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\author{Missurk}
\pdfinfo{
  /Title (anth-485-final-exam.pdf)
  /Creator (Cheatography)
  /Author (Missurk)
  /Subject (Anth 485 Final Exam Cheat Sheet)
}

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\fancyhead[L]{
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
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    \vspace{-7pt}
    {\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{Anth 485 Final Exam Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Missurk} via \textcolor{DarkBackground}{\uline{cheatography.com/50689/cs/13966/}}}
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\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}Missurk \\
  \uline{cheatography.com/missurk} \\
  \end{tabulary}
\vfill
\columnbreak
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 14th December, 2017.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
\vfill
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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
\end{tabulary}
\end{multicols}}




\begin{document}
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{One-Way ANOVA}}  \tn
% Row 0
\SetRowColor{LightBackground}
\seqsplit{Between-Group} Mean Square & Within-Group Mean Square & F-Ratio \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
1) (Subtract overall mean of pop from each group's mean)\textasciicircum{}2\textasciicircum{} & 1) (subtract overall mean of pop from each group (sample) mean), & 1) {[}(between group mean square) / (w/in-group mean square){]} \tn 
% Row Count 7 (+ 5)
% Row 2
\SetRowColor{LightBackground}
2) (squared difference) (sample size) & 2) then multiple each difference by (n-1) & 2) if \textasciitilde{} 1, then btwn-groups \& w/in-groups variances similar, accept H0 \tn 
% Row Count 13 (+ 6)
% Row 3
\SetRowColor{white}
3) compute degree of freedom (number of groups minus 1) & 3) calculate the grand sum & 3) if \textgreater{}1, then reject H0 \tn 
% Row Count 18 (+ 5)
% Row 4
\SetRowColor{LightBackground}
4) calculate \seqsplit{between-groups} mean square = {[}(btwn-group variance) / (df){]} & 4) calculate the degrees of freedom total (N-n of groups) &  \tn 
% Row Count 24 (+ 6)
% Row 5
\SetRowColor{white}
 & 5) calculate the w/in groups mean square = {[}(sum of squares) / (degrees of freedom total){]} &  \tn 
% Row Count 31 (+ 7)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\SetRowColor{LightBackground}
\mymulticolumn{3}{x{5.377cm}}{- Analysis of Variance ( compares means between  3+ samples) \newline Does not indicate which group(s) are different from which other groups (s) \newline - Parametric test \newline - Bonferroni post hoc test, reveals which specific means differed. Use if ANOVA was sig. using for pairwise comparison  \newline - It multiplies each of the significance levels from the LSD test by the number of tests performed. If this value is greater than 1, then a significance level of 1 is used.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.04425 cm} x{1.04425 cm} x{1.04425 cm} x{1.04425 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{4}{x{5.377cm}}{\bf\textcolor{white}{Chi-Square Test}}  \tn
% Row 0
\SetRowColor{LightBackground}
1) calculate the expected frequency (E) = {[}(row total) (column total) / total sample N{]} & \seqsplit{Standardized} Residuals & Phi (Ф) & Cramer's V \tn 
% Row Count 9 (+ 9)
% Row 1
\SetRowColor{white}
2) for each cell, find \seqsplit{(difference} between \seqsplit{overserved} \& expected counts)2 & reveal what cell adds the most \seqsplit{statistical} value to the test. & to measure the strength of \seqsplit{association} of \seqsplit{chi-square} test & to measure the strength of \seqsplit{association} of \seqsplit{chi-square} test \tn 
% Row Count 17 (+ 8)
% Row 2
\SetRowColor{LightBackground}
3) divide square \seqsplit{difference} by expected count for each cell, then sum results &  & 2x2 table & greater than 2x2 table \tn 
% Row Count 25 (+ 8)
% Row 3
\SetRowColor{white}
\mymulticolumn{4}{x{5.377cm}}{4) df = {[}(n of rows -1) (n of columns -1){]}} \tn 
% Row Count 26 (+ 1)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{4}{x{5.377cm}}{5) check X2 table for significance at @ 0.05 alpha level} \tn 
% Row Count 28 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}----}
\SetRowColor{LightBackground}
\mymulticolumn{4}{x{5.377cm}}{- {\bf{Dependent \& Independent nominal/nominal or nominal/ordinal data}} \newline - H0= no relationship between variables; expected counts for each cells = observed counts \newline - n is greater/equal to  20; no expected frequencies less/equal to 5 in 20\% or more of the cells}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}----}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Fisher's Exact Test for Chi-Square}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{-Use when Chi-Square assumptions are violated (\textgreater{}20\%) \newline % Row Count 2 (+ 2)
- Very small samples% Row Count 3 (+ 1)
} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Spearman's Rank Correlation}}  \tn
% Row 0
\SetRowColor{LightBackground}
1) Turn raw scores into ranks & Rho varies from -1 to +1 \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
2) find d2 = (difference between rankings)2 & -1 (a perfect negative correlation; as X increases, y decreases) \tn 
% Row Count 6 (+ 4)
% Row 2
\SetRowColor{LightBackground}
3) add up all the data in d2 column to obtain sumd2 & 0 = no association \tn 
% Row Count 9 (+ 3)
% Row 3
\SetRowColor{white}
4) calculation spearman's rank correlation coefficient (rho) rs = {[}1- (6*sumd2)/N3-N){]} df= n-2 & +1 (a perfect positive correlation; as X increases, Y increases \tn 
% Row Count 14 (+ 5)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{- Measures of associate for two ordinal variables; whether a relationship exists, how strong it is, what is the direction/pattern of relationship) (what happens to one variable, happens to the other variable) \newline - Nonparametric version of Pearson correlation coefficient \newline - H0= no sig  \newline {\bf{independent = x ; dependent = y}}}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{{\bf{Pearson's R Correlation Coefficient}}}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{r= Rho}} = measure of association (-1 to +1)} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{assumes x and y is normally distr. \& linearly related} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{(Pearson's r)2}} = PRE stat (strength of predicting amount of variance in Y based on X)} \tn 
% Row Count 5 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{r2}} = \% of variance in dependent (Y) explained by independent (X)} \tn 
% Row Count 7 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{usually {\bf{interval/ratio level data}}}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.33919 cm} x{2.63781 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Parametric vs. Non-parametric Tests}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Parametric}} & {\bf{Non-Parametric}} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
{\bf{interval or ratio data}} & {\bf{nominal and/or ordinal data}} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
one-way ANOVA & Distribution free \tn 
% Row Count 4 (+ 1)
% Row 3
\SetRowColor{white}
Pearson's R Correlation Coefficient & Wilcoxon Signed-Rank Test for {\bf{Two Related Conditions}} \tn 
% Row Count 7 (+ 3)
% Row 4
\SetRowColor{LightBackground}
 & Mann-Whiteny U Test for {\bf{Two Independent Conditions}} \tn 
% Row Count 10 (+ 3)
% Row 5
\SetRowColor{white}
 & Wilcoxon Rank Sum Test for {\bf{Two Independent Conditions}} \tn 
% Row Count 13 (+ 3)
% Row 6
\SetRowColor{LightBackground}
 & Chi-Square Test \tn 
% Row Count 14 (+ 1)
% Row 7
\SetRowColor{white}
 & Kruskal-Wallis \tn 
% Row Count 15 (+ 1)
% Row 8
\SetRowColor{LightBackground}
 & Spearman's Rank Correlation \tn 
% Row Count 17 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Wilcoxon Rank-Sum \&  Mann-Whitney U tests}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{nonparametric equivalent of independent-sample t-test} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{nominal and/or ordinal data} \tn 
% Row Count 3 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{Tests two independent conditions}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Wilcoxon Signed-Rank}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{- Use this test for {\bf{two related conditions (paired, matched)}} \newline % Row Count 2 (+ 2)
- ordinal data \newline % Row Count 3 (+ 1)
- nonparametric equivalent to the{\bf{ dependent-sample t-test}} \newline % Row Count 5 (+ 2)
H0 = The two groups are identically distributed.% Row Count 6 (+ 1)
} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Kruskal-Wallis}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{nonparametric equivalent of one-way ANOVA} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{nominal or ordinal data, but more than two independent samples} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{uses chi-square distribution} \tn 
% Row Count 4 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Regression}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{Predicts {\bf{dependent (y)}} based on value of {\bf{independent (x)}}} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Regression Formula}}: line that makes the sum of squares of the vertical distances of the data points from the line as small as possible} \tn 
% Row Count 5 (+ 3)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Principle of least-squares}} - finds estimates of parameters in a stat model based on observed data} \tn 
% Row Count 8 (+ 3)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{y= a + bx}}; a= y-axis; b= slope} \tn 
% Row Count 9 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{interval/ratio level data}} \newline  assumes linear relationship \newline observes independent (x)}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.55618 cm} x{1.51041 cm} x{1.51041 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{3}{x{5.377cm}}{\bf\textcolor{white}{Correlation}}  \tn
% Row 0
\SetRowColor{LightBackground}
Tests for & Difference between (r) and (r)2 & Assumptions \tn 
% Row Count 3 (+ 3)
% Row 1
\SetRowColor{white}
How well X predicts Y & r= Pearson's correlation coefficient = measure of association & For each independent (x), dependent (y) must be normal \tn 
% Row Count 8 (+ 5)
% Row 2
\SetRowColor{LightBackground}
how "tightly the predicted values fit regression line & r\textasciicircum{}2\textasciicircum{} = PRE stat (strength of predicting amount of variance in Y based on X) & Dependent variable variances same for all independent values \seqsplit{(homoscedasticity)} \tn 
% Row Count 15 (+ 7)
% Row 3
\SetRowColor{white}
to what degree X covaries with Y & r\textasciicircum{}2\textasciicircum{} = \% of variance in dependent (Y) explained by independent (X) & Avoid predictions outside the observed values; beware extremes; \seqsplit{relationships} must be linear over all values. \tn 
% Row Count 24 (+ 9)
% Row 4
\SetRowColor{LightBackground}
 &  & linear relationship, observes independent (X) \tn 
% Row Count 28 (+ 4)
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\SetRowColor{LightBackground}
\mymulticolumn{3}{x{5.377cm}}{usually, {\bf{interval/ratio level data}}}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}---}
\end{tabularx}
\par\addvspace{1.3em}


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

\end{document}