Essay statistics coursework

If the line at the top of the TV hours graph is blanked out the two graphs look almost identical. 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.

Hypothesis 1 Females Again Essay statistics coursework will start with the scatter graphs. I know that it can only be 1 or 2 anomalous because the point where it goes flat is at about 38 and there are only 39 sets of data in the graph. Although the Essay statistics coursework graphs did show a a negative correlation it was proved to be made by a few anomalous results by the cumulative frequency and later the inconsistency with the female sample.

There is no correlation between the 2 sets of data. This graph showed the data much clearer and I could then start analyzing it. 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. Cumulative frequency graphs for the number of TV hours watched and Weights of males: This similarity is the reason why the scatter graph had no correlation and therefore no relationship.

This means that my hypothesis is wrong. Although there is 1 area where the data is concentrated and the gradient very steep, between These anomalous results on the TV hours graph are what caused the slight negative correlation on the trend line.

Statistics Coursework

But for the scatter graphs I cancelled them all out which gave no correlation. In this it may be that my hypothesis is incorrect. Second male scatter graph: This means that it is unlikely that there is a relationship between IQ and Average number of TV hours watched per week.

The second scatter graph in this section, without the anomalous result completely changed the trend line. To do this I will use random sampling. Hypothesis 2 Males In this hypothesis I will be comparing the Average number of TV hours watched per week and Weight, to see if there is any relationship between them.

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.

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I will again start with Males and the Scatter graphs. Box plots of cumulative frequency graphs for Number of TV hours watched and weights of females: On the graph without the anomalous result there is clearly no correlation whatsoever as the line is nearly horizontal. I think that there will be a relationship between them and will attempt to reveal it.

There is almost a straight line near the top of the graph; this shows that there is likely to be some anomalous results and 0 pupils in between that result and the main bulk. This means that as the number of TV hours goes up Weight goes down.

The box plots for these graphs will look quite different and will make it easy to make a simple comparison. For my hypothesis to have been correct there would have needed to be a strong positive correlation.

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.

From the box plots I can see that the two sets of data are almost identical in range which would cause a straight line on the scatter graph it is because of the anomalous results on the TV hours which caused the slight negative correlation.

Now I will look at the cumulative frequency graphs to see what results I get from them.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.

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