\documentclass[11pt,twoside]{article}\makeatletter

\IfFileExists{xcolor.sty}%
  {\RequirePackage{xcolor}}%
  {\RequirePackage{color}}
\usepackage{colortbl}
\usepackage{wrapfig}
\usepackage{ifxetex}
\ifxetex
  \usepackage{fontspec}
  \usepackage{xunicode}
  \catcode`⃥=\active \def⃥{\textbackslash}
  \catcode`❴=\active \def❴{\{}
  \catcode`❵=\active \def❵{\}}
  \def\textJapanese{\fontspec{Noto Sans CJK JP}}
  \def\textChinese{\fontspec{Noto Sans CJK SC}}
  \def\textKorean{\fontspec{Noto Sans CJK KR}}
  \setmonofont{DejaVu Sans Mono}
  
\else
  \IfFileExists{utf8x.def}%
   {\usepackage[utf8x]{inputenc}
      \PrerenderUnicode{–}
    }%
   {\usepackage[utf8]{inputenc}}
  \usepackage[english]{babel}
  \usepackage[T1]{fontenc}
  \usepackage{float}
  \usepackage[]{ucs}
  \uc@dclc{8421}{default}{\textbackslash }
  \uc@dclc{10100}{default}{\{}
  \uc@dclc{10101}{default}{\}}
  \uc@dclc{8491}{default}{\AA{}}
  \uc@dclc{8239}{default}{\,}
  \uc@dclc{20154}{default}{ }
  \uc@dclc{10148}{default}{>}
  \def\textschwa{\rotatebox{-90}{e}}
  \def\textJapanese{}
  \def\textChinese{}
  \IfFileExists{tipa.sty}{\usepackage{tipa}}{}
\fi
\def\exampleFont{\ttfamily\small}
\DeclareTextSymbol{\textpi}{OML}{25}
\usepackage{relsize}
\RequirePackage{array}
\def\@testpach{\@chclass
 \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else
  \ifnum \@lastchclass=7 5 \else
   \ifnum \@lastchclass=8 \tw@ \else
    \ifnum \@lastchclass=9 \thr@@
   \else \z@
   \ifnum \@lastchclass = 10 \else
   \edef\@nextchar{\expandafter\string\@nextchar}%
   \@chnum
   \if \@nextchar c\z@ \else
    \if \@nextchar l\@ne \else
     \if \@nextchar r\tw@ \else
   \z@ \@chclass
   \if\@nextchar |\@ne \else
    \if \@nextchar !6 \else
     \if \@nextchar @7 \else
      \if \@nextchar (8 \else
       \if \@nextchar )9 \else
  10
  \@chnum
  \if \@nextchar m\thr@@\else
   \if \@nextchar p4 \else
    \if \@nextchar b5 \else
   \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi
   \fi \fi  \fi  \fi  \fi  \fi  \fi \fi \fi \fi \fi \fi}
\gdef\arraybackslash{\let\\=\@arraycr}
\def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}}
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\def\abbr{}
\def\corr{}
\def\expan{}
\def\gap{}
\def\orig{}
\def\reg{}
\def\ref{}
\def\sic{}
\def\persName{}\def\name{}
\def\placeName{}
\def\orgName{}
\def\textcal#1{{\fontspec{Lucida Calligraphy}#1}}
\def\textgothic#1{{\fontspec{Lucida Blackletter}#1}}
\def\textlarge#1{{\large #1}}
\def\textoverbar#1{\ensuremath{\overline{#1}}}
\def\textquoted#1{‘#1’}
\def\textsmall#1{{\small #1}}
\def\textsubscript#1{\@textsubscript{\selectfont#1}}
\def\textxi{\ensuremath{\xi}}
\def\titlem{\itshape}
\newenvironment{biblfree}{}{\ifvmode\par\fi }
\newenvironment{bibl}{}{}
\newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par }
\newenvironment{citbibl}{}{\ifvmode\par\fi }
\newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi }
\newenvironment{docDate}{}{\ifvmode\par\fi }
\newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi }
\newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par }
\newenvironment{msHead}{\vskip 6pt}{\par}
\newenvironment{msItem}{\vskip 6pt}{\par}
\newenvironment{rubric}{}{}
\newenvironment{titlePart}{}{\par }

\newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}}
\newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}}
\newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}}
\newcolumntype{P}[1]{){\arraybackslash}p{#1}}
\newcolumntype{B}[1]{){\arraybackslash}b{#1}}
\newcolumntype{M}[1]{){\arraybackslash}m{#1}}
\definecolor{label}{gray}{0.75}
\def\unusedattribute#1{\sout{\textcolor{label}{#1}}}
\DeclareRobustCommand*{\xref}{\hyper@normalise\xref@}
\def\xref@#1#2{\hyper@linkurl{#2}{#1}}
\begingroup
\catcode`\_=\active
\gdef_#1{\ensuremath{\sb{\mathrm{#1}}}}
\endgroup
\mathcode`\_=\string"8000
\catcode`\_=12\relax

\usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry}
\usepackage{framed}

\definecolor{shadecolor}{gray}{0.95}
\usepackage{longtable}
\usepackage[normalem]{ulem}
\usepackage{fancyvrb}
\usepackage{fancyhdr}
\usepackage{graphicx}
\usepackage{marginnote}

\renewcommand{\@cite}[1]{#1}


\renewcommand*{\marginfont}{\itshape\footnotesize}

\def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif}

  \pagestyle{fancy}

\usepackage[pdftitle={Comparison of Capability Analysis of Cumulative Cardiac Thoracic Ratio (CTR) Outputs},
 pdfauthor={}]{hyperref}
\hyperbaseurl{}

	 \paperwidth210mm
	 \paperheight297mm
              
\def\@pnumwidth{1.55em}
\def\@tocrmarg {2.55em}
\def\@dotsep{4.5}
\setcounter{tocdepth}{3}
\clubpenalty=8000
\emergencystretch 3em
\hbadness=4000
\hyphenpenalty=400
\pretolerance=750
\tolerance=2000
\vbadness=4000
\widowpenalty=10000

\renewcommand\section{\@startsection {section}{1}{\z@}%
     {-1.75ex \@plus -0.5ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large\bfseries}}
\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%
     {-1.75ex\@plus -0.5ex \@minus- .2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large}}
\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%
     {-1.5ex\@plus -0.35ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\large}}
\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}%
     {-1ex \@plus-0.35ex \@minus -0.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\normalsize}}
\renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}%
     {1.5ex \@plus1ex \@minus .2ex}%
     {-1em}%
     {\reset@font\normalsize\bfseries}}


\def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt}
 \@tempdima 1.5em \begingroup
 \parindent \z@ \rightskip \@pnumwidth 
 \parfillskip -\@pnumwidth 
 \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par
 \endgroup}
\def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}}
\def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}}
\def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}}
\def\l@subparagraph{\@dottedtocline{5}{10em}{5em}}
\@ifundefined{c@section}{\newcounter{section}}{}
\@ifundefined{c@chapter}{\newcounter{chapter}}{}
\newif\if@mainmatter 
\@mainmattertrue
\def\chaptername{Chapter}
\def\frontmatter{%
  \pagenumbering{roman}
  \def\thechapter{\@roman\c@chapter}
  \def\theHchapter{\roman{chapter}}
  \def\thesection{\@roman\c@section}
  \def\theHsection{\roman{section}}
  \def\@chapapp{}%
}
\def\mainmatter{%
  \cleardoublepage
  \def\thechapter{\@arabic\c@chapter}
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \pagenumbering{arabic}
  \setcounter{secnumdepth}{6}
  \def\@chapapp{\chaptername}%
  \def\theHchapter{\arabic{chapter}}
  \def\thesection{\@arabic\c@section}
  \def\theHsection{\arabic{section}}
}
\def\backmatter{%
  \cleardoublepage
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \setcounter{secnumdepth}{2}
  \def\@chapapp{\appendixname}%
  \def\thechapter{\@Alph\c@chapter}
  \def\theHchapter{\Alph{chapter}}
  \appendix
}
\newenvironment{bibitemlist}[1]{%
   \list{\@biblabel{\@arabic\c@enumiv}}%
       {\settowidth\labelwidth{\@biblabel{#1}}%
        \leftmargin\labelwidth
        \advance\leftmargin\labelsep
        \@openbib@code
        \usecounter{enumiv}%
        \let\p@enumiv\@empty
        \renewcommand\theenumiv{\@arabic\c@enumiv}%
	}%
  \sloppy
  \clubpenalty4000
  \@clubpenalty \clubpenalty
  \widowpenalty4000%
  \sfcode`\.\@m}%
  {\def\@noitemerr
    {\@latex@warning{Empty `bibitemlist' environment}}%
    \endlist}

\def\tableofcontents{\section*{\contentsname}\@starttoc{toc}}
\parskip0pt
\parindent1em
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\newenvironment{reflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemsep}{0pt}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\itshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{sansreflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\itemsep}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\upshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{specHead}[2]%
 {\vspace{20pt}\hrule\vspace{10pt}%
  \phantomsection\label{#1}\markright{#2}%

  \pdfbookmark[2]{#2}{#1}%
  \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}%
  }{}
      \def\TheFullDate{2014-01-15 (revised: 15 January 2014)}
\def\TheID{\makeatother }
\def\TheDate{2014-01-15}
\title{Comparison of Capability Analysis of Cumulative Cardiac Thoracic Ratio (CTR) Outputs}
\author{}\makeatletter 
\makeatletter
\newcommand*{\cleartoleftpage}{%
  \clearpage
    \if@twoside
    \ifodd\c@page
      \hbox{}\newpage
      \if@twocolumn
        \hbox{}\newpage
      \fi
    \fi
  \fi
}
\makeatother
\makeatletter
\thispagestyle{empty}
\markright{\@title}\markboth{\@title}{\@author}
\renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@
\belowdisplayskip \abovedisplayskip
\abovedisplayshortskip \z@ plus2\p@
\belowdisplayshortskip 4\p@ plus2\p@ minus2\p@
\def\@listi{\leftmargin\leftmargini
               \topsep 2\p@ plus1\p@ minus1\p@
               \parsep 2\p@ plus\p@ minus\p@
               \itemsep 1pt}
}
\makeatother
\fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm}
\fancyhf{} 
\setlength{\headheight}{14pt}
\fancyhead[LE]{\bfseries\leftmark} 
\fancyhead[RO]{\bfseries\rightmark} 
\fancyfoot[RO]{}
\fancyfoot[CO]{\thepage}
\fancyfoot[LO]{\TheID}
\fancyfoot[LE]{}
\fancyfoot[CE]{\thepage}
\fancyfoot[RE]{\TheID}
\hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true}
\fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}}

\date{}
\usepackage{authblk}

\providecommand{\keywords}[1]
{
\footnotesize
  \textbf{\textit{Index terms---}} #1
}

\usepackage{graphicx,xcolor}
\definecolor{GJBlue}{HTML}{273B81}
\definecolor{GJLightBlue}{HTML}{0A9DD9}
\definecolor{GJMediumGrey}{HTML}{6D6E70}
\definecolor{GJLightGrey}{HTML}{929497} 

\renewenvironment{abstract}{%
   \setlength{\parindent}{0pt}\raggedright
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
   \textcolor{GJBlue}{\large\bfseries\abstractname\space}
}{%   
   \vskip8pt
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
}

\usepackage[absolute,overlay]{textpos}

\makeatother 
      \usepackage{lineno}
      \linenumbers
      
\begin{document}

             \author[1]{Shaib,  I.O}

             \affil[1]{  Redeemer Univeristy}

\renewcommand\Authands{ and }

\date{\small \em Received: 15 December 2013 Accepted: 2 January 2014 Published: 15 January 2014}

\maketitle


\begin{abstract}
        


This study investigated the Capability Process Analysis of cumulative Cardiac Thoracic Ratio (CTR) during Radiological Chest Examination using MX.4 Radiological Diagnostic Machine (DRM) at the Fate Medical Foundation Radiological Department, Auchi. The data for the study are classified as raw and simulated CTR values. Statistical process control was investigated to address process stability and capability analysis was performed for the two processes. The pattern of the means of the raw and simulated values was investigated using normal probability plots and empirical CDF functions. The raw computed CTR values and simulated CTR values confirmed that the system is operating under 1.0 â??" 1.3 sigma level for the raw CTR values. Around 28-39% of the raw CTR values obtained fall outside the specification limits. In addition, for all the cumulative raw CTR values suggested that the process is off centered and is towards the lower specification limit. Further study should be conducted on large repeated experimental CTR sample to ascertain the reliability of this study. Fellow up study of patients should be undertaken by the cardiologist to reduce the possible health risk associated with high CTR.

\end{abstract}


\keywords{algorithms, capability plot, CTR, mx.4 DRM x-ray, heart failure.}

\begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) 
\textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark}
\end{textblock*}

\begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) 
\textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}}
\end{textblock*}


\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
dvance knowledge has made the study of process capability analysis not limited to the industry or manufacturing process only but is gaining overwhelming application in other fields of human endeavour especially in medicine for the evaluation of health care performance such as surgical site control, infection rate, response of patient to change in treatment in the hospital, outbreak of epidemic and performance of a forecasting system related to medical studies such as heart false positive radiological examination. This study looks at the process monitoring of CTR output measurements and check its state of stability for abnormality detection.\par
In medicine, chest radiography is commonly called chest X-ray (CXR). It is a projection of radiography of the chest use to diagnose conditions affecting the chest, its contents and nearly structure. Ribeiro, Jose, Renato, Roberto, Francisco, Domingo, and Beatriz (2012) observed that chest radiography is among the most common films taken to diagnose many conditions. Like all methods of radiography, chest radiography employs ionizing radiation in the form of xrays to generate images of the chest  {\ref (Ribeiro, et al. (2012)}.\par
This research is motivated by the real life application of process capability analysis in the output of Cardio Thoracic Ratio of chest X-ray measurements in the examination of radiological process to establish capability analysis of the CTR experimental values. The aim of this study is to determine the capability analysis of Radiological CTR experimental values and the simulated values. The Specific objectives of the research are:\par
? To do capability analysis for experimental (Raw) and Simulated Cardiac Thoracic Ratio (CTR) values.\par
? To compare the capability analysis of the experimental Cardiac Thoracic Ratio (CTR) data (Raw values) and the simulated Cardiac Thoracic Ratio (CTR) data.\par
? To examine the significant difference in the variance of Cardiac Thoracic Ratio (CTR) data of raw and simulated CTR values. II. 
\section[{Literature Review}]{Literature Review}\par
The most commonly and widely used indices are p C (Juan 1974), pk C (Kane 1986), pm C (Hsiang   and Taguchi 1985) and pmk C (Choiward and Owen   1970; Pearn and Kotz and Chen 1994-95) and their generalization for non-normal process suggested  {\ref (Pearn and Kotz, 1995;}\hyperref[b18]{Pearn and Chen 1995)}. \hyperref[b17]{Mukherjee (1995)} studied conceptual approaches to process capability analysis. A number of new approaches to process capability analysis have been attempted and experimented \hyperref[b3]{(Carr 1991;} {\ref Flaig 1996)}. Another index is given by \hyperref[b1]{Boyles (1994)}, when researcher or quality control officer is confronted with processes described by a characteristic whose values are discrete. Therefore, in such cases none of these indices can be used. The indices suggested so far whose assessment is meaningful regardless of whether the studied process in discrete or continuous are those suggested by  {\ref Yeh and Bhaltachiya (1998)}. \hyperref[b0]{Borges and Ho (2001)}, \hyperref[b21]{Perakis and Xekalaki (2002;}\hyperref[b22]{2005)}  In this study, evaluation of cumulative capability characteristics of the experimental CTR values (Raw) and Simulated CTR values using uniform distribution are investigated.\par
In real life application, calculation of proposed capability index boils down to computation of the process yield. To evaluate the process yield, it is necessary to apply a curve fitting method to approximate the quality characteristic distribution ( ) x f . Polansky (1999) used non-parametric approach particularly Kernel density estimation to estimate process yield for both univariate as well as multivariate quality characteristics. Ciarlini, Gigli and Regoliosi (1999) used bootstrap methodology to estimate failed probabilities even in regions not supported by data with accuracy. Independent of the sample variances is useful when data are not nearly normal. The Pearson distribution was implemented (Clement 1989), the Johnson distribution was suggested \hyperref[b6]{(Chou and Polansky 1996;}\hyperref[b7]{Chou, Polansky and Mason 1998;}. Burr distribution was used to describe non-normal process data  {\ref (Castaghola 1996)}.\par
In practice, one may often be faced with processes whose distributions are far from being normal. In this capability study the index and the assumption that the underlying distribution of the examined process is a non-normal form and in particular, exponential. Gunter (1989) observed the experimental distribution arises frequently in industrial processes and were explained in the article  {\ref (Yeh and Bhattachayya 1998)}. The normal and exponential process index is achievable for continuous process however; they are useless when the process is discrete. Poison process index pk C is used in the assessment of discrete process. The properties of are examined in the case where the studied process is described by a poison distribution characteristic with parameter m>0. The uniform process index is achievable for continuous process however; it is useful when the process was discrete. Uniform process index pk C is used in the assessment of discrete process. The properties of pk C are examined in the case where the studied process is described by a uniform distribution characteristic with some parameter a and b (Maiti etal., 2009).\par
In this study chart such as histogram with normal distribution is used to detect the trend behaviour of the CTR distribution outlier for abnormal CTR values. Uniformly simulated data will be compared with the raw CTR values based on capability analysis and variance. Uniform distribution process is simulated to compare with the raw CTR value of chest radiological examination in this study. 
\section[{IV.}]{IV.} 
\section[{Simulation Technique}]{Simulation Technique}\par
Simulation provides a method for checking your understanding of the world around you and helps us to produce better results faster. 
\section[{a) A Study Simulation}]{a) A Study Simulation}\par
In the study of Cardiac Thoracic Ration of Chest X-ray films examination, the raw values of cardiac and thoracic measure shall be computed to obtain the CTR value of patients that undergo the Chest X-ray examination as:V V T C CTR = (1)\par
where V C is the cardiac value and the V T is the thoracic value of the measurements. If the CTR=0.5, the reading is said to be normal with boundary allowances of 0.45 and 0.55 for error of readings accommodation. Hence, the tolerance values are USL=0.55 and LSL=0.45 with the target value? ? ? ? ? ? + = 2 LSL USL T =0.5.\par
()\textbf{2}\par
The study employs simulation technique using   
\section[{Design and Implementation of Simulation}]{Design and Implementation of Simulation}\par
The simulation use in this study follows a uniform distribution process which ranges from 0.43 to 0.71 with 5 number of variable as subgroup measurements for 150 sample random number all together making 750 observations. Excel application package is the implementation medium used for the random number generation. 
\section[{VI.}]{VI.} 
\section[{Variance ctr Raw and Simulated Processes Comparison}]{Variance ctr Raw and Simulated Processes Comparison}\par
Bartlet 'b'-statistic is assumed as test-statistic that is distributed approximately as ? 2 ? distribution when samples are independently drawn from normal population \hyperref[b27]{(Singha, 2002)}. We test that2 2 0 : s r H ? ? = and 2 2 0 : s r H ? ? ?\par
to determine equality of variances \hyperref[b10]{(Gomez and Kwanchai, 1984)} of both raw and simulated CTR values of Chest X-ray measurement. Comparison of the variances of the raw CTR and Simulated CTR value is carried out in this study to investigate the process equality of variances. In this study, the variance of the CTR raw and simulated values are computed and tested for homogeneity based on the Bartlet Test 'b' statistic. The algorithm for the procedure is described by the following algorithm steps (A4). 
\section[{VII.}]{VII.} 
\section[{Research Method}]{Research Method}\par
The source of data for the analysis is primary through raw computation and computer simulation using uniform distribution. The raw data are generated through the measurement values of the cardiac and thoracic of films output of Chest X-ray of patients from the radiological machine process. The ratios of the measurements are computed to obtain various CTR values over time. Inspection Coding Sheet (ICS) is used to randomly generate the samples for the study. Limits are set equal to 3sigma as  
\section[{5}]{5}\par
. 0 = T is based on the specification criteria for non-sensitivity analysis (specificity) while statistical process control is investigated to address process stability. Capability analysis is performed for the two processes. The pattern of the means of the raw and simulated values are detected using exploratory data Analysis (EDA) approach like normal probability plots, empirical CDF functions and Box-plot. In addition, homogeneity of variance of the two processes is investigated based on Bartlet's 'b' statistic. The analysis of data is performed electronically with the aid of statistical software MINITAB version 16.0. 
\section[{VIII.}]{VIII.} 
\section[{Data Analysis and Result}]{Data Analysis and Result}\par
This aspect focuses on exploring data analysis behaviour pattern of Raw and Simulated Cardiac Thoracic Ratio (CTR) values. It also discusses control chart graphs, process capability analysis and the process variance comparison using Bartlet 'b' statistic.   The Boxplot of RCTRv and SCTRv illustrate non deviation in the RCTRv but deviation exists in the SCTRv because of the existence of the spike (whiskers of dispersion). This confirms that there is likelihood of more deviation from the 0.5 CTR standard in the SCTRv compare to the RCTRv.  
\section[{Xbar Chart of Mean}]{Xbar Chart of Mean} 
\section[{Figure 1a}]{Figure 1a}\par
From the fig1a, the aggregate observation of 150 samples indicates that all points of the raw CTR values are falling within control limit confirming the process statistical stability and under control with predicted trend of sensitivity.  
\section[{Process Capability of Mean}]{Process Capability of Mean}\par
(using 95.0\% confidence) 
\section[{Figure 1b}]{Figure 1b}\par
For sample 150, the mean estimated is 0.5654 where the within and overall standard deviation are 0.0222 and 0.0227,  
\section[{Xbar Chart of Mean}]{Xbar Chart of Mean} 
\section[{Figure 2a}]{Figure 2a}\par
From the fig2a, cumulative 150 samples all points of the simulated CTR values are falling within control limits implying process stable and follow a predictable trend. .This implies that the process is using about 39.1\% of the specification band.\par
Hence, the values of The average estimated value of CTR is 0.57 which is 0.02 higher than the upper specification limit. True sensitivity analysis value of about 59.9\% is confirmed fail points among the examined patients while the deviation among the sample measures is 0.023. Both p C and p P are near approximate hence there is little between subgroup variability. 
\section[{g) Bartlet Test 'b' Statistic Computation and Result}]{g) Bartlet Test 'b' Statistic Computation and Result}\par
The computational result of the Bartlet Test 'b' Statistic value do not exceed the Chi-square value, the variance of the raw and the simulated CTR values have unequal variance. 
\section[{IX.}]{IX.} 
\section[{Conclusion}]{Conclusion}\par
After aggregating all the raw computed CTR values and simulated CTR values obtained, it is empirically confirmed that the system is operating under 1.0 -1.3 sigma level for the raw CTR values. Around 28-39\% of the raw CTR values obtained are falling outside the specification limits and 30-45\% of the specification band is being used. In addition, the p pk C C < for all the cumulative raw CTR values suggesting that the process is off centred and is towards the lower specification limit. Therefore, the points are falling outside the upper specification limit which clearly indicates that the variability in the raw CTR process is very high. 
\section[{X.}]{X.} 
\section[{Recommendation}]{Recommendation}\par
Based on the empirical outputs of capability analysis of radiological result of CTR values (raw and simulated), this study therefore recommends that health awareness campaign on slow death resulting from heart failure as a result of absence of early detection of abnormal CTR value among patients should be created by the government and health agencies. Patients should be medically advised on the measure to control and maintain stable CTR. Also on how to adopt better management methods which can subsequently prevent possibility of high CTR and further study should be conducted on large repeated experimental scale to ascertain the reliability of this study. Fellow up study of patients should be undertaken by the cardiologist to reduce the possible health risk that could result from the CTR.\par
for each sample subgroups respectively.\par
Step 2 : Calculated the row total values ? = n i i x 1 and the row average value of the sample subgroups and the mean of the mean of sample subgroup as:? = ? = n i i x n X 1 1\par
and? = = = M j j X M X 1 \textunderscore  1\par
Step 3 : Calculate the sample range and the sample subgroup range;i i i x of MinValue x of MaxValue R ? = and ? = ? = M j j j R M R 1 1\par
Step 4 : Compute the sample variance and standarddeviation 2 1 1 ? = ? ? ? ? ? ? ? ? ? = n i i X X n ?\par
Step 5 : Evaluate the limits USL, CL and LSL for the sample mean? = + = R A X USL 2 ? = ? = R A X LSL 2 = = X CL\par
Step 6 : Evaluate the limits USL, LSL and CT for the sample range for = 0.577, , when n=5 from the SQC table readings.    ? = R D USL 4 ? = R D LSL 3 ? = R CL for 2 A = 0.? = ? ? ? ? ? ? ? ? = n i i X X n s and 2 1 2 2 1 ? = ? ? ? ? ? ? ? ? = n i i X X n s\par
Step 3 : Calculate the ( )   ... 2 , 1 1 2 2 = ? ? = ? i K N S n S i i Step 4 : Compute ( ) ( ) ? ? ? ? = 2 2 log 1 log i i S n S k n Q Step 5 : Calculate ( ) ( ) ( ) ? ? ? ? ? ? ? ? ? ? + = ? K N n k H i 1 1 1 1 3( ) K N S n S i i ? ? = ? 2 2 1 = ( ) ( ) K N S n S n ? ? + ? 2 2 2 2 2 1 1 1 = ( )( ) ( )( ) = ? ? + ? 5 1500 193 . 1 1 5 674 . 0 1 5 0.0515 ( ) ( ) ? ? ? ? = 2 2 log 1 log i i S n S k n Q = ( ) ( ) ( )( ) ( )( ) [ ] 193 . 1 4 674 . 0 4 0515 . 0 log 2 5 + ? ? Q = ( )( ) [ ] 4675 . 7 2882 . 1 3 ? ? = -11.3321 ( ) ( ) ( ) ? ? ? ? ? ? ? ? ? ? + = ? K N n k H i 1 1 1 1 3 1 1 = ( ) ( )( ) ( ) ? ? ? ? ? ? ? ? ? + 5 150 1 4 4 1 1 2 3 1 1 H = ( ) ( ) 006896 . 0 0625 . 0 33 . 1 145 1 16 1 3 1 1 ? = ? ? ? ? ? ? ? + =0.07395 H Q b 3026 . 2 = = ? ? ? ? ? ? ?\textbf{07395}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}(}\end{figure}
            \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.13453237410071942\textwidth}P{0.05911270983213429\textwidth}P{0.15083932853717028\textwidth}P{0.03261390887290168\textwidth}P{0.01630695443645084\textwidth}P{0.04280575539568345\textwidth}P{0.16103117505995201\textwidth}P{0.16918465227817747\textwidth}P{0.08357314148681055\textwidth}}
\tabcellsep \tabcellsep \multicolumn{5}{l}{Empirical CDF of RCTRv, SCTRv}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.50\tabcellsep 0.55\tabcellsep 0.60\tabcellsep 0.65\tabcellsep 0.70\\
\tabcellsep \multicolumn{2}{l}{RCT Rv}\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{SCT Rv}\tabcellsep RC TRv\\
\tabcellsep 100\tabcellsep \tabcellsep 100\tabcellsep \tabcellsep \tabcellsep Mean StDev\tabcellsep 0.5673 0.03372\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep N\tabcellsep 150\\
\tabcellsep 80\tabcellsep \tabcellsep 80\tabcellsep \tabcellsep \tabcellsep Mean SC TRv 0.5790\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep StDev\tabcellsep 0.03902\\
Percent\tabcellsep 60\tabcellsep \tabcellsep 60\tabcellsep \tabcellsep \tabcellsep N\tabcellsep 150\\
\tabcellsep 40\tabcellsep \tabcellsep 40\tabcellsep \tabcellsep \\
\tabcellsep 20\tabcellsep \tabcellsep 20\tabcellsep \tabcellsep \\
\tabcellsep 0\tabcellsep \tabcellsep 0\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{0.55 d) Boxplot of RCTRv and SCTRv 0.50 S CTRv RCTRv}\tabcellsep \multicolumn{5}{l}{0.60 Probability 0.65 Boxplot of RCTRv, SCTRv}\\
\tabcellsep \multicolumn{2}{l}{RCT Rv 0.50}\tabcellsep 0.55\tabcellsep Data\tabcellsep \multicolumn{2}{l}{SCT Rv 0.60}\tabcellsep 0.65\tabcellsep Mean StDev RC TRv 0.5673 0.70 0.03372\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep N\tabcellsep 150\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep A D\tabcellsep 0.368\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep P-Value\tabcellsep 0.426\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep SC TRv\\
Percent\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Mean StDev N A D\tabcellsep 0.5790 0.03902 150 0.222\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep P-Value\tabcellsep 0.826\end{longtable} \par
  {\small\itshape [Note: Normal -95\% CIFigure 1 : Normality Plot of RCTRv and SCTRv The probability plots of raw and simulated CTR Normal Figure 2 :]} 
\caption{\label{tab_0}Plot of RCTRv, SCTRv}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.027904832165252674\textwidth}P{0.29942825525636296\textwidth}P{0.0006270748801180376\textwidth}P{0.0003135374400590188\textwidth}P{0.015049797122832902\textwidth}P{0.06521578753227592\textwidth}P{0.0467170785687938\textwidth}P{0.010973810402065657\textwidth}P{0.0012541497602360752\textwidth}P{0.0003135374400590188\textwidth}P{0.0003135374400590188\textwidth}P{0.15802286978974547\textwidth}P{0.0012541497602360752\textwidth}P{0.0006270748801180376\textwidth}P{0.0003135374400590188\textwidth}P{0.06709701217263002\textwidth}P{0.0617668756916267\textwidth}P{0.026650682405016595\textwidth}P{0.03731095536702324\textwidth}P{0.0028218369605311692\textwidth}P{0.016617484323128\textwidth}P{0.0003135374400590188\textwidth}P{0.0003135374400590188\textwidth}P{0.0003135374400590188\textwidth}P{0.0003135374400590188\textwidth}P{0.0009406123201770564\textwidth}P{0.0003135374400590188\textwidth}P{0.0006270748801180376\textwidth}P{0.0018812246403541128\textwidth}P{0.004389524160826263\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Process Capability of Mean\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep (using 95.0\% confidence)\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{LSL}\tabcellsep \tabcellsep Target\tabcellsep USL\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{P rocess D ata}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep W ithin\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{LS L}\tabcellsep \multicolumn{2}{l}{0.45}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep O v erall\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{6}{l}{T arget U S L S ample M ean S ample N S tD ev (Within) S tD ev (O v erall) 0.0390218 0.5 0.038998 150 0.57904 0.55}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep C P U C P L U pper C L Low er C L C p P otential (Within) C apability -1.49 6.62 2.85 2.27 2.56\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep C pk\tabcellsep -1.49\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Low er C L\tabcellsep -1.85\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep U pper C L\tabcellsep -1.13\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep O v erall C apability\\
Year 2014 48\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{5}{l}{P P M < LS L O bserv ed P erformance 0.00 P P M > U S L 786666.67 P P M T otal 786666.67}\tabcellsep \tabcellsep \multicolumn{5}{l}{0.630 E xp. O v erall P erformance 0.585 0.540 468.34 0.495 E xp. Within P erformance 0.450 P P M < LS L P P M < LS L 471.72 P P M > U S L 771760.32 P P M > U S L 771623.09 P P M T otal 772228.65 P P M T otal 772094.80}\tabcellsep 0.675\tabcellsep Low er C L U pper C L P P L P P U P pk Low er C L U pper C L C pm P p Low er C L\tabcellsep 2.27 2.85 6.61 -1.49 -1.49 -1.85 -1.13 2.56 1.13 1.07\\
XIV Issue I Version I\tabcellsep \multicolumn{15}{l}{Figure 2b For cumulative sample 150, the mean estimated is 0.5790 where the within and overall standard deviation are 0.0289 and 0.0290, 56 . 2 = p C , 49 . 1 ? = pk C , 12 . 1 = pm C since the p pk C C < , the process is off centred and is toward the lower specification limits. The percentage of the specification band that the process uses up is \% 1 . 39 100 * ) / 1 ( = = p C P . This implies that the process is using about 39.1\% of the specification band. Therefore, the values of 56 . 2 = p C and 56 . 2 = p P are equal therefore the process has little between subgroup variability. The empirical analysis results and findings of process capability analysis of raw and simulated CTR values are summarized in the table below:}\\
Global Journal of Researches in Engineering ( ) Volume I\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{n µ ? C C C p p pk pm P ? ? ? ? p C 1 ? ? ? ? = P p C ? ? ? ? ? 1 p C P ? ? ? ? p *}\tabcellsep \multicolumn{2}{l}{100}\tabcellsep \tabcellsep \multicolumn{5}{l}{Raw Cardiac Thoracic Ratio Value (RCTRv) 50 75 100 0.5654 25 0.5658 0.5745 0.5681 0.0323 0.0276 0.0275 0.0226 4.42 2.99 4.40 4.27 -2.14 -1.27 -2.16 -1.58 1.07 1.21 1.19 1.12 2.09 2.66 2.66 2.97 0.226 0.334 0.227 0.234 22.6\% 33.4\% 22.7\% 23.4\% p p P C < p p P C < p p P C < p p P C <}\tabcellsep 125 0.5648 0.0229 4.55 -2.15 1.26 2.94 0.2180 22\% p C <\tabcellsep p P\tabcellsep 150 0.0227 4.42 -1.52 1.22 2.97 0.2260 22.6\% p P p C < 0.5654\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{10}{l}{Source: Results extracted from Minitab 16.0}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{12}{l}{For cumulative sample 150, the mean estimated}\tabcellsep uses up is\tabcellsep P\tabcellsep =\tabcellsep 1 (\tabcellsep /\tabcellsep C\tabcellsep p\tabcellsep )\tabcellsep *\tabcellsep 100\tabcellsep =\tabcellsep 39\tabcellsep \% 1 .\tabcellsep . This implies\\
\tabcellsep \multicolumn{15}{l}{is 0.5790 where the within and overall standard deviation}\tabcellsep that the process is using about 39.1\% of the\\
\tabcellsep \multicolumn{7}{l}{are 0.0289 and 0.0290,}\tabcellsep C\tabcellsep p\tabcellsep =\tabcellsep 2\tabcellsep .\tabcellsep 56\tabcellsep ,\tabcellsep C\tabcellsep pk\tabcellsep =\tabcellsep 1 ?\tabcellsep 49 . ,\tabcellsep specification band. Values of p C and p P are barely\\
\tabcellsep C\tabcellsep pm\tabcellsep =\tabcellsep . 1\tabcellsep 12\tabcellsep since the\tabcellsep \multicolumn{9}{l}{p C < , the process is off pk C}\tabcellsep equal hence there is substantial between subgroup\\
\tabcellsep \multicolumn{15}{l}{centred and is toward the lower specification limits. The}\tabcellsep variability.\\
\tabcellsep \multicolumn{15}{l}{percentage of the specification band that the process}\\
\tabcellsep \multicolumn{6}{l}{© 2014 Global Journals Inc. (US)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.3638183217859892\textwidth}P{0.0032717474980754426\textwidth}P{0.0032717474980754426\textwidth}P{0.007852193995381063\textwidth}P{0.05561970746728252\textwidth}P{0.003926096997690531\textwidth}P{0.0006543494996150885\textwidth}P{0.001308698999230177\textwidth}P{0.03468052347959969\textwidth}P{0.001308698999230177\textwidth}P{0.016358737490377213\textwidth}P{0.003926096997690531\textwidth}P{0.007852193995381063\textwidth}P{0.005889145496535797\textwidth}P{0.00916089299461124\textwidth}P{0.05758275596612778\textwidth}P{0.049076212471131635\textwidth}P{0.03468052347959969\textwidth}P{0.0399153194765204\textwidth}P{0.05365665896843726\textwidth}P{0.03402617397998461\textwidth}P{0.011123941493456505\textwidth}P{0.01308698999230177\textwidth}P{0.015704387990762125\textwidth}P{0.008506543494996152\textwidth}P{0.002617397998460354\textwidth}P{0.008506543494996152\textwidth}P{0.002617397998460354\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Simulated Cardiac Thoracic Ratio Value (SCTRv)\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{n}\tabcellsep \tabcellsep \tabcellsep 25\tabcellsep 50\tabcellsep 75\tabcellsep 100\tabcellsep 125\tabcellsep 150\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{µ}\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.5651\tabcellsep 0.5724\tabcellsep 0.5768\tabcellsep 0.5771\tabcellsep 0.5771\tabcellsep 0.5790\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{?}\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0.0422\tabcellsep 0.0413\tabcellsep 0.0400\tabcellsep 0.0280\tabcellsep 0.0280\tabcellsep 0.0227\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{C}\tabcellsep p\tabcellsep \tabcellsep \tabcellsep 2.21\tabcellsep 2.38\tabcellsep 2.56\tabcellsep 2.62\tabcellsep 2.61\tabcellsep 2.56\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{C}\tabcellsep \multicolumn{2}{l}{pk}\tabcellsep \tabcellsep -2.14\tabcellsep -1.08\tabcellsep -1.28\tabcellsep -1.42\tabcellsep -1.42\tabcellsep -1.49\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{C}\tabcellsep \multicolumn{2}{l}{pm}\tabcellsep \tabcellsep 1.07\tabcellsep 1.19\tabcellsep 1.15\tabcellsep 1.16\tabcellsep 1.16\tabcellsep 1.22\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{p P}\tabcellsep \tabcellsep \tabcellsep 2.09\tabcellsep 2.66\tabcellsep 2.66\tabcellsep 2.97\tabcellsep 2.94\tabcellsep 2.97\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ? ? ? ?\tabcellsep \multicolumn{3}{l}{p 1 C}\tabcellsep ? ? ? ?\tabcellsep 0.4516\tabcellsep 0.4201\tabcellsep 0.3908\tabcellsep 0.3821\tabcellsep 0.3827\tabcellsep 0.3907\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{P}\tabcellsep =\tabcellsep ? ? ? ?\tabcellsep \multicolumn{2}{l}{1 p C}\tabcellsep ? ? ? ?\tabcellsep *\tabcellsep 100\tabcellsep 45.2\%\tabcellsep 42\%\tabcellsep 39.1\%\tabcellsep 38.2\%\tabcellsep 38.2\%\tabcellsep 39.1\%\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{5}{l}{p C ?}\tabcellsep p P\tabcellsep p C <\tabcellsep p P\tabcellsep p C <\tabcellsep p P\tabcellsep p C <\tabcellsep p P\tabcellsep p C <\tabcellsep p P\tabcellsep p C <\tabcellsep p P\tabcellsep p C <\tabcellsep p P\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{8}{l}{Source: Results extracted from Minitab 16.0}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{12}{l}{For cumulative sample 150, the mean estimate}\\
\multicolumn{16}{l}{is 0.5790 where the within and overall standard deviation}\\
\multicolumn{16}{l}{are 0.0289 and 0.0290,}\tabcellsep C\tabcellsep p\tabcellsep =\tabcellsep 2\tabcellsep .\tabcellsep 56\tabcellsep ,\tabcellsep C\tabcellsep pk\tabcellsep =\tabcellsep 49 . 1 ?\tabcellsep ,\\
C\tabcellsep pm\tabcellsep =\tabcellsep \multicolumn{2}{l}{. 1}\tabcellsep \multicolumn{3}{l}{12}\tabcellsep \multicolumn{8}{l}{since the}\tabcellsep pk C <\tabcellsep C\tabcellsep p\tabcellsep , the process is off\\
\multicolumn{16}{l}{centred and is toward the lower specification limits. The}\\
\multicolumn{16}{l}{percentage of the specification band that the process}\\
\multicolumn{7}{l}{uses up is}\tabcellsep \multicolumn{2}{l}{P}\tabcellsep \tabcellsep \multicolumn{2}{l}{=}\tabcellsep \multicolumn{2}{l}{1 (}\tabcellsep /\tabcellsep C\tabcellsep p\tabcellsep )\tabcellsep *\tabcellsep 100\tabcellsep =\tabcellsep . 39\tabcellsep \% 1\tabcellsep . This implies\\
\multicolumn{16}{l}{that the process is using about 39.1\% of the}\\
\multicolumn{16}{l}{specification band. Values of p C and p P are barely}\\
\multicolumn{16}{l}{equal hence there is substantial between subgroup}\\
\multicolumn{6}{l}{variability.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{12}{l}{For the total sample 150, the mean value}\\
\multicolumn{16}{l}{estimated is 0.5790 where the within and overall}\\
\multicolumn{16}{l}{standard deviation are 0.0289 and 0.0290,}\tabcellsep C\tabcellsep p\tabcellsep =\tabcellsep . 2\tabcellsep 56\tabcellsep ,\\
C\tabcellsep pk\tabcellsep =\tabcellsep \multicolumn{6}{l}{49 . 1 ?}\tabcellsep \multicolumn{2}{l}{,}\tabcellsep \multicolumn{2}{l}{C}\tabcellsep \multicolumn{2}{l}{pm}\tabcellsep =\tabcellsep 1\tabcellsep 12 .\tabcellsep since the\tabcellsep pk C <\tabcellsep C\tabcellsep p\tabcellsep ,the\\
\multicolumn{16}{l}{process is off centred and is toward the lower}\\
\multicolumn{16}{l}{specification limits. The percentage of the specification}\\
\multicolumn{3}{l}{band}\tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{that}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{the}\tabcellsep process\tabcellsep uses\tabcellsep up\tabcellsep is\\
P\tabcellsep =\tabcellsep 1 (\tabcellsep /\tabcellsep \multicolumn{2}{l}{C}\tabcellsep p\tabcellsep )\tabcellsep \multicolumn{2}{l}{*}\tabcellsep \multicolumn{4}{l}{100}\tabcellsep \multicolumn{2}{l}{=}\tabcellsep \% 1 . 39\end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.48999999999999994\textwidth}P{0.005\textwidth}P{0.005\textwidth}P{0.095\textwidth}P{0.13999999999999999\textwidth}P{0.005\textwidth}P{0.005\textwidth}P{0.03\textwidth}P{0.045\textwidth}P{0.03\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Compute b}\tabcellsep \\
\multicolumn{8}{l}{A4 : Bartlet 'b' Statistic computational results for Raw and Simulated CTR values}\\
\multicolumn{3}{l}{Raw CTR}\tabcellsep Variance\tabcellsep \multicolumn{4}{l}{Simulated CTR}\tabcellsep Variance\\
1 s\tabcellsep 2\tabcellsep \tabcellsep 0.674\tabcellsep s\tabcellsep 2\tabcellsep 2\tabcellsep 1.193\\
\multicolumn{2}{l}{1 log s}\tabcellsep 2\tabcellsep -0.172\tabcellsep \multicolumn{3}{l}{log s}\tabcellsep 2\tabcellsep 2\tabcellsep 0.0765\end{longtable} \par
 
\caption{\label{tab_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.0306772100567721\textwidth}P{0.03550283860502839\textwidth}P{0.031021897810218975\textwidth}P{0.03136658556366585\textwidth}P{0.031021897810218975\textwidth}P{0.029987834549878342\textwidth}P{0.031021897810218975\textwidth}P{0.031021897810218975\textwidth}P{0.03136658556366585\textwidth}P{0.03412408759124087\textwidth}P{0.03136658556366585\textwidth}P{0.032400648824006485\textwidth}P{0.007238442822384428\textwidth}P{0.007927818329278183\textwidth}P{0.032400648824006485\textwidth}P{0.03274533657745336\textwidth}P{0.032400648824006485\textwidth}P{0.031021897810218975\textwidth}P{0.032055961070559615\textwidth}P{0.032055961070559615\textwidth}P{0.031021897810218975\textwidth}P{0.032400648824006485\textwidth}P{0.03171127331711273\textwidth}P{0.032400648824006485\textwidth}P{0.032400648824006485\textwidth}P{0.032400648824006485\textwidth}P{0.0306772100567721\textwidth}P{0.03274533657745336\textwidth}P{0.00551500405515004\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{5}{l}{Appendix B}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{B3}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 0.003\tabcellsep 0.010\tabcellsep 0.005\tabcellsep 0.005\tabcellsep 0.009\tabcellsep 0.010\tabcellsep 0.009\tabcellsep 0.010\tabcellsep 0.011\tabcellsep 0.010\tabcellsep 0.007\tabcellsep .\tabcellsep .\tabcellsep 0.006\tabcellsep 0.011\tabcellsep 0.010\tabcellsep 0.001\tabcellsep 0.007\tabcellsep 0.006\tabcellsep 0.006\tabcellsep 0.003\tabcellsep 0.005\tabcellsep 0.012\tabcellsep 0.012\tabcellsep 0.002\tabcellsep 0.008\tabcellsep 0.005\tabcellsep \\
\tabcellsep 0.14\tabcellsep 0.21\tabcellsep 0.19\tabcellsep 0.17\tabcellsep 0.22\tabcellsep 0.22\tabcellsep 0.23\tabcellsep 0.24\tabcellsep 0.26\tabcellsep 0.22\tabcellsep 0.18\tabcellsep .\tabcellsep .\tabcellsep 0.21\tabcellsep 0.25\tabcellsep 0.24\tabcellsep 0.06\tabcellsep 0.18\tabcellsep 0.2\tabcellsep 0.21\tabcellsep 0.15\tabcellsep 0.15\tabcellsep 0.24\tabcellsep 0.26\tabcellsep 0.12\tabcellsep 0.23\tabcellsep 0.16\tabcellsep \\
Year 2014\tabcellsep 3.07 0.61\tabcellsep 2.76 0.55\tabcellsep 2.73 0.55\tabcellsep 2.55 0.51\tabcellsep 2.5 0.5\tabcellsep 2.84 0.57\tabcellsep 2.79 0.56\tabcellsep 3.05 0.61\tabcellsep 2.65 0.53\tabcellsep 3.06 0.61\tabcellsep 2.69 0.54\tabcellsep . .\tabcellsep . .\tabcellsep 2.86 0.57\tabcellsep 2.95 0.59\tabcellsep 2.78 0.56\tabcellsep 2.86 0.57\tabcellsep 2.87 0.57\tabcellsep 2.77 0.55\tabcellsep 2.82 0.56\tabcellsep 3.27 0.65\tabcellsep 3.16 0.63\tabcellsep 2.97 0.59\tabcellsep 3.07 0.61\tabcellsep 2.55 0.51\tabcellsep 3.04 0.61\tabcellsep 3.17 0.63\tabcellsep \\
XIV Issue I Version I 54\tabcellsep 0.64 0.65 0.59 0.66 0.52\tabcellsep 0.65 0.57 0.64 0.47 0.43\tabcellsep 0.55 0.64 0.56 0.54 0.44\tabcellsep 0.6 0.56 0.43 0.48 0.48\tabcellsep 0.64 0.42 0.47 0.42 0.54\tabcellsep 0.45 0.47 0.63 0.68 0.61\tabcellsep 0.49 0.68 0.45 0.55 0.62\tabcellsep 0.48 0.67 0.72 0.64 0.54\tabcellsep 0.45 0.51 0.54 0.44 0.7\tabcellsep 0.62 0.7 0.52 0.51 0.72\tabcellsep 0.64 0.62 0.51 0.46 0.47\tabcellsep . . . .\tabcellsep . . . .\tabcellsep 0.55 0.54 0.68 0.47 0.62\tabcellsep 0.53 0.67 0.73 0.48 0.54\tabcellsep 0.68 0.62 0.51 0.43 0.53\tabcellsep 0.56 0.58 0.59 0.54 0.6\tabcellsep 0.47 0.65 0.61 0.63 0.5\tabcellsep 0.51 0.54 0.61 0.66 0.46\tabcellsep 0.67 0.55 0.55 0.46 0.59\tabcellsep 0.65 0.69 0.67 0.56 0.7\tabcellsep 0.56 0.7 0.71 0.6 0.59\tabcellsep 0.73 0.52 0.53 0.49 0.69\tabcellsep 0.46 0.63 0.71 0.55 0.72\tabcellsep 0.54 0.56 0.52 0.44 0.49\tabcellsep 0.61 0.47 0.66 0.59 0.7\tabcellsep 0.71 0.56 0.63 0.57 0.69\tabcellsep \\
I ( ) Volume Global Journal of Researches in Engineering\tabcellsep 0.013 1 0.71 0.7 0.76 3.25 0.65 0.29 Sample Sub 1 1 0.62 0.47\tabcellsep 0.005 2 0.73 0.59 0.56 3.23 0.65 0.17 2 0.65 0.7\tabcellsep 0.008 3 0.74 0.57 0.63 3.13 0.63 0.23 3 0.67 0.51\tabcellsep 0.013 4 0.78 0.68 0.52 3.11 0.62 0.27 4 0.51 0.63\tabcellsep 0.009 5 0.6 0.69 0.51 3.05 0.61 0.2 5 0.72 0.53\tabcellsep 0.007 6 0.73 0.55 0.57 2.99 0.6 0.21 6 0.63 0.52\tabcellsep 0.008 7 0.71 0.61 0.46 2.94 0.59 0.25 7 0.6 0.56\tabcellsep 0.001 8 0.51 0.59 0.57 2.71 0.54 0.09 8 0.53 0.52\tabcellsep 0.006 9 0.68 0.54 0.48 2.87 0.57 0.2 9 0.54 0.63\tabcellsep 0.007 10 0.64 0.61 0.56 3.02 0.6 0.21 10 0.71 0.49\tabcellsep 0.006 11 0.5 0.52 0.66 2.76 0.55 0.18 11 0.61 0.48\tabcellsep . . . . .\tabcellsep . . . . . .\tabcellsep 0.005 137 0.64 0.72 0.53 3.1 0.62 0.19 137 0.59 0.62\tabcellsep 0.001 138 0.61 0.58 0.57 3.03 0.61 0.08 138 0.66 0.62\tabcellsep 0.002 139 0.46 0.52 0.53 2.59 0.52 0.11 139 0.57 0.5\tabcellsep 0.002 140 0.55 0.5 0.5 2.71 0.54 0.1 140 0.6 0.57\tabcellsep 0.008 141 0.74 0.64 0.5 3.03 0.61 0.24 141 0.58 0.57\tabcellsep 0.007 142 0.71 0.66 0.51 3.08 0.62 0.2 142 0.66 0.54\tabcellsep 0.003 143 0.61 0.68 0.52 3 0.6 0.16 143 0.6 0.58\tabcellsep 0.005 144 0.55 0.67 0.58 2.91 0.58 0.19 144 0.63 0.48\tabcellsep 0.009 145 0.56 0.67 0.52 3.01 0.6 0.21 145 0.73 0.53\tabcellsep 0.008 146 0.56 0.55 0.52 2.86 0.57 0.23 146 0.73 0.5\tabcellsep 0.003 147 0.57 0.62 0.62 2.98 0.6 0.15 147 0.66 0.52\tabcellsep 0.001 148 0.59 0.56 0.63 2.87 0.57 0.1 148 0.57 0.53\tabcellsep 0.000 149 0.6 0.59 0.61 3 0.6 0.05 149 0.62 0.57\tabcellsep 0.007 150 0.59 0.75 0.62 3.06 0.61 0.22 150 0.58 0.53\tabcellsep Source: Chest X-\end{longtable} \par
 
\caption{\label{tab_5}Sub 2 Sub 3 Sub 4 Sub 5 Total Mean Range Sample Sub 1 Sub 2 Sub 3 Sub 4 Sub 5 Total Mean Range Variance}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_6}ray Radiological Readings 2011 Source: Chest X-ray Radiological Simulation Readings 2011 Raw Computed Data Simulated Cardio-Thoracic Ratio Using Uniform Distribution Radiographic Films Readings of Chest X-ray Radiographic Films Readings of Chest X-ray For congestive Heart Failure Cardiomegaly Conditions For congestive Heart Failure Cardiomegaly Conditions Cadio-Thoracic Ratio Variance Cadio-Thoracic Ratio}\end{figure}
 			\footnote{© 2014 Global Journals Inc. (US)} 			\footnote{? value, both processes do not have equal variance.© 2014 Global Journals Inc. (US)} 		 		\backmatter  			 			 			  				\begin{bibitemlist}{1}
\bibitem[Ribeiro et al.]{b25}\label{b25} 	 		\textit{},  		 			S M Ribeiro 		,  		 			Jose 		,  		 			M Renato 		,  		 			S Roberto 		,  		 			J S F Francisco 		,  		 			H Domingo 		,  		 			A 		,  		 			Beatriz 		.  		 	 
\bibitem[Borges and Ho ()]{b0}\label{b0} 	 		‘A fraction defective based capability index’.  		 			W Borges 		,  		 			L L Ho 		.  	 	 		\textit{Quality and Reliability Engineering International}  		2001. 17 p. .  	 
\bibitem[Vännman ()]{b30}\label{b30} 	 		‘A general class of capability indices in the case of asymmetric tolerances’.  		 			K Vännman 		.  	 	 		\textit{Communications in Statistics-Theory and Methods}  		1997. 26 p. .  	 
\bibitem[Vännman et al. ()]{b29}\label{b29} 	 		‘A graphical method to control process capability’.  		 			K. ; H-J Vännman 		,  		 			P-Th Lenz 		,  		 			Wilrich 		.  	 	 		\textit{Frontiers in Statistical Quality Control}  		2001. Physica-Verlag.  (6)  p. .  	 
\bibitem[Carr ()]{b3}\label{b3} 	 		\textit{A new Process capability index: parts per million},  		 			W A Carr 		.  		1991. Quality Progress. 24 p. 152.  	 
\bibitem[Perakis and Xekalaki ()]{b22}\label{b22} 	 		‘A process capability index for discrete processes’.  		 			M Perakis 		,  		 			E Xekalaki 		.  	 	 		\textit{Journal of Statistical Computation and Simulation}  		2005. 75  (3)  p. .  	 
\bibitem[Perakis and Xekalaki ()]{b21}\label{b21} 	 		‘A process capability index that is based on the proportion of conformance’.  		 			M Perakis 		,  		 			E Xekalaki 		.  	 	 		\textit{Journal of Statistical Computation and Simulation}  		2002. 72  (9)  p. .  	 
\bibitem[Yeh and Bhattacharya ()]{b32}\label{b32} 	 		‘A robust process capability index’.  		 			A B Yeh 		,  		 			S Bhattacharya 		.  	 	 		\textit{Communications in Statistics"-Simulation and Computation}  		1998. 27  (2)  p. .  	 
\bibitem[Choi and Owen ()]{b5}\label{b5} 	 		‘A study of a new process capability index’.  		 			B C Choi 		,  		 			D B Owen 		.  	 	 		\textit{Communications in Statistics" -Theory and Methods}  		1990. 19 p. .  	 
\bibitem[Vännman ()]{b28}\label{b28} 	 		‘A unified approach to capability indices’.  		 			K Vännman 		.  	 	 		\textit{Statistica Sinica}  		1995. 5 p. .  	 
\bibitem[
			BM
		 ()]{b26}\label{b26} 	 		\textit{Accuracy of chest radiography plus electrocardiogram in diagnosis of hypertrophy in hypertension},  		 			BM 		.  		2012. Sociedade Brasileira De Cardiologia MCMXLIII. p. .  	 
\bibitem[Pearn and Kotz ()]{b20}\label{b20} 	 		‘Application of elements' method for calculating second and thirdgeneration process capability indices for non-normal Pearsonian populations’.  		 			W L Pearn 		,  		 			S Kotz 		.  	 	 		\textit{Quality Engineering}  		1994-95. 7 p. .  	 
\bibitem[James ()]{b13}\label{b13} 	 		\textit{Design, use and performance of statistical control chart for process Improvement},  		 			C B James 		.  		2001. Boston USA. p. .  		 			Engineering centre, Northern University 		 	 
\bibitem[Pearn et al. ()]{b19}\label{b19} 	 		‘Distributional and inferential properties of process capability indices’.  		 			W L Pearn 		,  		 			S Kotz 		,  		 			N L Johnson 		.  	 	 		\textit{Journal of Quality Technology}  		1992. 24 p. .  	 
\bibitem[Polansky et al. ()]{b23}\label{b23} 	 		‘Estimating process capability indices for a I 11. truncated distribution’.  		 			A M Polansky 		,  		 			Y M Chou 		,  		 			R L Mason 		.  	 	 		\textit{Quality Engineering}  		1998. 11 p. .  	 
\bibitem[Polansky et al. ()]{b24}\label{b24} 	 		‘Estimating process capability indices for a truncated distribution’.  		 			A M Polansky 		,  		 			Y M Chou 		,  		 			R L Mason 		.  	 	 		\textit{Quality Engineering}  		1998. 11 p. .  	 
\bibitem[Pearn and Chen ()]{b18}\label{b18} 	 		‘Estimating process capability indices for non-normal Pearsonian populations’.  		 			W L Pearn 		,  		 			K S Chen 		.  	 	 		\textit{Quality and Reliability Engineering International}  		1995. 11 p. .  	 
\bibitem[Castagliola ()]{b4}\label{b4} 	 		‘Evaluation of non-normal process capability indices using Burr's Distribution’.  		 			P Castagliola 		.  	 	 		\textit{Quality Engineering}  		1996. 8 p. .  	 
\bibitem[Vännman ()]{b31}\label{b31} 	 		‘Families of capability indices for one-sided specification limits’.  		 			K Vännman 		.  	 	 		\textit{Statistics}  		1998. 31 p. .  	 
\bibitem[Chou and Polansky ()]{b6}\label{b6} 	 		‘Fitting SPC data using a sample quantile ratio’.  		 			Y M Chou 		,  		 			A M Polansky 		.  	 	 		\textit{Proceedings of the Section on Quality and Productivity, American StatisticaAssociation},  				 (the Section on Quality and Productivity, American StatisticaAssociation)  		1996. p. .  	 
\bibitem[Hsiang and Taguchi ()]{b12}\label{b12} 	 		 			T C Hsiang 		,  		 			G Taguchi 		.  		\textit{Tutorial on quality control and assurance -The Taguchi Methods},  				 (Las Vegas, Nevada)  		1985. American Statistical Association. p. 188.  	 	 (Joint Meeting of the) 
\bibitem[Singha ()]{b27}\label{b27} 	 		\textit{Introductory to statistical thinking in quality Bio-Statistics},  		 			P Singha 		.  		2002. NewDelhi, India: Prentice-Hall.  	 	 (4 th edition) 
\bibitem[Juran ()]{b14}\label{b14} 	 		\textit{Juran's Quality Control Handbook},  		 			J M Juran 		.  		1974. New York, USA: McGraw-Hill.  	 	 (3rd ed.) 
\bibitem[Kaminsky et al. ()]{b15}\label{b15} 	 		 			F C Kaminsky 		,  		 			R A Dovich 		,  		 			R J Burke 		.  		\textit{Process capability indices: now and in the future},  				 (ess capability indices: now and in the futureQuality Progress)  		1998. 10 p. .  	 
\bibitem[Kane ()]{b16}\label{b16} 	 		‘Process capability indices’.  		 			V E Kane 		.  	 	 		\textit{Journal of Quality Technology}  		1986. 18 p. .  	 
\bibitem[Mukherjee ()]{b17}\label{b17} 	 		‘Process capability indices and associated inference problems’.  		 			S P Mukherjee 		.  	 	 		\textit{Proceedings of the International Conference on Statistical Methods and Statistical Computation},  				 (the International Conference on Statistical Methods and Statistical ComputationSeoul, South Korea)  		1995. p. .  	 
\bibitem[Deleryd and Vännman ()]{b9}\label{b9} 	 		‘Process capability plots-A quality improvement tool’.  		 			M Deleryd 		,  		 			K Vännman 		.  	 	 		\textit{Quality and Reliability Engineering International}  		1999. 15 p. .  	 
\bibitem[Boyles ()]{b1}\label{b1} 	 		‘Process capability with asymmetric tolerances’.  		 			R A Boyles 		.  	 	 		\textit{Communications in Statistics" -Simulation and Computation}  		1994. 23 p. .  	 
\bibitem[Gomez and Kwanchai ()]{b10}\label{b10} 	 		\textit{Statistical procedures for agricultural research},  		 			A Gomez 		,  		 			A G Kwanchai 		.  		1984. New York, USA: John Wiley and Sons, Inc. 605 Third Avenue.  	 
\bibitem[Boyles ()]{b2}\label{b2} 	 		‘Taguchi capability index’.  		 			R A Boyles 		.  	 	 		\textit{Journal of Quality Technology}  		1991. 23 p. .  	 
\bibitem[Ciarlini et al. ()]{b8}\label{b8} 	 		‘The computation of accuracy of quality parameters by means of a Monte Carlo simulation’.  		 			P Ciarlini 		,  		 			A Gigli 		,  		 			G Regoliosi 		.  	 	 		\textit{Communications in Statistics-Simulation and Computation}  		1999. 28 p. .  	 
\bibitem[Hubele ()]{b11}\label{b11} 	 		‘The effect of pooled and un-pooled variance estimators on C pm When Using Subsamples’.  		 			S Hubele 		,  		 			VännmanK 		.  	 	 		\textit{Journal of Quality Technology}  		2004. 36 p. .  	 
\bibitem[Chou et al. ()]{b7}\label{b7} 	 		‘Transforming non-normal data to normality in statistical process control’.  		 			Y M Chou 		,  		 			A M Polansky 		,  		 			R L Mason 		.  	 	 		\textit{Journal of Quality Technology}  		1998. 30 p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
