dekker.one
Group work for a Monash Research Methods course
Create FIT4005_Wk9_Report
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wk9/FIT4005_Wk9_Report
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wk9/FIT4005_Wk9_Report
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/* NOTE! This has not been proofread */
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What we did (Method)
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Provided with a set of 132 unique records of the top 200 male tennis players, we sought to investigate the relationship between the height of particular individuals with their respective weights. We conducted basic statistical correlation analyses of the two variables with both Pearson's and Spearman's correlation coefficients to achieve this. Further, to understand the correlations more deeply, we carried out these correlation tests on the full population of cleaned data (removed duplicates etc), alongside several random samples and samples of ranking ranges within the top 200. To this end, we made use of Microsoft Excel tools and functions of the Python library SciPy.
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What we got (Results)
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We performed seperate statistical analyses on 10 different samples of the population, as well as the population itself. This included 5 separate subsets of the rankings (top 20 and 50, middle 20, bottom 20 and 50) and 5 seperate randomly chosen samples of 20 players.
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The results for the tests is as follows (all data is rounded to 5 decimal places):
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Test Set Pearson's Coefficient Spearman's Coefficient
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Population 0.77953 0.73925
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Top 20 0.80743 0.80345
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Middle 20 0.54134 0.36565
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Bottom 20 0.84046 0.88172
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Top 50 0.80072 0.78979
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Bottom 50 0.84237 0.81355
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Random set #1 0.84243 0.80237
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Random set #2 0.56564 0.58714
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Random set #3 0.59223 0.63662
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Random set #4 0.65091 0.58471
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Random set #5 0.86203 0.77832
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What this says (Discussion)
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The results generally indicate that there is a fairly strong positive correlation between the weight and weight of an individual tennis player, within the top 200 male players. The population maintains a strong positive correlation with both Pearson's and Spearman's correlation coefficients, indicating that a relationship may exist. Our population samples show promising consistency with this, with 6 seperate samples having values above 0.6 with both techniques. The sample taken from the middle 20 players, however, shows a relatively weaker correlation compared with the top 20 and middle 20, which provides some insight into the distribution of the strongest correlated heights and weights amongst the rankings. All five random samples of 20 taken from the population indicate however that there does appear to be a consistent trend through the population, which corresponds accurately with the coefficients on the general population.