearly no correlation whatsoever as the line is nearly horizontal. As you can see on both the graphs there is no correlation between the two sets of data.

The weights box plot shows me that the data is quite evenly spread in the middle of the range apart from a very heavy person at the end which is why the highest figure is so far apart from the upper quartile.

Now I will look at the cumulative frequency graphs to see what results I get from them. Again I had an anomalous result and had to create a second scatter graph without it there. For my hypothesis to have been correct there would have needed to be a strong positive correlation.

Both the male and female samples showed that my hypothesis was incorrect although some anomalous results created a slight negative correlation in both it was obvious that it was still wrong.

There is only a very slight gradient on the trendline that leans towards a negative correlation, but the gradient is not steep enough to draw any conclusions about the relationship between the two sets of data. The box plots for these to graphs will look alike apart from there will be a much longer line at the end of the TV hours graph because of the anomalous results.

This is what caused the horizontal line in my scatter graphs and proves my hypothesis.

This way it would help me to analyze the rest of the data. This means that as the number of TV hours goes up Weight goes down. My second hypothesis was proved correct.

I will now look at the box plots to compare the two cumulative frequency graphs. This means that my hypothesis is wrong.

The TV hours graph is much smoother and the data less spread. I could have made the cumulative frequency graphs a little better as the program I used did not put a scale on the x axis but only the length of the range. Box plots for cumulative frequency graphs of IQ and average number of TV hours watched per week: I think that there will not be any major relationship between as they will not affect Essay statistics coursework other greatly.

There is no correlation between the 2 sets of data. This is why the scatter graph got a near horizontal trend line. These box plots show me the same as the males did, that the data is almost identical if placed 1 on top of the other.

There may be a few exceptions as Essay statistics coursework pupil is likey to have a very low IQ which is why the lowest value is so low. In graph 1 there is a slight gradient on the graph which points towards a negative correlation, like those of the male sample.

Statistics Coursework 1st Hypothesis — For my first hypothesis I will investigate the relationship between the number of TV hours watched per week by the pupils against their IQ. I have created scatter graphs to show the relationship if the two data sources for my first hypothesis.

I will now analyze the graphs before drawing box plots to compare the graphs. Hypothesis 2 Females Again I will start with the scatter graphs to show the relationship between Number of TV hours watched and weight.

I will be able to make complete conclusions after looking at the female sample and seeing if that graph follows suit. I will select a number of pupils to base my data on and will use random sampling to ascertain the correct number of male and female pupils needed to make the investigation fair.

I will present my analysis and the results in graphs and tables and explain the results using the correlation of the graphs and arrangements of the figures. First male scatter graph: These anomalous results on the TV hours graph are what caused the slight negative correlation on the trend line.

The similarities on the cumulative frequency graphs and box plots further proved my hypothesis was correct. Second male scatter graph: The female scatter graph showed a near horizontal trend line which was what I needed to prove my hypothesis. This means that it is unlikely that there is a relationship between IQ and Average number of TV hours watched per week.The Statistics coursework is one of the most popular assignments among students' documents.

If you are stuck with writing or missing ideas, scroll down and find inspiration in the best samples. Statistics coursework is quite a rare and popular topic for writing an essay, but it certainly is in our database.

Statistics coursework payment and essay writing with hire white paper ghostwriters Paper presented at the appropriate methodology for writing a application letter the purposes is information science, icis pp.

Fantasy Football – Maths Coursework – Statistics Essay - Fantasy Football Â– Maths Coursework Â– Statistics My coursework is based on the game Â‘Fantasy FootballÂ’ which is ran by the British newspaper called Â‘The SunÂ’.

Fantasy Football is a competition based on building your own Â‘dream teamÂ’ and collecting. Statistics Coursework 1st Hypothesis – For my first hypothesis I will investigate the relationship between the number of TV hours watched per week by the pupils against their IQ.

I am going to use the columns “IQ” and “Average number of hours TV watched per week” taken from the Mayfield high datasheet. Database of FREE Statistics essays - We have thousands of free essays across a wide range of subject areas.

Sample Statistics essays! Free Essay: Statistics Coursework Introduction ===== The data that we are going to use is secondary data that has been collected from a high school.

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