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Update title and add initial text for week 9

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wk7/week7.tex
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@ -13,7 +13,7 @@
\usepackage{bookmark}
\begin{document}
\title{Evidence and experiments}
\title{Week 7 - Evidence and experiments}
\author{
Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
Silvestere

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wk8/week8.tex
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@ -14,7 +14,7 @@
\usepackage{bookmark}
\begin{document}
\title{Evidence and experiments}
\title{Week 8 - Quantitative data analysis}
\author{
Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
Silvestere

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wk9/week9.tex
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@ -14,7 +14,7 @@
\usepackage{bookmark}
\begin{document}
\title{Evidence and experiments}
\title{Week 9 - Correlation and Regression}
\author{
Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony
Silvestere
@ -24,9 +24,62 @@
\section{Introduction} \label{sec:introduction}
\section{Method} \label{sec:method}
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.
\section{Results} \label{sec:results}
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.
\\ \\
\Cref{tab:excel-results} shows the the results for the conducted tests.
\begin{table}[ht]
\centering
\begin{tabular}{|l|r|r|}
\hline
\textbf{Test Set} & \textbf{Pearson's Coefficient} & \textbf{Spearman's Coefficient} \\
\hline
\textbf{Population} & 0.77953 & 0.73925 \\
\textbf{Top 20} & 0.80743 & 0.80345 \\
\textbf{Middle 20} & 0.54134 & 0.36565 \\
\textbf{Bottom 20} & 0.84046 & 0.88172 \\
\textbf{Top 50} & 0.80072 & 0.78979 \\
\textbf{Bottom 50} & 0.84237 & 0.81355 \\
\textbf{Random Set \#1} & 0.84243 & 0.80237 \\
\textbf{Random Set \#2} & 0.56564 & 0.58714 \\
\textbf{Random Set \#3} & 0.59223 & 0.63662 \\
\textbf{Random Set \#4} & 0.65091 & 0.58471 \\
\textbf{Random Set \#5} & 0.86203 & 0.77832
\\ \hline
\end{tabular}
\caption{TODO: Insert better caption for this table. All data is rounded to 5 decimal
places}
\label{tab:excel-results}
\end{table}
\section{Discussion} \label{sec:discussion}
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.
\end{document}