Rules of Ten: rules of thumb for data collection and processing

Continuing my run making small posters of semi-useful information for physics classes (see Graph Analysis and Mathematical Models), I present the 3rd installment, adapted from Andy Smith’s list that he uses in his class.


Update: You can now find source code for this and other posters in my GitHub repository.

\documentclass[final]{beamer} % beamer 3.10: do NOT use option hyperref={pdfpagelabels=false} !
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\usetheme{default}    %% you should define your own theme e.g. for big headlines using your own logos
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\usepackage{amsmath,amsthm, amssymb, latexsym}
%\usefonttheme{professionalfonts}  % times is obsolete
%\usepackage[orientation=portrait,size=a0,scale=1.4,debug]{beamerposter}                       % e.g. for DIN-A0 poster
%\usepackage[orientation=portrait,size=a1,scale=1.4,grid,debug]{beamerposter}                  % e.g. for DIN-A1 poster, with optional grid and debug output
\usepackage[size=custom,width=45.72,height=60.96,scale=1.8,debug]{beamerposter}                     % e.g. for custom size poster (18in x 24in w/ printable 17in x 23in)
%\usepackage[orientation=portrait,size=a0,scale=1.0,printer=rwth-glossy-uv.df]{beamerposter}   % e.g. for DIN-A0 poster with rwth-glossy-uv printer check
% ...


\title[Rules of Ten]{Rules of Ten: rules of thumb for data collection and processing}
\author[Vancil]{Brian Vancil}
\institute[Sumner]{Sumner Academy of Arts & Sciences}

\begin{frame}{Rules of Ten}
\framesubtitle{rules of thumb for data collection and processing}

\item \header{Collect at least 10 data points.}  A data point is one pairing of independent and dependent variable measurements.  Without enough data points, we cannot reliably find trends in the data. \spacing

\item \header{The largest independent variable measurement should be at least 10 times the smallest independent variable measurement.}  Nature sometimes surprises us at larger or smaller scales than we think to look. \spacing

\item \header{We like the uncertainty in our measurements to be less than 10\% of the range of the measurements.}  There is no point in trying to understand our results mathematically if the variation we see is around the same size as the uncertainty in the measurements. \spacing

\item \header{We like the root mean square error (RMSE) for a fit to be less than 10\% of the range of dependent variable measurements.}  A large RMSE means that our mathematical model does not fit the data very well. \spacing

\item \header{We will consider the vertical intercept negligible if it is less than 5\% of the range of the dependent variable measurements.}  A vertical intercept is likely to be meaningful if it has a decent magnitude compared with our dependent variable measurements.

Adapted from a list by Andrew~Smith, Air~Academy~High~School, Colorado~Springs,~CO