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% Document Info
\author{yeeterbaby69}
\pdfinfo{
  /Title (college-elementary-statistics.pdf)
  /Creator (Cheatography)
  /Author (yeeterbaby69)
  /Subject (COLLEGE|ELEMENTARY STATISTICS Cheat Sheet)
}

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\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
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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{COLLEGE|ELEMENTARY STATISTICS Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{yeeterbaby69} via \textcolor{DarkBackground}{\uline{cheatography.com/203355/cs/43308/}}}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}yeeterbaby69 \\
  \uline{cheatography.com/yeeterbaby69} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 6th May, 2024.\\
   Page {\thepage} of \pageref{LastPage}.
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  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\begin{multicols*}{4}

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\mymulticolumn{4}{x{3.833cm}}{\bf\textcolor{white}{{\bf{VOCABULARY }}| CHAPTER 1}}  \tn
% Row 0
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\{\{ar\}\}{\bf{Statistics:}} & a branch of \seqsplit{mathematics} that deals with collecting data and analyzing \seqsplit{information} to draw \seqsplit{conclusions} and help make decisions when faced with \seqsplit{uncertainty}. Statistics also provides a measure of confidence in a conclusion that is drawn. & \{\{bl\}\}{\bf{Example:}} & 1) Gathering data  2) Organizing and \seqsplit{summarizing} that data \seqsplit{3)Analyzing} the data to find answers 4) Reporting the results in a way that shows how reliable those answers are \tn 
% Row Count 22 (+ 22)
% Row 1
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\{\{ar\}\}{\bf{Data}} & "a fact or \seqsplit{proposition} used to draw a conclusion or make a decision." & \{\{bl\}\}{\bf{Example:}} & numerical; height. \seqsplit{Nonnumerical;} gender. \tn 
% Row Count 29 (+ 7)
% Row 2
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\{\{ar\}\}{\bf{Anecdotal}} & The \seqsplit{information} being conveyed is based on casual \seqsplit{observation}, not scientific research. &  & —-{}-{}-{}- the misuse of data typically happens when data is \seqsplit{incorrectly} obtained or analyzed. \tn 
% Row Count 38 (+ 9)
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\mymulticolumn{4}{x{3.833cm}}{\bf\textcolor{white}{{\bf{VOCABULARY }}| CHAPTER 1 (cont)}}  \tn
% Row 3
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\} {\bf{Population vs. Sample}}} \tn 
% Row Count 1 (+ 1)
% Row 4
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\{\{ar\}\}{\bf{Population:}} & the entire group of items or \seqsplit{individuals} about which we want \seqsplit{information;} the entire set of objects or \seqsplit{individuals} to be studied & \{\{bl\}\}{\bf{Example:}} & the set of all \seqsplit{undergraduate} students enrolled in Boston University as of Jan. 19, 2024. \tn 
% Row Count 13 (+ 12)
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\{\{ar\}\}{\bf{Sample:}} & a subset of the population that is being studied. & \{\{bl\}\}{\bf{Example:}} & part of the population of interest that we examine in order to gather \seqsplit{information}. \tn 
% Row Count 21 (+ 8)
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\} {\bf{Descriptive vs. Inferential Statistics}}} \tn 
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\{\{ar\}\}{\bf{Descriptive Statistics:}} & consists of organizing and \seqsplit{summarizing} data using numerical summaries (e.g. mean, IQR, standard deviation), tables, and graphs. &  &  \tn 
% Row Count 34 (+ 12)
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\mymulticolumn{4}{x{3.833cm}}{\bf\textcolor{white}{{\bf{VOCABULARY }}| CHAPTER 1 (cont)}}  \tn
% Row 8
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\{\{ar\}\}{\bf{Inferential Statistics:}} & uses \seqsplit{information} from a sample to make a conclusion about a larger group of items or \seqsplit{individuals}, e.g. the \seqsplit{population.} \seqsplit{Inferential} statistics are used to draw inferences about a population from a sample. &  &  \tn 
% Row Count 19 (+ 19)
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\} {\bf{Types of Variables}}} \tn 
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\{\{ar\}\}{\bf{Qualitative (or \seqsplit{categorical)} variable:}} & a \seqsplit{characteristic} or attribute that places an individual into one of several categories & \{\{bl\}\}{\bf{Examples:}} & gender; year in college –e.g. freshman, sophomore; state in which a person was born. \tn 
% Row Count 28 (+ 8)
% Row 11
\SetRowColor{white}
\{\{ar\}\}{\bf{Quantitative variable:}} & a \seqsplit{characteristic} or attribute with numerical values for which arithmetic operations provide meaningful results (or "for which arithmetic operations make sense" & \{\{bl\}\}{\bf{Examples:}} & How the daily weather is described - \seqsplit{temperature}, relative humidity. \tn 
% Row Count 43 (+ 15)
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\} {\bf{Two Types of Quantitative Variables}}} \tn 
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\{\{ar\}\} {\bf{Discrete variable:}} & \seqsplit{quantitative} variable with either a finite number or countable number of possible values. Countable means the values result from counting, e. g. 0, 1, 2, 3 and so on. & \{\{bl\}\}{\bf{Examples:}} & a household could have three children or six children, but not 4.53 children. \tn 
% Row Count 17 (+ 16)
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\{\{ar\}\}{\bf{Continuous variable:}} & \seqsplit{quantitative} variable with infinite possible values which are not countable & \{\{bl\}\}{\bf{Examples:}} & the response time of a computer could be 0.64 seconds, or it could be \seqsplit{0.64237123922121} seconds \tn 
% Row Count 26 (+ 9)
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\} {\bf{Observational Study vs. Designed Experiment}}} \tn 
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\{\{ar\}\}{\bf{Observational Study:}} & \seqsplit{researchers} simply observe \seqsplit{individuals} or question \seqsplit{participants} without trying to influence their response. Often \seqsplit{participants} are chosen randomly. &  &  \tn 
% Row Count 42 (+ 14)
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\{\{ar\}\}{\bf{Designed Experiment \seqsplit{(Experimental} Study)}} & \seqsplit{Researchers} setup an experiment and manipulate a variable and measure the effect of the \seqsplit{manipulation} on some outcome of interest. Often \seqsplit{participants} are randomly assigned to the various conditions and \seqsplit{treatments.} &  &  \tn 
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\mymulticolumn{4}{x{3.833cm}}{} \tn 
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\{\{ar\}\}{\bf{Confounding:}} & occurs in a study "when the effects of two or more \seqsplit{explanatory} variables are not \seqsplit{separated."} &  &  \tn 
% Row Count 29 (+ 9)
% Row 20
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\{\{ar\}\}{\bf{Lurking variable:}} & a variable that was not considered explicitly "in a study, but that affects the value of the response variable" &  &  \tn 
% Row Count 40 (+ 11)
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\} {\bf{Bias In Sampling}}} \tn 
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\}{\bf{Bias is a common problem during survey sampling.}}} \tn 
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\{\{ar\}\}{\bf{Selection bias (or Sampling bias):}} & occurs if the method for selecting the \seqsplit{participants} produces a sample that does not represent the population of interest. &  &  \tn 
% Row Count 14 (+ 11)
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\mymulticolumn{1}{x{3.833cm}}{\bf\textcolor{white}{STATS}}  \tn
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\mymulticolumn{4}{x{3.833cm}}{\bf\textcolor{white}{VOCABULARY| CHAPTER 2}}  \tn
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\}	{\bf{Graphical Methods for Qualitative (Categorical) Variables}}} \tn 
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\{\{ar\}\}	{\bf{Qualitative \seqsplit{(categorical)} variable:}} & a \seqsplit{characteristic} that places an individual into one of several categories & \{\{bl\}\}{\bf{Examples:}} & e.g. sex, \seqsplit{nationality}, political party) \tn 
% Row Count 9 (+ 7)
% Row 2
\SetRowColor{LightBackground}
\{\{ar\}\}	{\bf{Qualitative variables:}} &  & \{\{bl\}\} {\bf{Example:}} & can be \seqsplit{numerically} described with \seqsplit{frequencies} (counts), relative \seqsplit{frequencies} (percent, \seqsplit{proportions)}, cumulative \seqsplit{frequencies}, and cumulative relative \seqsplit{frequencies}. \tn 
% Row Count 24 (+ 15)
% Row 3
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\mymulticolumn{4}{x{3.833cm}}{\{\{ac\}\}{\bf{The number of times each unique variable element is observed is called the count, or frequency (f). The relative frequency equals the frequency divided the sample size n or f/n. Relative Frequency = f/n}}} \tn 
% Row Count 29 (+ 5)
\hhline{>{\arrayrulecolor{DarkBackground}}----}
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% That's all folks
\end{multicols*}

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