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\author{ktown022}
\pdfinfo{
  /Title (psyc300a-test-2.pdf)
  /Creator (Cheatography)
  /Author (ktown022)
  /Subject (PSYC300A - test \#2 Cheat Sheet)
}

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        \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{PSYC300A - test \#2 Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{ktown022} via \textcolor{DarkBackground}{\uline{cheatography.com/164409/cs/34853/}}}
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\noindent
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}ktown022 \\
  \uline{cheatography.com/ktown022} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 22nd October, 2022.\\
   Page {\thepage} of \pageref{LastPage}.
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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{multicols*}{3}

\begin{tabularx}{5.377cm}{x{2.83689 cm} x{2.14011 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Equations!}}  \tn
% Row 0
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Deviation score: & (x-x̄) \tn 
% Row Count 1 (+ 1)
% Row 1
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Squared deviation score: & (x-x̄)2 \tn 
% Row Count 3 (+ 2)
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Sum of squares: & SS= Σ(x-x̄)2 \tn 
% Row Count 4 (+ 1)
% Row 3
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Variance: & SD2 = SS÷N \tn 
% Row Count 5 (+ 1)
% Row 4
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Standard deviation: & √variance or √SD2 \tn 
% Row Count 7 (+ 2)
% Row 5
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Covariance & cov = SP÷N \tn 
% Row Count 8 (+ 1)
% Row 6
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Pearson correlation: & r = cov. ÷ (SDx)(SDy) \tn 
% Row Count 10 (+ 2)
% Row 7
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Slope: & by = r(SDy÷SDx) \tn 
% Row Count 11 (+ 1)
% Row 8
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intercept: & ay = ȳ - by(x̄) \tn 
% Row Count 12 (+ 1)
% Row 9
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Total variability: & SST = Σ(Y-ȳ)2 \tn 
% Row Count 13 (+ 1)
% Row 10
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explained variability: & SSR = Σ(Y'-ȳ)2 \tn 
% Row Count 15 (+ 2)
% Row 11
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unexplained variability & SSE = Σ(Y-Y')2 \tn 
% Row Count 17 (+ 2)
% Row 12
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Standard error of prediction: & SDy-y' = SDy√1-r2 \tn 
% Row Count 19 (+ 2)
% Row 13
\SetRowColor{white}
Predicting X': & X' = ax + bxY \tn 
% Row Count 20 (+ 1)
% Row 14
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Predicting Y': & Y' = ay + byX \tn 
% Row Count 21 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.09494 cm} x{3.88206 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{General guidelines for test reliability}}  \tn
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\textgreater{}.85 & very desirable \tn 
% Row Count 1 (+ 1)
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.70 to .85 & desirable aka moderately acceptable \tn 
% Row Count 3 (+ 2)
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\textless{}.70 & not desirable aka poor reliability \tn 
% Row Count 5 (+ 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}{describe relationship between two variables?}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{1.) Direction of the relationship:}} \newline % Row Count 1 (+ 1)
Positive (+) or negative (-) \newline % Row Count 2 (+ 1)
{\emph{Positive correlation}} = As the values of x increase or decrease, so do the values of y \newline % Row Count 4 (+ 2)
{\emph{No relationship}} = no consistent relationship between variables \newline % Row Count 6 (+ 2)
{\emph{Negative correlation}} = As the values of x increases, the value of y decreases, and vice versa \newline % Row Count 8 (+ 2)
{\bf{2.) shape of the relationship}} \newline % Row Count 9 (+ 1)
Linear relationship = straight line relationships \newline % Row Count 10 (+ 1)
– All dots clustered around straight line \newline % Row Count 11 (+ 1)
Curvilinear relationship = consistent, predictable relationship, but not linear \newline % Row Count 13 (+ 2)
– As the values of x increase, the values of y increases but at some point the pattern reverses \newline % Row Count 15 (+ 2)
{\bf{3.) Strength of the relationship}} \newline % Row Count 16 (+ 1)
Subjective measure of relationship between two scores (e.g., weak, moderate, strong, no relationship) \newline % Row Count 19 (+ 3)
how closely the data points cluster together \newline % Row Count 20 (+ 1)
The more spread out they are from a line of some sort, the weaker the correlation between variables \newline % Row Count 22 (+ 2)
{\bf{4.) Magnitude of the relationship}} \newline % Row Count 23 (+ 1)
Objective measure of relationship based on computed r value: ranges from -1 to 1% Row Count 25 (+ 2)
} \tn 
\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}{biserial correlation}}  \tn
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\mymulticolumn{2}{x{5.377cm}}{} \tn 
% Row Count 0 (+ 0)
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\mymulticolumn{2}{x{5.377cm}}{{\bf{When to use it:}} \newline – when one of the variables is nominal (with only two groups) and the other variable is interval/ratio \newline {\emph{How to calculate:}} \newline – use the same formula as pearson r}  \tn 
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\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Curvilinear relationships:}}  \tn
% Row 0
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\mymulticolumn{1}{x{5.377cm}}{Linear:  Y' = a + bX} \tn 
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% Row 1
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\mymulticolumn{1}{x{5.377cm}}{Quadratic: Y' = a +bX + cX2} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Cubic: Y' = a + bX + cX2 + dX3} \tn 
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\mymulticolumn{1}{x{5.377cm}}{Quartic: Y' = a + bX + cX2 + dX3 + eX4} \tn 
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\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Comparing SDy-y' and SDy}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{When R does not equal Zero, SDy-y' will be smaller than SDy} \tn 
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% Row 1
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\mymulticolumn{1}{x{5.377cm}}{When R=0 (no \seqsplit{correlation/relationship)},  SDy-y' = SDy} \tn 
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% Row 2
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\mymulticolumn{1}{x{5.377cm}}{When R=+/- 1 (perfect correlation), SDy-y'=0} \tn 
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\begin{tabularx}{5.377cm}{x{1.64241 cm} x{3.33459 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{How do we describe our data?}}  \tn
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1.) Shape & plotting a scatter plot, linearity, strength, direction, magnitude \tn 
% Row Count 3 (+ 3)
% Row 1
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2.) Central tendency & defining the regression line (mean of bivariate data) \tn 
% Row Count 6 (+ 3)
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3.) Variability & standard error or estimates (SDY-Y') \tn 
% Row Count 8 (+ 2)
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Factors affecting R}}  \tn
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1.) Relationship is real and strong or weak & contributes to a bigger/smaller r \tn 
% Row Count 3 (+ 3)
% Row 1
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2.) Sampling error & Sampling error = naturally occurring discrepancy, or error, that exists between a sample statistic and the corresponding parameter \tn 
% Row Count 10 (+ 7)
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3.) Unmeasured third variable & contributes to a bigger/smaller r,Correlation tells us if a relationship between two variables exists but does not tell us about causation \tn 
% Row Count 17 (+ 7)
% Row 3
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4.) Heterogeneous sample & Data in which the sample of observations could be subdivided into two distinct sets on the basis of some other variable \tn 
% Row Count 23 (+ 6)
% Row 4
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5.) Sampling from a restricted (truncated) range & The correlation coefficient will be affected by the range of score in the data \tn 
% Row Count 27 (+ 4)
% Row 5
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6.) Non-linearity: relationship is curvilinear & Reminder: r underestimates a curvilinear relationship, contributes to a smaller r \tn 
% Row Count 32 (+ 5)
\end{tabularx}
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\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Factors affecting R (cont)}}  \tn
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7.) Heteroscedasticity in the data & contributes to a smaller r \tn 
% Row Count 2 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
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\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{PHI}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{When to use it:}} \newline % Row Count 1 (+ 1)
– when both variables are nominal (with only two groups per variable, i.e., dichotomous) \newline % Row Count 3 (+ 2)
{\emph{Calculating Phi:}} \newline % Row Count 4 (+ 1)
– use the same formula as pearson r% Row Count 5 (+ 1)
} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.38896 cm} x{2.58804 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{How to calculate Pearson r:}}  \tn
% Row 0
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\mymulticolumn{2}{x{5.377cm}}{1.) Plot the data (scatterplot)} \tn 
% Row Count 1 (+ 1)
% Row 1
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2.) Compute bivariate statistics & (e.g., deviation scores, SP, COV) \tn 
% Row Count 3 (+ 2)
% Row 2
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3.) Compute correlation coefficient r & (number beyond +/-1 means you did it wrong) \tn 
% Row Count 6 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.14011 cm} x{2.83689 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Interpreting Pearson Correlation}}  \tn
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\textless{} |.10| & no relationship \tn 
% Row Count 1 (+ 1)
% Row 1
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|.10| to |.30| & weak relationship \tn 
% Row Count 2 (+ 1)
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\textgreater{} |.30| to |.50| & moderate relationship \tn 
% Row Count 3 (+ 1)
% Row 3
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\textgreater{} |.50| & strong relationship \tn 
% Row Count 4 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Reporting in APA format}}  \tn
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1.) describes relationship in statistical terms & Give variables, R = ?, Mean = ?, Standard deviation = ?, Give sample size, Mention strength and if its positive for negative \tn 
% Row Count 7 (+ 7)
% Row 1
\SetRowColor{white}
\mymulticolumn{2}{x{5.377cm}}{2.) Results in plain language} \tn 
% Row Count 8 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
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\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{extra stuff}}  \tn
% Row 0
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Homoscedasticity (a good thing): & Variability in Y scores remains constant across increasing values of X \tn 
% Row Count 4 (+ 4)
% Row 1
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Heteroscedasticity (not a good thing): & variability in y scores changes across increasing values of x, Caused by a skew in one or both variables \tn 
% Row Count 10 (+ 6)
% Row 2
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SST = SSy & SSe = SSy-y' (error) \tn 
% Row Count 11 (+ 1)
% Row 3
\SetRowColor{white}
SSr = SSt - SSe & Σ(Y-Y') = 0 \tn 
% Row Count 12 (+ 1)
% Row 4
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\mymulticolumn{2}{x{5.377cm}}{For Y': if r=0, by=0 (i.e., regression line is parallel to the x-axis), and ay=ȳ} \tn 
% Row Count 14 (+ 2)
% Row 5
\SetRowColor{white}
\mymulticolumn{2}{x{5.377cm}}{For X': if r=0, bx=0 (i.e., regression line is parallel to the x-axis), and ax=x̄} \tn 
% Row Count 16 (+ 2)
% Row 6
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{As correlation (r) increases, the numerical value for b increases} \tn 
% Row Count 18 (+ 2)
% Row 7
\SetRowColor{white}
Total variability = differences between observed data (Y) and the mean value of Y & – Y-ȳ \tn 
% Row Count 23 (+ 5)
% Row 8
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Unexplained variability (i.e., residuals) = difference between the observed value for Y and the predicted value for Y(Y') & – Y - Y' \tn 
% Row Count 30 (+ 7)
\end{tabularx}
\par\addvspace{1.3em}

\vfill
\columnbreak
\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{extra stuff (cont)}}  \tn
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Explained variability = the difference between total and unexplained variability & – Y'- ȳ \tn 
% Row Count 4 (+ 4)
% Row 10
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\mymulticolumn{2}{x{5.377cm}}{Standardized test = interval} \tn 
% Row Count 5 (+ 1)
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\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Spearman rho}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{When to use it: \newline % Row Count 1 (+ 1)
– one or both variables are on an ordinal scale of measurement \newline % Row Count 3 (+ 2)
– there is a weak curvilinear relationship in interval/ratio data \newline % Row Count 5 (+ 2)
– there is heteroscedasticity in interval/ratio data \newline % Row Count 7 (+ 2)
{\emph{How to calculate:}} \newline % Row Count 8 (+ 1)
Convert all scores into ranks \newline % Row Count 9 (+ 1)
Lower scores get lower ranks \newline % Row Count 10 (+ 1)
High scores get higher ranks \newline % Row Count 11 (+ 1)
Use the pearson correlation formula to find how consistently increases in one variable are associated with increases in another variable% Row Count 14 (+ 3)
} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}


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

\end{document}