Six Sigma Quality Using R: Tools and Training Emilio L. Cano
This document discusses using the statistical software R for Six Sigma quality improvement projects. It introduces Six Sigma methodology and the DMAIC problem-solving strategy. The presentation outlines how R can be used for Six Sigma tools and analysis, highlights useful R packages for Six Sigma, and describes efforts to spread the use of R for quality improvement.
These slides were presented at the Use R! 2011 Conference, held in the University of Warwick (Coventry) in August 2011. It was one of the presentations of the Lightning Talks session.
Energy-efficient technology investments using a decision support system frame...Emilio L. Cano
This document presents an integrated framework for decision support systems using R. It describes using R and related packages to represent stochastic energy optimization problems, generate input files for solvers, analyze results, and produce reproducible reports. Stochastic models are developed and solved within this framework. The framework allows statistical analysis, graphical output, model equations, solver inputs/outputs, and comprehensive reports to be combined for modeling, analysis, and stakeholder communication.
Six Sigma as a Quality Improvement Tool for Academic ProgramsEmilio L. Cano
The document discusses using Six Sigma as a quality improvement tool for academic programs. It aims to design and improve an Internal System Quality Assurance for a university to comply with accreditation standards. The authors extend the Six Sigma methodology, which uses the DMAIC strategy of Define, Measure, Analyze, Improve, Control to industrial quality processes, to academic processes. They develop a catalog of process typologies and apply Six Sigma to examples like defining quality policies and student selection. The goal is to systematically identify variations and continuously improve procedures.
The document outlines the steps involved in conducting marketing research and presenting the findings, including defining the problem, research design, data collection and analysis, interpreting results, and structuring a written report with sections for an executive summary, problem definition, methodology, findings, conclusions, and recommendations. It emphasizes that conclusions and recommendations should be guided by the original research objectives and background. The document also provides initial guidance on creating a report style sheet to ensure consistency in formatting, spelling, abbreviations, and proofreading.
Six Sigma Quality Using R: Tools and Training Emilio L. Cano
This document discusses using the statistical software R for Six Sigma quality improvement projects. It introduces Six Sigma methodology and the DMAIC problem-solving strategy. The presentation outlines how R can be used for Six Sigma tools and analysis, highlights useful R packages for Six Sigma, and describes efforts to spread the use of R for quality improvement.
These slides were presented at the Use R! 2011 Conference, held in the University of Warwick (Coventry) in August 2011. It was one of the presentations of the Lightning Talks session.
Energy-efficient technology investments using a decision support system frame...Emilio L. Cano
This document presents an integrated framework for decision support systems using R. It describes using R and related packages to represent stochastic energy optimization problems, generate input files for solvers, analyze results, and produce reproducible reports. Stochastic models are developed and solved within this framework. The framework allows statistical analysis, graphical output, model equations, solver inputs/outputs, and comprehensive reports to be combined for modeling, analysis, and stakeholder communication.
Six Sigma as a Quality Improvement Tool for Academic ProgramsEmilio L. Cano
The document discusses using Six Sigma as a quality improvement tool for academic programs. It aims to design and improve an Internal System Quality Assurance for a university to comply with accreditation standards. The authors extend the Six Sigma methodology, which uses the DMAIC strategy of Define, Measure, Analyze, Improve, Control to industrial quality processes, to academic processes. They develop a catalog of process typologies and apply Six Sigma to examples like defining quality policies and student selection. The goal is to systematically identify variations and continuously improve procedures.
The document outlines the steps involved in conducting marketing research and presenting the findings, including defining the problem, research design, data collection and analysis, interpreting results, and structuring a written report with sections for an executive summary, problem definition, methodology, findings, conclusions, and recommendations. It emphasizes that conclusions and recommendations should be guided by the original research objectives and background. The document also provides initial guidance on creating a report style sheet to ensure consistency in formatting, spelling, abbreviations, and proofreading.
The document discusses the need to change the website for the R Project for Statistical Computing. It notes that the current website has been in use since ancient Greek times and that new R users find it intimidating. The document then lists several technical problems with the current website such as obsolete code, lack of SEO tags, poor CSS, and a complex structure. It proposes creating a new user interface to solve current problems and restructuring the site map. The technologies involved would include Webby and Ruby on Rails and the source code would be made freely available under GPL. It closes with a call for volunteers to help with new features and restructuring.
This document contains a survey project conducted by a group of students on cardiovascular diseases. It includes an introduction outlining the importance and prevalence of cardiovascular diseases in Malaysia. The objectives of the survey were to raise awareness of cardiovascular diseases among university students and analyze their understanding levels.
The methodology section describes how the group distributed 250 survey forms equally between male and female students. It also shows pictures of the distribution and data analysis process. The statistical analysis section presents the results of 15 survey questions in tables and charts. It compares the understanding levels of male and female students on general topics, causes/effects, and solutions of cardiovascular diseases. Overall, the survey found that female students generally displayed slightly higher levels of understanding across various aspects of cardiovascular diseases
This document presents a study analyzing male and female literacy rates in India. It examines literacy data from 10 Indian states, calculating mean, standard deviation, and coefficient of variation to determine that male literacy is higher and more stable than female literacy. Additional data from 10 more states is also analyzed, with the same result. The conclusion suggests that while male literacy is better, female literacy remains inconsistent and could be improved through educational programs and free education initiatives.
This document discusses loading social network data into R and performing social network analysis. It covers loading edge list data into igraph objects, visualizing networks using tkplot, calculating centrality measures like degree, betweenness, closeness and eigenvector centrality using functions from igraph, and identifying key actors by plotting eigenvector centrality against betweenness and examining residuals.
This document discusses R and Julia for data analysis and advanced analytics. It provides an overview of R's history, how it works, performance improvements, and use in production. Julia is introduced as a new high-performance dynamic language with similarities to R but faster performance due to its just-in-time compiler and type information. Examples are given comparing the performance of Julia to other languages. The document recommends Julia for those already using C/Fortran and suggests it will be useful for R users once fully developed.
This document summarizes a statistics project conducted by Jenny Lee and Han Woong Kim on random sampling. They separated male and female students into groups and used a random number generator to randomly select 8 male students out of 20 and 6 female students out of 15 to participate. The students each shot basketballs 3 times with their eyes closed and 3 times with their eyes open. The results from the male and female groups were then combined and compared to determine if closing or opening the eyes affected shooting accuracy. Potential biases included differences in sleep, nutrition, physical strength and eyesight between subjects.
R is a programming language and software environment for statistical analysis, graphics, and statistical computing. It is used widely in academic and industry settings. This document provides an introduction to R, including its history and community, how to get started, important data structures, visualization, statistical analysis techniques, and how to work with big data in R. It also discusses challenges of open source R and how Microsoft R products address these challenges through commercial support, scalability, and integration with SQL Server.
This document outlines an agenda for analyzing social networks with R. It discusses connecting to social networks like Facebook via APIs, extracting friend data, creating a friendship matrix, and visualizing the resulting friend graph in Gephi. It also provides examples of analyzing Facebook data like extracting post likes counts and generating statistics on popular posts. The document encourages exploring one's own social network data to find insights like common interests between friends or the gender distribution of one's network.
A presentation meant for non-statisticians on statistics and general statistical analysis. Basically provides a short overview of the processes involved in data collection, storage, hypothesis generation and statistical analysis. It does not deal with bayesian statistics. Presented at PRODVANCE 2016 Ahmedabad
This document summarizes a research report that compares the brands Shan and National for recipe masala mixes in Pakistan. The report analyzed data from 80 respondents through a questionnaire. Key findings include:
- Shan was the most recalled brand at 46.3%, compared to National at 12.5%
- TV ads were the most effective promotion at 80% awareness
- 68.8% of respondents use Shan most frequently, compared to 28.8% for National
- Biryani masala mix was the most commonly used product at 51.2% usage
- Taste and aroma were considered the most important brand aspect by 65% of respondents.
The document discusses a study on the types of electronic gadgets used by industrial design students at the University of Santo Tomas. It aims to determine the most common gadgets, how long students use them, and if they have brand preferences. The methodology involves surveying 30 students about the gadgets they use, brands, and how the gadgets help with their studies. Preliminary results found that 12 students use 3 gadgets, 14 are comfortable with 3 brands, and gadgets are mostly used for 6-12 hours per day for research and schoolwork, with Apple being the most popular brand.
This document appears to be a statistical research paper analyzing survey results from JRU students regarding their preferences for president in the 2010 Philippine election. It includes the following key points:
1. The paper aims to determine JRU students' preferences for president as well as differences between male and female students.
2. 250 JRU students were surveyed, with 115 male students and 135 female students.
3. The most common age for both male and female students was 20 years old.
4. The paper includes statistical analysis to test differences in preferences and perceptions between male and female students.
The document discusses key concepts related to formulating and testing hypotheses, including:
- Null and alternative hypotheses, which are mutually exclusive statements tested through sample analysis.
- Type I and Type II errors that can occur when making decisions to accept or reject the null hypothesis.
- The level of significance, critical region, and test statistics used to determine whether to reject the null hypothesis.
- The differences between one-tailed and two-tailed tests, parametric vs. non-parametric tests, and one-sample vs. two-sample tests.
- The document discusses different statistical measures including the mean, median, and mode.
- It provides examples of calculating the mean, median, and mode from sets of data. For example, it calculates the mean number of days students were absent from school based on attendance records.
- The examples demonstrate how to determine the measure, possible limitations, and common uses of each statistical measure.
Smoking has many serious health consequences including lung cancer, emphysema, and gangrene which can be fatal. The document recommends visiting websites like www.oxygen.com and www.quit.org.au or talking to a doctor to help quit smoking, which contains over 4,001 chemicals and kills over 5.5 million people per year globally.
This document contains contact information for Richard Sink including his LinkedIn, Facebook, and Twitter profiles as well as his email address and phone number. It provides multiple ways to connect with Richard Sink through various social media platforms and traditional contact methods.
This document discusses using QR codes in the classroom. It provides examples of how QR codes can be used for worksheets, stories, skill posters, book reviews, math answers, and more. It also lists some apps that can be used to create and scan QR codes on iPhone, iPad, Android devices. Contact information is provided for the author who blogs about using QR codes in education.
This document summarizes the November 2010 meeting of the Great Lakes Area .NET Users Group. It lists the 2010 executive officers and volunteers. It also provides information on sponsors, goals for 2010 which were met, speakers for 2010 meetings, and announcements on upcoming events and job opportunities. The document concludes with information on elections for 2011 officers and a social hour following the meeting at a local brewery.
The document repeats the date "Wednesday, 11 July 12" 20 times, indicating an event or log that occurred on that single day without providing any other context or details about what happened.
Recommendations to Avoid Problems and Difficulties in Implementing CMMI High ...isabelmargarido
Presentation done at SEPG Europe 2013 in Amesterdam, organised by the CMMI Institute. This presentation gives valuable lessons that can be applied by any organisation that wants to improve processes.
Six Sigma and the application perspectives of various industries: A bibliomet...IJAEMSJORNAL
In recent years, quality in companies has become a priority issue which, according to the organization, is a matter of very little investment and dedication in the production lines, so a quality tool can be the solution to this problem. The present research work is to demonstrate the state of the art on Six Sigma and the perspectives in several industries through a bibliometric analysis using Bibliometrix. A database from Scopus is used, which includes a total of 857 articles in a time span from 2016 to 2020. From the results obtained, some characteristics of the articles are illustrated and analyzed (keywords, main authors, country of origin, main journals, scientific production and collaborative networks). The results obtained from the analysis show the existence of an exponential increase trend on Six sigma, being the basis for improving the quality of the processes.
The document discusses the need to change the website for the R Project for Statistical Computing. It notes that the current website has been in use since ancient Greek times and that new R users find it intimidating. The document then lists several technical problems with the current website such as obsolete code, lack of SEO tags, poor CSS, and a complex structure. It proposes creating a new user interface to solve current problems and restructuring the site map. The technologies involved would include Webby and Ruby on Rails and the source code would be made freely available under GPL. It closes with a call for volunteers to help with new features and restructuring.
This document contains a survey project conducted by a group of students on cardiovascular diseases. It includes an introduction outlining the importance and prevalence of cardiovascular diseases in Malaysia. The objectives of the survey were to raise awareness of cardiovascular diseases among university students and analyze their understanding levels.
The methodology section describes how the group distributed 250 survey forms equally between male and female students. It also shows pictures of the distribution and data analysis process. The statistical analysis section presents the results of 15 survey questions in tables and charts. It compares the understanding levels of male and female students on general topics, causes/effects, and solutions of cardiovascular diseases. Overall, the survey found that female students generally displayed slightly higher levels of understanding across various aspects of cardiovascular diseases
This document presents a study analyzing male and female literacy rates in India. It examines literacy data from 10 Indian states, calculating mean, standard deviation, and coefficient of variation to determine that male literacy is higher and more stable than female literacy. Additional data from 10 more states is also analyzed, with the same result. The conclusion suggests that while male literacy is better, female literacy remains inconsistent and could be improved through educational programs and free education initiatives.
This document discusses loading social network data into R and performing social network analysis. It covers loading edge list data into igraph objects, visualizing networks using tkplot, calculating centrality measures like degree, betweenness, closeness and eigenvector centrality using functions from igraph, and identifying key actors by plotting eigenvector centrality against betweenness and examining residuals.
This document discusses R and Julia for data analysis and advanced analytics. It provides an overview of R's history, how it works, performance improvements, and use in production. Julia is introduced as a new high-performance dynamic language with similarities to R but faster performance due to its just-in-time compiler and type information. Examples are given comparing the performance of Julia to other languages. The document recommends Julia for those already using C/Fortran and suggests it will be useful for R users once fully developed.
This document summarizes a statistics project conducted by Jenny Lee and Han Woong Kim on random sampling. They separated male and female students into groups and used a random number generator to randomly select 8 male students out of 20 and 6 female students out of 15 to participate. The students each shot basketballs 3 times with their eyes closed and 3 times with their eyes open. The results from the male and female groups were then combined and compared to determine if closing or opening the eyes affected shooting accuracy. Potential biases included differences in sleep, nutrition, physical strength and eyesight between subjects.
R is a programming language and software environment for statistical analysis, graphics, and statistical computing. It is used widely in academic and industry settings. This document provides an introduction to R, including its history and community, how to get started, important data structures, visualization, statistical analysis techniques, and how to work with big data in R. It also discusses challenges of open source R and how Microsoft R products address these challenges through commercial support, scalability, and integration with SQL Server.
This document outlines an agenda for analyzing social networks with R. It discusses connecting to social networks like Facebook via APIs, extracting friend data, creating a friendship matrix, and visualizing the resulting friend graph in Gephi. It also provides examples of analyzing Facebook data like extracting post likes counts and generating statistics on popular posts. The document encourages exploring one's own social network data to find insights like common interests between friends or the gender distribution of one's network.
A presentation meant for non-statisticians on statistics and general statistical analysis. Basically provides a short overview of the processes involved in data collection, storage, hypothesis generation and statistical analysis. It does not deal with bayesian statistics. Presented at PRODVANCE 2016 Ahmedabad
This document summarizes a research report that compares the brands Shan and National for recipe masala mixes in Pakistan. The report analyzed data from 80 respondents through a questionnaire. Key findings include:
- Shan was the most recalled brand at 46.3%, compared to National at 12.5%
- TV ads were the most effective promotion at 80% awareness
- 68.8% of respondents use Shan most frequently, compared to 28.8% for National
- Biryani masala mix was the most commonly used product at 51.2% usage
- Taste and aroma were considered the most important brand aspect by 65% of respondents.
The document discusses a study on the types of electronic gadgets used by industrial design students at the University of Santo Tomas. It aims to determine the most common gadgets, how long students use them, and if they have brand preferences. The methodology involves surveying 30 students about the gadgets they use, brands, and how the gadgets help with their studies. Preliminary results found that 12 students use 3 gadgets, 14 are comfortable with 3 brands, and gadgets are mostly used for 6-12 hours per day for research and schoolwork, with Apple being the most popular brand.
This document appears to be a statistical research paper analyzing survey results from JRU students regarding their preferences for president in the 2010 Philippine election. It includes the following key points:
1. The paper aims to determine JRU students' preferences for president as well as differences between male and female students.
2. 250 JRU students were surveyed, with 115 male students and 135 female students.
3. The most common age for both male and female students was 20 years old.
4. The paper includes statistical analysis to test differences in preferences and perceptions between male and female students.
The document discusses key concepts related to formulating and testing hypotheses, including:
- Null and alternative hypotheses, which are mutually exclusive statements tested through sample analysis.
- Type I and Type II errors that can occur when making decisions to accept or reject the null hypothesis.
- The level of significance, critical region, and test statistics used to determine whether to reject the null hypothesis.
- The differences between one-tailed and two-tailed tests, parametric vs. non-parametric tests, and one-sample vs. two-sample tests.
- The document discusses different statistical measures including the mean, median, and mode.
- It provides examples of calculating the mean, median, and mode from sets of data. For example, it calculates the mean number of days students were absent from school based on attendance records.
- The examples demonstrate how to determine the measure, possible limitations, and common uses of each statistical measure.
Smoking has many serious health consequences including lung cancer, emphysema, and gangrene which can be fatal. The document recommends visiting websites like www.oxygen.com and www.quit.org.au or talking to a doctor to help quit smoking, which contains over 4,001 chemicals and kills over 5.5 million people per year globally.
This document contains contact information for Richard Sink including his LinkedIn, Facebook, and Twitter profiles as well as his email address and phone number. It provides multiple ways to connect with Richard Sink through various social media platforms and traditional contact methods.
This document discusses using QR codes in the classroom. It provides examples of how QR codes can be used for worksheets, stories, skill posters, book reviews, math answers, and more. It also lists some apps that can be used to create and scan QR codes on iPhone, iPad, Android devices. Contact information is provided for the author who blogs about using QR codes in education.
This document summarizes the November 2010 meeting of the Great Lakes Area .NET Users Group. It lists the 2010 executive officers and volunteers. It also provides information on sponsors, goals for 2010 which were met, speakers for 2010 meetings, and announcements on upcoming events and job opportunities. The document concludes with information on elections for 2011 officers and a social hour following the meeting at a local brewery.
The document repeats the date "Wednesday, 11 July 12" 20 times, indicating an event or log that occurred on that single day without providing any other context or details about what happened.
Recommendations to Avoid Problems and Difficulties in Implementing CMMI High ...isabelmargarido
Presentation done at SEPG Europe 2013 in Amesterdam, organised by the CMMI Institute. This presentation gives valuable lessons that can be applied by any organisation that wants to improve processes.
Six Sigma and the application perspectives of various industries: A bibliomet...IJAEMSJORNAL
In recent years, quality in companies has become a priority issue which, according to the organization, is a matter of very little investment and dedication in the production lines, so a quality tool can be the solution to this problem. The present research work is to demonstrate the state of the art on Six Sigma and the perspectives in several industries through a bibliometric analysis using Bibliometrix. A database from Scopus is used, which includes a total of 857 articles in a time span from 2016 to 2020. From the results obtained, some characteristics of the articles are illustrated and analyzed (keywords, main authors, country of origin, main journals, scientific production and collaborative networks). The results obtained from the analysis show the existence of an exponential increase trend on Six sigma, being the basis for improving the quality of the processes.
CES 2013 conference - Rethinking the Relationship between Monitoring and Eval...CesToronto
The document discusses rethinking the relationship between evaluation, performance measurement/monitoring, and results-based management (RBM). It notes that while evaluation and performance measurement are meant to complement each other in theory, this complementarity is not always realized in practice. The document observes issues in Canada and internationally where performance measurement has not adequately supported evaluation. It also considers how evaluation and performance measurement can better support RBM and each other through clarifying their uses and users, and building organizational capacity.
International Journal of Scientific and Research Publications,.docxnormanibarber20063
International Journal of Scientific and Research Publications, Volume 2, Issue 1, January 2012 1
ISSN 2250-3153
www.ijsrp.org
Understanding the Benefits and Limitations of Six Sigma
Methodology
Nilesh V Fursule, Dr. Satish V Bansod, Swati N. Fursule
Abstract- Six Sigma is both a philosophy and a methodology
that improves quality by analyzing data with statistics to find the
root cause of quality problems and to implement controls.
Statistically, Six Sigma refers to a process in which the range
between the mean of a process quality measurement and the
nearest specification limit is at least six times the standard
deviation of the process.
Despite the pervasiveness of Six Sigma program
implementations, there is increasing concern about
implementation failures. One reason many Six Sigma programs
fail is because an implementation model on how to effectively
guide the implementation of these programs is lacking. While Six
Sigma is increasingly implemented in industry, little academic
research has been done on Six Sigma and its influence on quality
management theory and application. There is a criticism that Six
Sigma simply puts traditional quality management practices in a
new package. To investigate this issue and the role of Six Sigma
in quality management, this study reviewed both the traditional
quality management and Six Sigma literatures. Quality
professionals are aware that the six-sigma methodology employs
existing, well-known tools developed in quality sciences and are
based on the works of Deming, Juran, Ishikawa, Taguchi, and
others. Nevertheless six sigma, a Motorola innovation, has been a
positive force. A good presentation – black belts and green belts
honoring six-sigma experts – can make statistical process
improvement, and the systematic six-sigma methodology taste
good, and do good work.
Index Terms- lean manufacturing, six sigma, DMAIC, SCM
I. INTRODUCTION
ix Sigma is both a philosophy and a methodology that
improves quality by analyzing data with statistics to find the
root cause of quality problems and to implement controls.
Statistically, Six Sigma refers to a process in which the range
between the mean of a process quality measurement and the
nearest specification limit is at least six times the standard
deviation of the process. The statistical objectives of Six Sigma
are to centre the process on the target and reduce process
variation. A Six Sigma process will approach 'zero defects' with
only 3.4 defects per million opportunities (DPMO) for a defect to
occur. In comparison, the goal of many quality initiatives
throughout the 1980s and early 90s was to obtain a process
capability index (Cpk) of at least 1.0, which roughly translates to
3 Sigma. However, this level of quality still produces a defect
rate of 66,810 DPMO. Six Sigma differs from other quality
programmes in its 'top-down' driv.
Requirement Elicitation Model (REM) in the Context of Global Software Develop...IJAAS Team
Contxext:Requirement elicitation is difficult and critical phase of requirement engineering and the case is worst in global software development (GSD). The study is about requirement elicitation in the context of GSD. Objective: Development of requirement elicitation model (REM) which can address the factors that have positive impact and the factors that have negative impact during elicitation in GSD. The propose model will give solutions and practices to the challenges during elicitation. Method: Systematic literature review (SLR) and empirical research study will be used for achieving the goals and objectives. Expected outcomes: The expected results of this study will be REM that will help vendor organizations for better elicitation during GSD.
The growth rate of fair-size institutions and Organization is directly related to the
implementation of Six Sigma Methodology. This paper consists of detailed analysis regarding application
of six sigma Methodologies in Organizations and Institutions. This paper provides an overview to the
literature into various categories and considers various methods/techniques suggested in the literature.
Based on the review, avenues for further research are also discussed.
This document is a resume for Jing Ji summarizing their education and work experience. Ji has a M.S. in Operations Research from Columbia University and a B.S. in Applied Mathematics and B.A. in Economics from Peking University. Ji has worked as a researcher developing algorithms to test market parameters, a project consultant developing models to estimate transportation capacities, and a research assistant implementing prediction models with oil price data. Ji also received several academic awards and has skills in computer programming, leadership, and hobbies including piano, photography, and swimming.
Proceedings of the 2015 Industrial and Systems Engineering Res.docxwkyra78
Proceedings of the 2015 Industrial and Systems Engineering Research Conference
S. Cetinkaya and J. K. Ryan, eds.
Use of Symbolic Regression for Lean Six Sigma Projects
Daniel Moreno-Sanchez, MSc.
Jacobo Tijerina-Aguilera, MSc.
Universidad de Monterrey
San Pedro Garza Garcia, NL 66238, Mexico
Arlethe Yari Aguilar-Villarreal, MEng.
Universidad Autonoma de Nuevo Leon
San Nicolas de los Garza, NL 66451, Mexico
Abstract
Lean Six Sigma projects and the quality engineering profession have to deal with an extensive selection of tools
most of them requiring specialized training. The increased availability of standard statistical software motivates the
use of advanced data science techniques to identify relationships between potential causes and project metrics. In
these circumstances, Symbolic Regression has received increased attention from researchers and practitioners to
uncover the intrinsic relationships hidden within complex data without requiring specialized training for its
implementation. The objective of this paper is to evaluate the advantages and drawbacks of using computer assisted
Symbolic Regression within the Analyze phase of a Lean Six Sigma project. An application of this approach in a
service industry project is also presented.
Keywords
Symbolic Regression, Data Science, Lean Six Sigma
1. Introduction
Lean Six Sigma (LSS) has become a well-known hybrid methodology for quality and productivity improvement in
organizations. Its wide adoption in several industries has shaped Process Innovation and Operational Excellence
initiatives, enabling LSS to become a main topic in quality practitioner sites of interest [1], recognized Six Sigma
(SS) certification body of knowledge contents [2], and professional society conferences [3].
However LSS projects and the quality engineering profession have to deal with an extensive selection of tools most
of them requiring specialized training. To assist LSS practitioners it is common to categorize tools based on the
traditional DMAIC model which stands for Define, Measure, Analyze, Improve, and Control phases. Table 1
presents an overview of the main tools that are commonly used in each phase of a LSS project, allowing team
members to progressively develop an understanding between realizing each phase’s intent and how the selected
tools can contribute to that purpose.
This paper focuses on the Analyze phase where tools for statistical model building are most likely to be selected.
The increased availability of standard statistical software motivates the use of advanced data science techniques to
identify relationships between potential causes and project metrics. In these circumstances Symbolic Regression
(SR) has received increased attention from researchers and practitioners even though SR is still in an early stage of
commercial availability.
The objective of this paper is to evaluate the advantages and drawbacks o ...
Statistical learning theory provides a framework for machine learning that draws from statistics and functional analysis. It deals with finding predictive functions based on data. Supervised learning involves learning from input-output pairs in a training data set to infer the function that maps inputs to outputs. The goals of learning are understanding relationships and enabling prediction. Statistical learning in R can perform regression for continuous outputs or classification for discrete outputs. It is commonly used in fields like computer vision, speech recognition, and bioinformatics.
IRJET- Teaching Learning Practices for Metrology & Quality Control Subject in...IRJET Journal
1. The document discusses teaching and learning practices for the Metrology and Quality Control subject in an outcome-based education system.
2. It outlines the program educational objectives, program outcomes, and course outcomes for the subject and describes how they are mapped and assessed.
3. Internal evaluations of students including unit tests, assignments, and exams are used to measure course outcome attainment, with lower attainment found for two course outcomes, leading to corrective actions being taken like industrial visits and expert lectures.
PRODUCTIVITY OF AGILE TEAMS: AN EMPIRICAL EVALUATION OF FACTORS AND MONITORIN...Claudia Melo
Presenting my thesis during the National Thesis Contest in Computer Science - top 6 PhD Computer Science Thesis in Brasil/ 2013.
XXXIV Congresso da Sociedade Brasileira de Computação (CSBC 2014) - CTD.
This document discusses how companies can improve their sales and operations planning (S&OP) processes through predictive analytics, scenario planning, and risk management. It recommends that companies use digital modeling, simulation, and probabilistic predictive analytics to evaluate different scenarios and supply chain designs without experimenting on live operations. Incorporating risk management into S&OP allows companies to develop response plans for uncertain events and improve long-term sustainability and competitive advantage.
Parma 2016-05-17 - JGrass-NewAGE - Some About The State of ArtRiccardo Rigon
This describes the motivation behind the JGrass-NewAGE infrastructure. It also shows the main components that were implemented. Finally it shows and comments some case studies and some use cases
IRJET- A Research Study on Critical Challenges in Agile Requirements EngineeringIRJET Journal
This document summarizes a research study on the critical challenges of agile requirements engineering. A survey was conducted of over 80 respondents from IT companies in North America and India. The survey found that key challenges included difficulties with effort estimation, architectural structure, documentation, and end-user involvement. A literature review supported these findings and identified additional challenges such as dealing with non-functional requirements, team organization, minimal documentation, changing requirements, and cost and deadline estimation. The study concluded that while agile methods promise benefits, companies still struggle with effective implementation, particularly regarding requirements engineering.
Seeking a challenging position to utilize my quantitative and data interpretation skills complementing with my knowledge of Technology and Management to excel in areas of Analytics and Digital Marketing; which will nurture and bring forth the best I can offer to the organization, self & society
This document describes a strategic analytics methodology (SAM) that was developed to better integrate data mining and analytics with business decision making. It summarizes a case study where SAM was applied to a telecommunications customer retention project that had previously used the CRISP-DM methodology. The case study found that CRISP-DM provided limited integration with the business, failing to meet objectives or timelines. SAM embedded data mining within the business decision making process to provide more strategic insights and improved results for the retention project. The document outlines the key phases and steps of SAM and compares the outcomes of applying CRISP-DM versus SAM on the case study project.
The presentation has discussed comparatively among three SEM instruments which are (1) SAS CALIS procedure, (2) R's lavaan package, and (3) Mplus version 8.0 on MIDUS II dataset.
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...Dr. Haxel Consult
Word embeddings, deep learning, transformer models and other pre-trained neural language models (sometimes recently referred to as "foundational models") have fundamentally changed the way state-of-the-art systems for natural language processing and information access are built today. The "Data-to-Value" process methodology (Leidner 2013; Leidner 2022a,b) has been devised to embody best practices for the construction of natural language engineering solutions; it can assist practitioners and has also been used to transfer industrial insights into the university classroom. This talk recaps how the methodology supports engineers in building systems more consistently and then outlines the changes in the methodology to adapt it to the deep learning age. The cost and energy implications will also be discussed.
Similar to Using R for Statistical Training: An Application to Six Sigma Methodology for Process Improvement. (20)
R and Shiny to support real estate appraisers: An expert algorithm implementa...Emilio L. Cano
This document describes an expert algorithm implementation for Automated Valuation Models (AVM) to help real estate appraisers value properties. The algorithm collects property characteristics and sale prices from online listings to find comparable properties in a similar way to a human appraiser. It uses an modified inverse distance weighting estimator to determine a property's value based on comparable properties found. The algorithm is implemented using R and Shiny to allow configuration of rules and provide an interactive interface for appraisers to explore estimation results. Ongoing work aims to improve precision through machine learning and geostatistics models.
Generación de materiales didácticos multiformato con bookdownEmilio L. Cano
Este documento describe cómo usar la herramienta bookdown para generar materiales didácticos en múltiples formatos. El objetivo es guiar a los estudiantes y proporcionar recursos atractivos y actualizables en varias plataformas. Se utiliza R Markdown para crear los documentos, que luego se compilan en formato libro usando bookdown. Esto permite incluir código R y resultados dinámicos. El resultado es un sitio web con los apuntes que los estudiantes pueden usar en diferentes dispositivos.
Unattended SVM parameters fitting for monitoring nonlinear profilesEmilio L. Cano
This document discusses using support vector machines (SVM) for unattended parameter fitting to monitor nonlinear profiles. It presents an illustrative example of using SVM regression to smooth measured density profiles of engineered wood boards. The key points are:
1) SVM regression requires selecting parameters C (regularization parameter) and ε (width of insensitive zone), which control the complexity and deviations of the model.
2) Methods are presented for unattended selection of C and ε based on properties of the input noise and data.
3) The SVM model is applied to smooth individual nonlinear profiles from measured wood board density data and identify potential outliers.
Appling Scrum to Organize University Degrees CourseworkEmilio L. Cano
The document discusses applying the Scrum framework to organize university coursework. Scrum is an agile project management framework typically used for software development. It involves sprints, daily stand-up meetings, product backlogs and user stories. The authors applied Scrum concepts like sprints and user stories to structure practical work for a university course. Students worked in scrum teams on assignments divided into sprints. They found Scrum helped organize their work and the teachers found it improved classroom work organization and planning.
Monitoring nonlinear profiles with {R}: an application to quality controlEmilio L. Cano
This document discusses using R to analyze nonlinear profiles. It introduces the SixSigma package for smoothing nonlinear profiles using support vector machines. An example is provided using particle board density data to create a prototype profile and identify out-of-control boards. Nonlinear profiles allow more complex quality characteristics to be modeled and can be used with Shewhart control charts.
Generación y corrección automática de trabajos evaluables personalizados con ...Emilio L. Cano
El documento describe un método para generar trabajos evaluables personalizados para estudiantes individuales utilizando el software R. El método genera datos y enunciados únicos para cada estudiante, crea archivos de trabajo en formato Excel, evalúa automáticamente los trabajos terminados y califica a los estudiantes de forma eficiente. El objetivo es proporcionar una evaluación justa y diferenciada que promueva métodos de enseñanza innovadores.
Talentyon: how to turn R expertise into business within the collaborative eco...Emilio L. Cano
Talentyon is a platform that connects data analytics experts with businesses seeking their expertise. It aims to address challenges like the talent crunch in analytics and the rise of freelancers by building a network of verified experts. The case study describes how Talentyon matched an industrial manager at a food company with an R expert, providing statistical training and support to improve the company's industrial processes. As a result, the company benefited from ongoing improvement projects using statistical methods, while the expert earned remuneration through the Talentyon network.
Las normas ISO como puerta de entrada de la Estadística en la empresaEmilio L. Cano
Una norma ISO está reconocida y aceptada internacionalmente. Son desarrolladas por expertos de todo el mundo a través de comités técnicos a los que pertenecen entidades de normalización nacionales como AENOR, que canaliza la participación española en la elaboración de normas. El subcomité AENOR de métodos estadísticos CTN66/SC3 participa en el comité técnico de ISO TC69 ``Applications of statistical methods''. El subcomité CTN66/SC3 participa en el desarrollo y adopción de normas internacionales en estadística, así como su traducción y adopción a nivel nacional como normas UNE-ISO. Algunas de las normas adoptadas como normas UNE-ISO tratan sobre Seis Sigma(serie ISO 13053), gráficos de control (serie ISO 7870), inspección por muestreo (series ISO 2589 e ISO 3951), vocabulario (serie ISO 3534), entre otras. La normalización proporciona beneficios directos a las empresas, y una manera de llevar la Estadística a las empresas es a través de las normas.
Las 7 herramientas básicas de la calidad con REmilio L. Cano
Este documento presenta las 7 herramientas básicas de la calidad con R. Describe cada una de las herramientas, incluyendo el diagrama de causa-efecto, la hoja de verificación, el gráfico de control, el histograma, el gráfico de Pareto, el gráfico de dispersión y la estratificación. Muestra cómo crear cada una de estas herramientas estadísticas básicas utilizando paquetes de R como qcc y SixSigma.
Análisis de inversiones energéticas en el ámbito del edificioEmilio L. Cano
El documento analiza las inversiones energéticas en edificios bajo condiciones de incertidumbre. Explica que los enfoques deterministas conducen a riesgos al no considerar la variabilidad. Propone el uso de modelos de optimización estocástica para gestionar el riesgo. Presenta el Sistema de Ayuda a la Decisión EnRiMa desarrollado para apoyar la toma de decisiones estratégicas a largo plazo en condiciones de incertidumbre.
Standardisation on Statistics: ISO Standards and R ToolsEmilio L. Cano
This document discusses standardization in statistics through ISO standards and how R statistical software can support them. It provides an overview of ISO/TC 69, which develops standards for statistical applications, and AENOR, Spain's standards body involved in adopting and managing statistical standards. The document concludes that data scientists can benefit from understanding both standards and using R, as R code is open source and can be easily verified, meeting requirements of standards like ISO 9001.
An integrated Solver Manager: using R and Python for energy systems optimizationEmilio L. Cano
1) Decision support systems are needed to address new challenges for building managers around energy planning given global changes and local needs.
2) A Solver Manager was developed to integrate optimization models and solvers in a flexible and extensible way for use in decision support systems.
3) An example energy systems optimization model is presented involving minimizing costs subject to capacity and demand constraints. The model is specified, an instance is generated with data, and the solution is obtained.
Calidad Seis Sigma con R: Aplicación a la docenciaEmilio L. Cano
This document discusses using R software to support Six Sigma methodology. It introduces reproducible research approaches for statistical training, provides examples using Sweave documents to integrate R code and LaTeX, and outlines an EADAPU training program covering Six Sigma phases and tools. The document also describes using R for process mapping, loss function analysis, and measurement system analysis for quality improvement projects.
Strategic Energy Systems Planning under UncertaintyEmilio L. Cano
The document discusses a decision support system (DSS) called EnRiMa that was developed for operators of energy-efficient buildings. The DSS uses a strategic model to make long-term decisions about technology installations and a linked operational model to determine short-term energy dispatching. The model accounts for uncertainty through a scenario tree and stochastic optimization. An example application to a building evaluating photovoltaic and combined heat and power technologies under different demand scenarios is presented.
Reproducible Operations Research. An Application to Energy Systems OptimizationEmilio L. Cano
This document discusses reproducible operations research using an integrated framework in R. It presents a case study on the EnRiMa project, which developed a decision support system for energy systems optimization. The key components discussed include a symbolic model specification to represent optimization models mathematically, a solver manager to generate solver input and output documentation, and reporting of results. The goal is to tie specific instructions to data analysis and models so results can be recreated and better understood.
A Solver Manager for energy systems planning within a Stochastic Optimization...Emilio L. Cano
The document describes an energy systems planning model within a stochastic optimization framework. It includes both strategic decisions about technology deployment and operational decisions about energy system usage. A solver manager is proposed to integrate different optimization solvers to solve the strategic and operational subproblems. The model is being developed as part of the EnRiMa project to create a decision support system for efficient energy management in buildings.
Strategic Buildings’ Energy Systems PlanningEmilio L. Cano
The document describes an energy systems planning model for buildings developed as part of the EnRiMa project. It includes both strategic and operational decision modules. The strategic module determines what energy technologies to install or decommission over long periods, while considering budget limits and emissions constraints. The embedded operational module models short-term energy dispatch and storage decisions subject to energy balance constraints. The overall goal is to develop a decision support system to help operators of public buildings optimize their energy systems.
A Symbolic Model Specification for Energy Efficiency Optimization ModelsEmilio L. Cano
The document describes a symbolic model specification for energy efficiency optimization models. It presents the outline which includes the introduction to the EnRiMa project and decision support system, the optimization models involving strategic and operational modules, the symbolic model specification including representation of variables, parameters, sets, and equations, and reproducible research. Key aspects of the symbolic model specification are that it contains the mathematical representation of optimization models for relevant energy subsystems and their interactions in a data-driven way using indices to identify individual model entities.
Decision Making under Uncertainty: R implementation for Energy Efficient Buil...Emilio L. Cano
The document describes a decision support system for operators of energy efficient buildings developed by Emilio L. Cano and Javier M. Moguerza of the University Rey Juan Carlos. It presents an R implementation for modeling decision making under uncertainty that symbolically specifies optimization problems, generates solver input files, and analyzes solutions to support strategic and operational energy management decisions in public buildings.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Using R for Statistical Training: An Application to Six Sigma Methodology for Process Improvement.
1. Using R for
Statistical Training
17/04/2012
EL Cano,
Using R for Statistical Training
JM Moguerza,
A Redchuk An Application to Six Sigma Methodology
Statistical Training for Process Improvement.
The Problem
Approaches
The R Choice
The R framework
Sweave
Emilio L. Cano, Andr´s Redchuk and Javier
e
Application M. Moguerza
Six Sigma
Examples
Environments Departamento de Estad´ıstica e Investigaci´n Operativa
o
Universidad Rey Juan Carlos (Madrid)
XXXIII Congreso Nacional de Estad´
ıstica e
Investigaci´n Operativa
o
SEIO 2012 1/28
2. Using R for
Statistical Training
Contenido
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk 1 Statistical Training
Statistical Training The Problem
The Problem
Approaches Approaches
The R Choice
The R framework
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 2/28
3. Using R for
Statistical Training
Contenido
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk 1 Statistical Training
Statistical Training The Problem
The Problem
Approaches Approaches
The R Choice
The R framework
Sweave 2 The R Choice
Application
Six Sigma
The R framework
Examples
Environments Sweave
SEIO 2012 2/28
4. Using R for
Statistical Training
Contenido
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk 1 Statistical Training
Statistical Training The Problem
The Problem
Approaches Approaches
The R Choice
The R framework
Sweave 2 The R Choice
Application
Six Sigma
The R framework
Examples
Environments Sweave
3 Application
Six Sigma
Examples
Environments
SEIO 2012 2/28
5. Using R for
Statistical Training
Contenido
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk 1 Statistical Training
Statistical Training The Problem
The Problem
Approaches Approaches
The R Choice
The R framework
Sweave 2 The R Choice
Application
Six Sigma
The R framework
Examples
Environments Sweave
3 Application
Six Sigma
Examples
Environments
SEIO 2012 3/28
6. Using R for
Statistical Training
The Problem
17/04/2012
Elements of Statistical Training
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 4/28
7. Using R for
Statistical Training
Copy-paste Approach
17/04/2012
Approaches
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Inconsistencies
Sweave
Application Errors
Six Sigma
Examples
Environments
Out-of-date
non-reproducible
Painful changes
SEIO 2012 5/28
8. Using R for
Statistical Training
Reproducible Research Approach
17/04/2012
Approaches
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training Reproducible Research
The Problem
Approaches
The goal of reproducible research is to tie
The R Choice
The R framework specific instructions to data analysis and
Sweave
Application experimental data so that scholarship can be
Six Sigma
Examples recreated, better understood and verified
Environments
Literate Programming
Literate programming is a methodology that
combines a programming language with a
documentation language
SEIO 2012 6/28
9. Using R for
Statistical Training
Reproducible Research
17/04/2012
Workflow
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 7/28
10. Using R for
Statistical Training
Contenido
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk 1 Statistical Training
Statistical Training The Problem
The Problem
Approaches Approaches
The R Choice
The R framework
Sweave 2 The R Choice
Application
Six Sigma
The R framework
Examples
Environments Sweave
3 Application
Six Sigma
Examples
Environments
SEIO 2012 8/28
11. Using R for
Statistical Training
The R System
17/04/2012
Choosing R
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training What is R?
The Problem
Approaches R is a language and environment for statistical
The R Choice
The R framework computing and graphics.
Sweave
Application
Six Sigma
Examples
Open Source
Environments
Platform independent
Huge community
Extensible
3 730 available
http://www.r-project.org
packages
SEIO 2012 9/28
12. Using R for
A
LTEX, Beamer, PDF
Statistical Training
17/04/2012
Choosing R
EL Cano,
JM Moguerza,
A Redchuk
A
LTEX
Statistical Training
The Problem
Approaches
LaTeX is a high-quality typesetting system; it
The R Choice
The R framework
includes features designed for the production
Sweave
of technical and scientific documentation
Application
Six Sigma
Examples
Environments Beamer
Beamer is a LaTeX class for creating
presentations that are held using a projector,
but it can also be used to create transparency
slides
LTEXFiles can easily be converted to PDF.
A
SEIO 2012 10/28
13. Using R for
Statistical Training
Sweave Documents
17/04/2012
An Efficient Framework
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
Sweave
The R framework
Sweave
A Sweave document is a plain-text file which
Application merges LTEX code and R code. The R
A
Six Sigma
Examples
Environments
function Sweave() converts the Sweave
document (*.Rnw) into a LTEXfile (*.tex).
A
The code chunks are executed and the results
embedded into the LTEX file.
A
SEIO 2012 11/28
14. Using R for
Statistical Training
Contenido
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk 1 Statistical Training
Statistical Training The Problem
The Problem
Approaches Approaches
The R Choice
The R framework
Sweave 2 The R Choice
Application
Six Sigma
The R framework
Examples
Environments Sweave
3 Application
Six Sigma
Examples
Environments
SEIO 2012 12/28
15. Using R for
Statistical Training
Methodology at a Glance
17/04/2012
Six Sigma
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
The Essense
Approaches
The application of the Scientific Method to
The R Choice
The R framework
Sweave
process improvement, using an easy language.
Application
Six Sigma
Examples DMAIC Cycle
Environments
Roles
Define
Champion
Measure
Master Black Belt
Analyze
Black Belt
Improve
Green Belt
Control
SEIO 2012 13/28
16. Using R for
Statistical Training
SixSigma Package
17/04/2012
Six Sigma
EL Cano,
JM Moguerza, Six Sigma with R | Paper Helicopter template
Using packages
max
A Redchuk (9.5cm)
std
(8cm)
Statistical Training
The Problem
min
(6.5cm)
Manuals
Approaches
Data sets
← wings length →
The R Choice
The R framework
Sweave
Templates
cut
Application
Learn-by-Code
?
pe
Six Sigma fold ↑ fold ↓
ta
Examples
Environments
cut
Six Sigma Process Map
operators
INPUTS
cut cut tools
X raw material
facilities
← body length → INSPECTION ASSEMBLY TEST LABELING
sheets sheets helicopter helicopter
...
INPUTS
INPUTS
INPUTS
INPUTS
tape?
tape?
Param.(x): width NC Param.(x): operator C Param.(x): operator C Param.(x): operator C
operator C cut P throw P label P
Measure pattern P fix P discard P Featur.(y): label
discard P rotor.width C environment N
Featur.(y): ok rotor.length C Featur.(y): time
paperclip C
tape C
min Featur.(y): weight
(6.5cm)
LEGEND
std helicopter
OUTPUTS
fold ↓ ↓
fold ↑ ↑
(C)ontrollable
(8cm) (Cr)itical
(N)oise
Y
(P)rocedure
clip? max
Paper Helicopter Project
max min ← body width → min max (9.5cm)
SEIO 2012 (6cm) (4cm) (4cm) (6cm) 14/28
17. Using R for
Statistical Training
Book
17/04/2012
Six Sigma
EL Cano,
JM Moguerza,
A Redchuk
Six Sigma with R
Statistical Training
The Problem A live example: The entire book has been
Approaches
The R Choice
produced using Sweave.
The R framework
Sweave
Application The roadmap: The
Six Sigma
Examples
Environments
DMAIC Cycle
The case study: paper
helicopter
SixSigma package: data
sets, functions
Easy explanations,
further readings
SEIO 2012 15/28
18. Using R for
Statistical Training
Sweave Example I
17/04/2012
Six Sigma Application
EL Cano,
JM Moguerza,
A Redchuk
documentclass [ a4paper ]{ article }
Statistical Training usepackage { Sweave }
The Problem title { Design of Experiments }
Approaches author { EL Cano and JM Moguerza and A Rechuk }
The R Choice begin { document }
The R framework maketitle
Sweave section { Introduction }
Application Design of experiments is the most important took in the I
Six Sigma DMAIC cycle ldots .
Examples
< < > >=
Environments
library ( SixSigma )
doe . model1 <- lm ( score ~ flour + salt + bakPow +
flour * salt + flour * bakPow +
salt * bakPow + flour * salt * bakPow ,
data = ss . data . doe1 )
summary ( doe . model1 )
@
This is the general model :
begin { equation }
label { eq : doe : model }
SEIO 2012 16/28
19. Using R for
Statistical Training
Sweave Example II
17/04/2012
Six Sigma Application
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
y_ { ijkl }= mu + alpha_i + beta_j + gamma_k +( alpha beta ) _ { ij }
Approaches ( alpha gamma ) _ { ik }+( beta gamma ) _ { kl }+( alpha beta gamma
The R Choice
varepsilon_ { ijkl } ,
The R framework
end { equation }
Sweave And here we have a plot of effects :
Application
Six Sigma << maineff , echo = FALSE , fig = TRUE > >=
Examples plot ( c ( -1 , 1) , ylim = range ( ss . data . doe1$score ) ,
Environments
coef ( doe . model1 )[1] + c ( -1 , 1) * coef ( doe
type =" b " , pch =16)
abline ( h = coef ( doe . model1 )[1])
@
% input { section2 }
end { document }
SEIO 2012 17/28
22. Using R for
Statistical Training
Project Example
17/04/2012
Divide and Conquer!
EL Cano,
JM Moguerza,
A Redchuk
Strategies
Statistical Training
The Problem
Approaches
Partial Sweave files can be compiled to get
The R Choice partial LTEX files. R scripts can Sweave .Rnw
A
The R framework
Sweave files and “source” .R files. The final document
Application
Six Sigma is obtained by compiling the “master”
Examples
Environments LTEX file.
A
> source("code/myoptions.R")
> source("code/myfunctions.R")
> source("code/mydata.R")
> Sweave("rnw/theorem01.Rnw")
> Sweave("rnw/lesson01.Rnw")
> Sweave("rnw/exercises01.Rnw")
> ...
> texi2pdf("master.tex")
SEIO 2012 20/28
23. Using R for
Statistical Training
Some useful extensions
17/04/2012
Packages
EL Cano,
JM Moguerza,
A Redchuk
knitr, pgfSweave: enhanced options for
Statistical Training
The Problem
Sweave
Approaches
The R Choice
RGIFT: Automatic generation of
The R framework
Sweave questionnaires for Moodle
Application
Six Sigma exams: Automatic generation of printable
Examples
Environments exams
odfWeave: Open Document format
documents generation
More in the “Reproducible Research” Task
View at CRAN.
http://cran.r-project.org/web/views/
ReproducibleResearch.html
SEIO 2012 21/28
24. Using R for
Statistical Training
R GUI
17/04/2012
Integrated Environments
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 22/28
25. Using R for
Statistical Training
R Studio
17/04/2012
Integrated Environments
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 23/28
26. Using R for
Statistical Training
EMACS + ESS
17/04/2012
Integrated Environments
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 24/28
27. Using R for
Statistical Training
Eclipse + StatET
17/04/2012
Integrated Environments
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 25/28
28. Using R for
Statistical Training
Summary
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk
Statistical training entail some challenges
regarding contents and materials.
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 26/28
29. Using R for
Statistical Training
Summary
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk
Statistical training entail some challenges
regarding contents and materials.
Statistical Training
The Problem
Approaches
R is the perfect partner for statistical
The R Choice
The R framework
training.
Sweave
Application
Six Sigma
Examples
Environments
SEIO 2012 26/28
30. Using R for
Statistical Training
Summary
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk
Statistical training entail some challenges
regarding contents and materials.
Statistical Training
The Problem
Approaches
R is the perfect partner for statistical
The R Choice
The R framework
training.
Sweave
Application
Reproducible research and literate
Six Sigma
Examples
programming enhance training materials
Environments
quality.
SEIO 2012 26/28
31. Using R for
Statistical Training
Summary
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk
Statistical training entail some challenges
regarding contents and materials.
Statistical Training
The Problem
Approaches
R is the perfect partner for statistical
The R Choice
The R framework
training.
Sweave
Application
Reproducible research and literate
Six Sigma
Examples
programming enhance training materials
Environments
quality.
The use of R and LTEX through Sweave,
A
comprise a complete framework for
statistical documentation generation.
SEIO 2012 26/28
32. Using R for
Statistical Training
Summary
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk
Statistical training entail some challenges
regarding contents and materials.
Statistical Training
The Problem
Approaches
R is the perfect partner for statistical
The R Choice
The R framework
training.
Sweave
Application
Reproducible research and literate
Six Sigma
Examples
programming enhance training materials
Environments
quality.
The use of R and LTEX through Sweave,
A
comprise a complete framework for
statistical documentation generation.
Extensions and integrated environments
make easy exploiting the R capabilities.
SEIO 2012 26/28
33. Using R for
Statistical Training
Acknowledgements
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches R Core Team and R enthusiasts in general.
The R Choice Springer
The R framework
Sweave
Application This work has been partially funded by the projects:
Six Sigma AGORANET project (IPT-430000-2010-32)
Examples VRTUOSI www.vrtuosi.org: 502869-LLP-1-2009-ES-ERASMUS-EVC)
Environments
HAUS: IPT-2011-1049-430000
EDUCALAB: IPT-2011-1071-430000
DEMOCRACY4ALL: IPT-2011-0869-430000
CORPORATE COMMUNITY: IPT-2011-0871-430000
SEIO 2012 27/28
34. Using R for
Statistical Training
Discussion
17/04/2012
EL Cano,
JM Moguerza,
A Redchuk
Statistical Training
The Problem
Approaches
The R Choice
The R framework
Sweave
Thanks for your
Application
Six Sigma
Examples
Environments
attention !
SEIO 2012 28/28