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% Document Info
\author{Phoebe Zhang (Phoebe12)}
\pdfinfo{
  /Title (8f-statistics-test.pdf)
  /Creator (Cheatography)
  /Author (Phoebe Zhang (Phoebe12))
  /Subject (8F Statistics Test Cheat Sheet)
}

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\noindent
\begin{multicols}{3}
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    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{8F Statistics Test Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Phoebe Zhang (Phoebe12)} via \textcolor{DarkBackground}{\uline{cheatography.com/30133/cs/11885/}}}
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\noindent
\begin{multicols}{3}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}Phoebe Zhang (Phoebe12) \\
  \uline{cheatography.com/phoebe12} \\
  \end{tabulary}
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 22nd May, 2017.\\
   Updated 22nd May, 2017.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Sponsor}}  \\
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  \vspace{-5pt}
  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Primary and secondary data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{Primary data collection involves collecting data yourself. This means that you have ownership of the data, and no one else has access to the data until it is released or published.} \tn 
% Row Count 4 (+ 4)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{5.377cm}}{Secondary data are data that have been collected by someone else. They often provide data which would not be possible for an individual to collect. The data can be qualitative or quantitative. The accuracy and reliability of the data sometimes needs to be questioned, depending on its source. The age of the data should always be considered.} \tn 
% Row Count 11 (+ 7)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Measures of centre}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{The {\bf{mean}} or average of a set of scores is the sum of all scores divided by the number of scores.} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{5.377cm}}{Mean = total of all scores ÷ number of scores} \tn 
% Row Count 3 (+ 1)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{The {\bf{median}} is the middle score for an odd number of scores and the average of the two middle scores for an even number of scores.} \tn 
% Row Count 6 (+ 3)
% Row 3
\SetRowColor{white}
\mymulticolumn{2}{x{5.377cm}}{Alternatively, if a set of data contains n scores, the median is given by the ((n + 1) ÷ 2)th score.} \tn 
% Row Count 9 (+ 3)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{The {\bf{mode}} is the most common score in a set of data. It is the score with the highest frequency. It measures clustering of scores. Some sets of scores have more than one mode or no mode at all. There is no mode when all values occur an equal number of times.} \tn 
% Row Count 15 (+ 6)
% Row 5
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\mymulticolumn{2}{x{5.377cm}}{Having two modes is called "bimodal".  Having more than two modes is called "multimodal".} \tn 
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\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Measures of spread}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{The {\bf{range}} of a set of scores is the difference between the highest and lowest scores.} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{5.377cm}}{A symmetrical graph is a {\bf{normal distribution}}. A graph that is gathered to one end of the distribution is {\bf{skewed.}} A graph can be positively skewed or negatively skewed.} \tn 
% Row Count 6 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
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\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Samples and populations}}  \tn
% Row 0
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\mymulticolumn{2}{x{5.377cm}}{A survey is the process of collecting data. If every member of a target population is surveyed, the process is called a {\bf{census}}.} \tn 
% Row Count 3 (+ 3)
% Row 1
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\mymulticolumn{2}{x{5.377cm}}{Due to limitations in time, cost and practicality, in many cases a {\bf{sample}} of the population is selected at random to prevent biased results. Sample sizes should be about the square root of the population.} \tn 
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% Row 2
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\mymulticolumn{2}{x{5.377cm}}{Questions can be open or closed. Open questions are those where the respondent has no guided boundries within which to answer. The main problem with open questions is that their answers are often difficult to classify and analyse.Closed questions are the type where the respondent must answer within a category. These types of answers are easier to analyse than answers to open questions.} \tn 
% Row Count 16 (+ 8)
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\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Percentiles}}  \tn
% Row 0
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\mymulticolumn{2}{x{5.377cm}}{Percentile: the value below which a percentage of data falls.} \tn 
% Row Count 2 (+ 2)
% Row 1
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\mymulticolumn{2}{x{5.377cm}}{Deciles are similar to Percentiles (sounds like decimal and percentile together), as they split the data into 10\% groups.} \tn 
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% Row 2
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\mymulticolumn{2}{x{5.377cm}}{Another related idea is Quartiles, which splits the data into quarters.} \tn 
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\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Quartiles}}  \tn
% Row 0
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\mymulticolumn{2}{x{5.377cm}}{Quartiles are the values that divide a list of numbers into quarters (3 cuts):} \tn 
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% Row 1
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\mymulticolumn{2}{x{5.377cm}}{Put the list of numbers in order} \tn 
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% Row 2
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\mymulticolumn{2}{x{5.377cm}}{Then cut the list into four equal parts} \tn 
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% Row 3
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\mymulticolumn{2}{x{5.377cm}}{The Quartiles are at the "cuts"} \tn 
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% Row 4
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\mymulticolumn{2}{x{5.377cm}}{Sometimes a "cut" is between two numbers ... the Quartile is the average of the two numbers.} \tn 
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% Row 5
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\mymulticolumn{2}{x{5.377cm}}{The "Interquartile Range" is from Q1 to Q3. To calculate it just subtract Quartile 1 from Quartile 3.} \tn 
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\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Organising and displaying data}}  \tn
% Row 0
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\mymulticolumn{2}{x{5.377cm}}{Organising raw data into a frequency table is the first step in allowing us to see trends in data. Sometimes there is too much data to treat as single entries, and it is necessary to group the data into {\bf{class intervals}}. The choice for the size of the class intervals should lead to between 5 and 10 groups being formed. Class intervals are set so that each score belongs to one group only.} \tn 
% Row Count 8 (+ 8)
% Row 1
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\mymulticolumn{2}{x{5.377cm}}{Once a frequency table has been constructed from the data, it can be diplayed in graphical form. The most important statistical displays are column graphs. A special type of column graph is called a {\bf{histogram}}.} \tn 
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% Row 2
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\mymulticolumn{2}{x{5.377cm}}{If we join the midpoints of the tops of the columns of a histogram, then extend the ends to the x-axis, we form what is called a {\bf{frequency polygon}}.} \tn 
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\end{tabularx}
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\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Types of data}}  \tn
% Row 0
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\mymulticolumn{2}{x{5.377cm}}{Data can be qualitative or quantitative. Qualitative data is descriptive information (it describes something) Quantitative data, is numerical information (numbers).} \tn 
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% Row 1
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\mymulticolumn{2}{x{5.377cm}}{And Quantitative data can also be Discrete or Continuous:} \tn 
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% Row 2
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\mymulticolumn{2}{x{5.377cm}}{Discrete data can only take certain values (like whole numbers)} \tn 
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\mymulticolumn{2}{x{5.377cm}}{Continuous data can take any value (within a range)} \tn 
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\mymulticolumn{2}{x{5.377cm}}{ut simply: Discrete data is counted, Continuous data is measured} \tn 
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\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{To help you remember think "Quantitative is about Quantity"}  \tn 
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\begin{tabularx}{5.377cm}{p{0.4977 cm} p{0.4977 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Grouped data}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{You can't calculate the mean, mode or median using grouped data. However, you can make estimates using the midpoints of each class. he midpoints are in the middle of each class.} \tn 
% Row Count 4 (+ 4)
% Row 1
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\mymulticolumn{2}{x{5.377cm}}{Using midpoints, you can calculate the modal group, median group and mean group.} \tn 
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\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

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