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.
Efficient Immutable Data Structures (Okasaki for Dummies)Tom Faulhaber
A talk I did at Intertrust on September 18, 2015.
I present some core concepts from functional programming and show how the work done by Chris Okasaki and others on efficient immutable data structures has made it practical to use functional techniques in production programs.
Efficient Immutable Data Structures (Okasaki for Dummies)Tom Faulhaber
A talk I did at Intertrust on September 18, 2015.
I present some core concepts from functional programming and show how the work done by Chris Okasaki and others on efficient immutable data structures has made it practical to use functional techniques in production programs.
Learning is Not a Mechanism: Assessment, Student Agency, and Digital SpacesJesse Stommel
An objective and portable system for grading students was created so that systematized schooling could scale. And we’ve designed technological tools in the 20th and 21st Centuries that have allowed us to scale even further. Toward mass-processing and away from subjectivity, human relationships, and care.
Explore how a transformative culture of learning can boost employee engagement and encourage a “growth mindset” that yields higher levels of performance, motivation, creativity, and innovation.
More on how to create a culture of learning: http://www.lynda.com/Business-training-tutorials/29-0.html
Rethinking Learning in the Age of Digital FluencyJudy O'Connell
Digital connectivity is a transformative phenomenon of the 21st century. While many have debated its impact on society, educators have been quick to mandate technology in school development - often without analysing the digital fluency of those involved, and the actual impact on learning. Is being digitally tethered creating a new learning nexus for those involved?
Ένα profil στο Facebook για την κινητοποίηση και ενίσχυση των μαθητών του Γυμνασίου στα Μαθηματικά.
How Social Media can be involved in education. Using Facebook to motivate students in Mathematics.
This presentation will provide insight into Watson’s DeepQA process, the complexities and
details of the DeepQA challenge, and how these tools and techniques can be applied in a clinical setting. Prototype tools will be presented that open conceptual frameworks for
delivering advanced analytics in the radiologist’s workplace that offer rapid access to critical, specific and highly relevant data with corresponding links to underlying evidence.
Seminario Bando Creazioni Giovani Sicilia 2013Eugenio Agnello
Slide del seminario gratuito curato da Eugenio Agnello, esperto in progettazione di bandi regionali, e da Fabio Mondino, commercialista ed esperto di startup e cofondatore di InnovaStartUp. L’evento si è svolto l'8 Agosto 2013.
Le slide sono rivolte ai giovani siciliani di età 18/36 anni che vogliono conoscere meglio il bando "Creazioni Giovani" e le opportunità che offre.
On Unified Stream Reasoning - The RDF Stream Processing realmDaniele Dell'Aglio
The presentation of my talk at WU Vienna on 18/2/2016. I discuss the problem of unifying existing solutions to process semantic streams - with a particular focus on the ones that perform continuous query answering over RDF streams
Forecasting Questions
Student Name
University Affiliate
Forecasting Questions
Problem 1: Planning
Step 1: Define – Create a list of all the tasks that require to be completed so as to complete this examination appropriately and keep a track of the tasks accordingly
Step 2: Plan – What information is available for solving the problem? Lecture Notes and Canvas Handouts. The lecture Notes provided is from Notes on PERT Chart, GRANTT Chart and Activity Matrix.
Step 3: Execute - Create an activity matrix and a table to make comparisons on your plan
Activity Matrix
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GRANTT chart
GANTT CHART
PROJECT TITLE
CSE171B Midterm
COMPANY NAME
CSE171B
PROJECT MANAGER
Shen Cheng
DATE
2/11/2019
WBS NUMBER
TASK TITLE
TASK OWNER
START DATE
END DATE
DURATION
PCT OF TASK COMPLETE
FRIDAY
SATURDAY
SUNDAY
MONDAY
TUESDAY
1
Problem 1: Planning
1.1
Activity Matrix
Shen Cheng
2/7/20
2/7/20
1
100%
1.2
GANTT chart
Shen Cheng
2/7/20
2/11/20
1
100%
1.3
PERT Chart
Shen Cheng
2/7/20
2/11/20
1
100%
1.4
Keep track of each task and document
Completion
Shen Cheng
2/7/20
2/11/20
100%
2
Problem 2
2.1
Read the Specialty Packaging Corporation Case
Study
Shen Cheng
2/8/20
2/8/20
1
100%
2.2
Competitive Strategy
Shen Cheng
2/8/20
2/8/20
1
100%
2.3
Supply Chain Strategy
Shen Cheng
2/8/20
2/8/20
1
100%
2.4
Where does SPC lie in the zone of strategic fit
between IDU and responsiveness?
Shen Cheng
2/8/20
2/8/20
1
100%
2.5
Identify what SPC’s high-level SC strategy
should be for each of the SC drivers.
Shen Cheng
2/8/20
2/8/20
1
100%
3
Problem 3
3.1
Form Hypothesis
Shen Cheng
2/8/20
2/10/20
1
100%
3.2
Forecast Demand for clear plastic using the 5
Methods
Shen Cheng
2/8/20
2/10/20
1
100%
3.3
Identify the better method for Clear Plastic.
Shen Cheng
2/8/20
2/10/20
1
100%
3.4
Was the Hypothesis Correct?
Shen Cheng
2/8/20
2/10/20
1
100%
3.5
Forecast Demand for 2007 Clear Plastic.
Shen Cheng
2/8/20
2/10/20
1
100%
4
Problem 4
4.1
Why should ...
Learning is Not a Mechanism: Assessment, Student Agency, and Digital SpacesJesse Stommel
An objective and portable system for grading students was created so that systematized schooling could scale. And we’ve designed technological tools in the 20th and 21st Centuries that have allowed us to scale even further. Toward mass-processing and away from subjectivity, human relationships, and care.
Explore how a transformative culture of learning can boost employee engagement and encourage a “growth mindset” that yields higher levels of performance, motivation, creativity, and innovation.
More on how to create a culture of learning: http://www.lynda.com/Business-training-tutorials/29-0.html
Rethinking Learning in the Age of Digital FluencyJudy O'Connell
Digital connectivity is a transformative phenomenon of the 21st century. While many have debated its impact on society, educators have been quick to mandate technology in school development - often without analysing the digital fluency of those involved, and the actual impact on learning. Is being digitally tethered creating a new learning nexus for those involved?
Ένα profil στο Facebook για την κινητοποίηση και ενίσχυση των μαθητών του Γυμνασίου στα Μαθηματικά.
How Social Media can be involved in education. Using Facebook to motivate students in Mathematics.
This presentation will provide insight into Watson’s DeepQA process, the complexities and
details of the DeepQA challenge, and how these tools and techniques can be applied in a clinical setting. Prototype tools will be presented that open conceptual frameworks for
delivering advanced analytics in the radiologist’s workplace that offer rapid access to critical, specific and highly relevant data with corresponding links to underlying evidence.
Seminario Bando Creazioni Giovani Sicilia 2013Eugenio Agnello
Slide del seminario gratuito curato da Eugenio Agnello, esperto in progettazione di bandi regionali, e da Fabio Mondino, commercialista ed esperto di startup e cofondatore di InnovaStartUp. L’evento si è svolto l'8 Agosto 2013.
Le slide sono rivolte ai giovani siciliani di età 18/36 anni che vogliono conoscere meglio il bando "Creazioni Giovani" e le opportunità che offre.
On Unified Stream Reasoning - The RDF Stream Processing realmDaniele Dell'Aglio
The presentation of my talk at WU Vienna on 18/2/2016. I discuss the problem of unifying existing solutions to process semantic streams - with a particular focus on the ones that perform continuous query answering over RDF streams
Forecasting Questions
Student Name
University Affiliate
Forecasting Questions
Problem 1: Planning
Step 1: Define – Create a list of all the tasks that require to be completed so as to complete this examination appropriately and keep a track of the tasks accordingly
Step 2: Plan – What information is available for solving the problem? Lecture Notes and Canvas Handouts. The lecture Notes provided is from Notes on PERT Chart, GRANTT Chart and Activity Matrix.
Step 3: Execute - Create an activity matrix and a table to make comparisons on your plan
Activity Matrix
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
A
A
x
x
B
B
x
C
X
x
C
x
D
x
D
E
E
x
F
x
F
X
G
x
x
G
X
H
x
x
X
H
I
x
x
X
X
I
x
J
J
x
K
x
K
x
L
x
x
L
x
M
x
x
x
M
x
N
x
x
x
x
N
x
O
O
P
x
P
Q
x
x
Q
x
R
x
R
S
x
S
GRANTT chart
GANTT CHART
PROJECT TITLE
CSE171B Midterm
COMPANY NAME
CSE171B
PROJECT MANAGER
Shen Cheng
DATE
2/11/2019
WBS NUMBER
TASK TITLE
TASK OWNER
START DATE
END DATE
DURATION
PCT OF TASK COMPLETE
FRIDAY
SATURDAY
SUNDAY
MONDAY
TUESDAY
1
Problem 1: Planning
1.1
Activity Matrix
Shen Cheng
2/7/20
2/7/20
1
100%
1.2
GANTT chart
Shen Cheng
2/7/20
2/11/20
1
100%
1.3
PERT Chart
Shen Cheng
2/7/20
2/11/20
1
100%
1.4
Keep track of each task and document
Completion
Shen Cheng
2/7/20
2/11/20
100%
2
Problem 2
2.1
Read the Specialty Packaging Corporation Case
Study
Shen Cheng
2/8/20
2/8/20
1
100%
2.2
Competitive Strategy
Shen Cheng
2/8/20
2/8/20
1
100%
2.3
Supply Chain Strategy
Shen Cheng
2/8/20
2/8/20
1
100%
2.4
Where does SPC lie in the zone of strategic fit
between IDU and responsiveness?
Shen Cheng
2/8/20
2/8/20
1
100%
2.5
Identify what SPC’s high-level SC strategy
should be for each of the SC drivers.
Shen Cheng
2/8/20
2/8/20
1
100%
3
Problem 3
3.1
Form Hypothesis
Shen Cheng
2/8/20
2/10/20
1
100%
3.2
Forecast Demand for clear plastic using the 5
Methods
Shen Cheng
2/8/20
2/10/20
1
100%
3.3
Identify the better method for Clear Plastic.
Shen Cheng
2/8/20
2/10/20
1
100%
3.4
Was the Hypothesis Correct?
Shen Cheng
2/8/20
2/10/20
1
100%
3.5
Forecast Demand for 2007 Clear Plastic.
Shen Cheng
2/8/20
2/10/20
1
100%
4
Problem 4
4.1
Why should ...
We will talk about how we can calculate the Big O in terms of Time Complexity, and then deep dive to understand Big O Rules. And the last, we will learn some techniques to improve the performance of our algorithm.
The talk I gave at the Stream Reasoning workshop in TU Berlin on December 8. I give an overview of RSEP-QL and how it can capture and formalise the behaviour of existing RSP engines, e.g. CSPARQL, EP-SPARQL, CQELS, SPARQLstream
Keynote in KLEE workshop on Symbolic Execution 2018
Systematic greybox fuzzing inspired by ideas from symbolic execution, work at NUS
Covers new usage of symbolic execution in automated program repair, work at NUS
Formal methods help improve the quality and reliability of software by providing proof of correctness. However, ensuring the correctness of verification tools that apply these formal methods, is itself a much harder problem. A typical way to justify the correctness is to provide soundness proofs based on semantic models. For program verifiers these soundness proofs are quite large and complex. In this thesis, we introduce certified reasoning to provide machine checked proofs of various components of an automated verification system. We develop new certified decision procedures (Omega++) and certified proofs (for compatible sharing) and integrate with an existing automated verification system (HIP/SLEEK). We show that certified reasoning improves the correctness and expressivity of automated verification without sacrificing on performance.
Classes without Dependencies - UseR 2018Sam Clifford
Presented by Sam Clifford at the 2018 UseR conference, Brisbane, Australia. The talk describes the design of SEB113 - Quantitative Methods in Science, a first year statistics/mathematics unit in the Bachelor of Science at Queensland University of Technology. The unit uses RStudio and the tidyverse packages to give students the skills to do meaningful data manipulation and analysis without relying on prior knowledge of advanced mathematics.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3
Calidad Seis Sigma con R: Aplicación a la docencia
1. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Seis Sigma con R
Emilio L. Cano
Departamento de Estad´ıstica e Investigaci´on Operativa
Universidad Rey Juan Carlos (Madrid)
December 5, 2012
E.T.S. Ingenieros Industriales
Universidad de Castilla-La Mancha
Seminario EIO UCLM 1/66
2. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Contents
1 Statistical Training
Approaches
Examples
Application
2 Six Sigma with R
Six Sigma
R
Packages
Seminario EIO UCLM 2/66
3. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Elements of Statistical Training
Seminario EIO UCLM 3/66
4. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Copy-paste Approach
Inconsistencies
Errors
Out-of-date
non-reproducible
Painful changes
Seminario EIO UCLM 4/66
5. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Reproducible Research Approach
Reproducible Research
The goal of reproducible research is to tie
specific instructions to data analysis and
experimental data so that scholarship can be
recreated, better understood and verified
Literate Programming
Literate programming is a methodology that
combines a programming language with a
documentation language
Seminario EIO UCLM 5/66
6. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Reproducible Research
Workflow
Seminario EIO UCLM 6/66
7. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Sweave Documents
Sweave
A Sweave document is a plain-text file which
merges LATEX code and R code. The R
function Sweave() converts the Sweave
document (*.Rnw) into a LATEX file (*.tex).
The code chunks are executed and the results
embedded into the LATEX file.
Seminario EIO UCLM 7/66
8. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Sweave Example
documentclass [a4paper ]{ article}
usepackage{Sweave}
title{Design of Experiments}
author{EL Cano and JM Moguerza and A Rechuk}
begin{document}
maketitle
section{ Introduction }
Design of experiments is the most important took in the I
DMAIC cycle ldots.
<<>>=
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}
y_{ijkl }=mu+ alpha_i + beta_j + gamma_k +( alphabeta)_{ij}
( alphagamma)_{ik }+( betagamma)_{kl }+( alphabetagamma
Seminario EIO UCLM 8/66
9. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Sweave Example (cont.)
varepsilon_{ijkl},
end{equation}
And here we have a plot of effects:
<<maineff , echo=FALSE , fig=TRUE >>=
plot(c(-1, 1), ylim = range(ss.data.doe1$score),
coef(doe.model1 )[1] + c(-1, 1) * coef(doe
type ="b", pch =16)
abline(h=coef(doe.model1 )[1])
@
%input{section2}
end{document}
Seminario EIO UCLM 9/66
10. Design of Experiments
EL Cano and JM Moguerza and A Rechuk
April 10, 2012
1 Introduction
Design of experiments is the most important took in the Improve phase of the
DMAIC cycle . . . .
> 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)
Call:
lm(formula = score ~ flour + salt + bakPow + flour * salt + flour *
bakPow + salt * bakPow + flour * salt * bakPow, data = ss.data.doe1)
Residuals:
Min 1Q Median 3Q Max
-0.5900 -0.2888 0.0000 0.2888 0.5900
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.5150 0.3434 16.061 2.27e-07 ***
flour+ 1.8350 0.4856 3.779 0.005398 **
salt+ -0.8350 0.4856 -1.719 0.123843
bakPow+ -2.9900 0.4856 -6.157 0.000272 ***
flour+:salt+ 0.1700 0.6868 0.248 0.810725
flour+:bakPow+ 0.8000 0.6868 1.165 0.277620
salt+:bakPow+ 1.1800 0.6868 1.718 0.124081
flour+:salt+:bakPow+ 0.5350 0.9712 0.551 0.596779
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4856 on 8 degrees of freedom
Multiple R-squared: 0.9565, Adjusted R-squared: 0.9185
F-statistic: 25.15 on 7 and 8 DF, p-value: 7.666e-05
This is the general model:
yijkl = µ + αi + βj + γk + (αβ)ij + (αγ)ik + (βγ)kl + (αβγ)ijk + εijkl, (1)
1
11. And here we have a plot of effects:
q
q
−1.0 −0.5 0.0 0.5 1.0
34567
c(−1, 1)
coef(doe.model1)[1]+c(−1,1)*coef(doe.model1)[2]
2
12. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Project Example
Strategies
Partial Sweave files can be compiled to get
partial LATEX files. R scripts can Sweave .Rnw
files and“source”.R files. The final document
is obtained by compiling the“master”
LATEX file.
> 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")Seminario EIO UCLM 12/66
13. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
EADAPU
Seminario EIO UCLM 13/66
14. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
EADAPU - Programa
Sesi´on 1 (4 horas)
1 Introducci´on a la Metodolog´ıa Seis
Sigma.a
2 Herramientas de la fase de definici´on.
3 Herramientas de la fase de medida.
a
Incluye introducci´on a RStudio
Seminario EIO UCLM 14/66
15. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
EADAPU - Programa (cont.)
Sesi´on 2 (4 horas)
1 La importancia de experimentar.
2 Introducci´on al dise˜no de experimentos.
3 Dise˜no de experimentos como
herramienta de mejora.
4 Dise˜no robusto.
5 Dise˜nos avanzados.
Seminario EIO UCLM 15/66
16. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Contents
1 Statistical Training
Approaches
Examples
Application
2 Six Sigma with R
Six Sigma
R
Packages
Seminario EIO UCLM 16/66
17. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Publisher
http://www.springer.com/statistics/book/978-1-4614-3651-5
Seminario EIO UCLM 17/66
18. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Book website
http://www.sixsigmawithr.com/
Seminario EIO UCLM 18/66
19. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
R Package
http://cran.r-project.org/web/packages/SixSigma/index.html
Seminario EIO UCLM 19/66
20. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Contents
Foreword (Thanks David R´ıos)
Preface
Part I: Basics
Part II: R Tools for the Define Phase
Part III: R Tools for the Measure Phase
Part IV: R Tools for the Analyze Phase
Part V: R Tools for the Improve Phase
Part VI: R Tools for the Control Phase
Part VII: Further and Beyond
Seminario EIO UCLM 20/66
21. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
1. Six Sigma in a Nutshell
Herbert Spencer
“Science is organised knowledge”
Seminario EIO UCLM 21/66
22. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
2. R from the Beginning
Linus Torvalds
“Software is like sex; it’s better when it’s free”
Seminario EIO UCLM 22/66
23. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
3. Process Mapping with R
Charles Franklin Kettering
“A problem well stated is a problem half
solved”
Seminario EIO UCLM 23/66
24. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
A Process Map
Six Sigma Process Map
Paper Helicopter Project
INPUTS
X
operators
tools
raw material
facilities
INSPECTION
INPUTS
sheets
...
Param.(x):width NC
operator C
Measure pattern P
discard P
Featur.(y):ok
ASSEMBLY
INPUTS
sheets
Param.(x):operator C
cut P
fix P
rotor.width C
rotor.length C
paperclip C
tape C
Featur.(y):weight
TEST
INPUTS
helicopter
Param.(x):operator C
throw P
discard P
environment N
Featur.(y):time
LABELING
INPUTS
helicopter
Param.(x):operator C
label P
Featur.(y):label
OUTPUTS
Y
helicopterLEGEND
(C)ontrollable
(Cr)itical
(N)oise
(P)rocedure
Seminario EIO UCLM 24/66
25. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
4. Loss Funtion Analysis with R
W. Edwards Deming
Defects are not free. Somebody makes them,
and gets paid for making them
Seminario EIO UCLM 25/66
26. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
A Loss Function Example
Loss Function Analysis
10 mm. Bolts Project
0e+00
1e−04
2e−04
3e−04
4e−04
5e−04
LSL USL
T
9.6 9.8 10.0 10.2 10.4
Observed Value
CostofPoorQuality
L = 0.002 ⋅ (Y − 10)2
Data
CTQ: diameter
Y0 = 10
∆ = 0.5
L0 = 0.001
Size = 1e+05
Mean = 10.0308
k = 0.002
MSD = 0.0337
Av.Loss = 1e−04
Loss = 6.7441
Seminario EIO UCLM 26/66
27. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
5. Measurement System Analysis
Lord Kelvin
“If you cannot measure it,
you cannot improve it.”
Seminario EIO UCLM 27/66
28. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
MSA with R
Six Sigma Gage R&R Study
Helicopter Project
Components of Variation
Percent
0
20
40
60
80
G.R&R Repeat Reprod Part2Part
%Contribution %Study Var
Var by Part
var
1.0
1.2
1.4
1.6
1.8
prot #1 prot #2 prot #3
q
q
q
q
q
q
q
q
q
qq
q
q
q
q
qq
q
q
q
q
q
qq
q
q
q
Var by appraiser
var
1.0
1.2
1.4
1.6
1.8
op #1 op #2 op #3
q
q
q
q
q
q
q
q
q
qq
q
q
q
q
qq
q
q
q
q
q
qq
q
q
q
Part*appraiser Interaction
var
1.1
1.2
1.3
1.4
1.5
1.6
1.7
prot #1 prot #2 prot #3
q
q
q
q
q
q
q
q
q
op #1
op #2
op #3
x Chart by appraiser
part
var
1.1
1.2
1.3
1.4
1.5
1.6
1.7
prot #1 prot #2 prot #3
q
q
q
op #1
prot #1 prot #2 prot #3
q
q
q
op #2
prot #1 prot #2 prot #3
q
q
q
op #3
R Chart by appraiser
part
var
0.1
0.2
0.3
0.4
0.5
prot #1 prot #2 prot #3
q
q
q
op #1
prot #1 prot #2 prot #3
q
q
q
op #2
prot #1 prot #2 prot #3
q
q
q
op #3
Seminario EIO UCLM 28/66
29. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
6. Pareto Analysis with R
Ovidio
Causa latet: vis est notissima. [The cause is
hidden, but the result is known.]
Seminario EIO UCLM 29/66
30. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Cause-and-effect diagram
Six Sigma Cause−and−effect Diagram
Paper Helicopter Project
Flight Time
Operator
operator #1
operator #2
operator #3
Environment
height
cleaning
Tools
scissors
tape
Design
rotor.length
rotor.width2
paperclip
Raw.Material
thickness
marks
Measure.Tool
calibrate
model
Seminario EIO UCLM 30/66
31. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Pareto Chart
Delays
Materials
Customer
Training
Rework
Errors
Rain
Wind
Permissions
Inadequate
Temperature
Pareto Chart for b.vector
Frequency
0204060
q
q
q
q
q
q
q
q
q
q
q
80%
CumulativePercentage
Seminario EIO UCLM 31/66
32. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
7. Process Capability Analysis
Johann Wolfgang von Goethe
One cannot develop taste from what is of
average quality but only from the very best.
Seminario EIO UCLM 32/66
33. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Capability Analysis Output
Six Sigma Capability Analysis Study
Winery Project
Histogram & Density
LSL
Target
USL
740 745 750 755 760
Check Normality
q
q
q q
qq
qqq
qq
qqqqq
q q
q
q Shapiro−Wilk Test
p−value: 0.07506
Lilliefors (K−S) Test
p−value: 0.2291
Normality accepted when p−value > 0.05
Density Lines Legend
Density ST
Theoretical Dens. ST
Density LT
Theoretical Density LT
Specifications
LSL: 740
Target: 750
USL: 760
ProcessShort Term
Mean: 749.7625
SD: 2.1042
n: 20
Zs: 3.14
Long Term
Mean: 753.7239
SD: 2.6958
n: 40
Zs: 2.33
DPMO: 9952.5
IndicesShort Term
Cp: 1.5841
CI: [1.4,1.7]
Cpk: 1.5465
CI: [1.4,1.7]
Long Term
Pp: 1.2365
CI: [1.1,1.3]
Ppk: 0.7760
CI: [0.7,0.8]
Seminario EIO UCLM 33/66
34. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
8. Charts with R
John Tukey
“The greatest value of a picture is when it
forces us to notice what we never expected to
see.”
Seminario EIO UCLM 34/66
35. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Multi-vari chart
Multi−vari chart for Volume by color and operator
Filler
Volume
14
15
16
17
18
1 2 3
q
q
q
q
q
q
q
q
q
q
q
q
B
1
q
q
qq
q
q
q
q
q
q
q
q
C
1
q
q
q
q
q
q
q
q
q
q
q q
B
2
14
15
16
17
18
q
q
q
q
q
qq
q
q
q
q
q
C
2
14
15
16
17
18
q q q
q
q qq q
q
q
q
q
B
3
1 2 3
q q q
q
q
q
q q
q
q q q
C
3
batch
1 2 3 4q q q q
Seminario EIO UCLM 35/66
36. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
9. Statistics and Probability with R
Aaron Levenstein
“Statistics are like bikinis. What they reveal is
suggestive, but what they conceal is vital.”
Seminario EIO UCLM 36/66
37. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Distributions
0 1 2 3 4
0.00.3
Hypergeometric
Elements in class A
Probability
0 10 30
0.000.10
Geometric
Items extracted until first success
Probability
10 20 30 40
0.000.06
Negative Binomial
Number of trials until 3 events
Probability
0 5 10 20
0.000.15
Poison
Number of successful experiments per unit
Probability
0 1 2 3 4 5
0.00.6
Exponential
Random Variable X
ProbabilityDensity
0 2 4 6
0.00.40.8
Lognormal
Random Variable X>0
ProbabilityDensity
−0.5 0.5 1.5
0.00.61.2
Uniform
Random Variable X
ProbabilityDensity
0 2 4 6 8
0.00.20.4
Gamma
Random Variable X
ProbabilityDensity
0.0 0.4 0.8
0.01.02.0
Beta
Random Variable X
ProbabilityDensity
0 2 4 6
0.00.30.6
Weibul
Random Variable X
ProbabilityDensity
−4 0 2 4
0.00.3
t−Student
Random Variable X
ProbabilityDensity
1.73
95%
5%
10 30 50
0.000.06
Chi−squared
Random Variable X
ProbabilityDensity
30.14
95% 5%
0 1 2 3 4
0.00.6
F
Random Variable X
ProbabilityDensity
2.34
95%
5%
Seminario EIO UCLM 37/66
38. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
10. Statistical Inference with R
George E.P. Box
“All models are wrong; some models are
useful.”
Seminario EIO UCLM 38/66
39. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Confidence Interval Example
Confidence Interval for the Mean
Mean:
StdDev:
n:
Missing:
950.016
0.267
120
0
95% CI:
P−Var:
t:
[949.967, 950.064]
unknown
1.98
Shapiro−Wilks
Normality Test
0.985
p−value: 0.19
q
qq
q
qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq
qqq
q
Normal q−q Plot
0.0
0.5
1.0
1.5
949.0 949.5 950.0 950.5
Value of len
density
Histogram & Density Plot
Seminario EIO UCLM 39/66
40. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
11. Design of Experiments with R
R.A. Fisher
“Sometimes the only thing you can
do with a poorly designed
experiment is to try to find out what
it died of”
Seminario EIO UCLM 40/66
41. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
The Importance of Experimenting
“An engineer who does not know
experimental design is not an
engineer”
Comment made by to one
of the authors [of“Statistics
for experimenters”] by an
executive of the Toyota
Motor Company.
Seminario EIO UCLM 41/66
42. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
12.Process Control with R
Walter A. Shewhart
“Special causes of variation may be found and
eliminated.”
Seminario EIO UCLM 42/66
43. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Control Chart Plotting
p Chart
for stockouts
Group
Groupsummarystatistics
1 3 5 7 9 11 13 15 17 19 21
0.000.050.100.150.200.25
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
q
LCL
UCL
CL
q
Number of groups = 22
Center = 0.1212294
StdDev = 0.3263936
LCL is variable
UCL is variable
Number beyond limits = 1
Number violating runs = 0
Seminario EIO UCLM 43/66
44. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
13. Other Tools and
Methodologies
Johann Wolfgang von Goethe
Instruction does much, but encouragement
everything.
Seminario EIO UCLM 44/66
45. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Other topics
Failure Mode, Effects, and Criticality
Analysis
Design for Six Sigma
Lean
Gantt Chart
Some Advanced R Topics
Seminario EIO UCLM 45/66
46. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Case Study
Seminario EIO UCLM 46/66
47. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Helicopter Template
> ss.heli()
null device
1
> #vignette("HelicopterInstructions") t
Seminario EIO UCLM 47/66
48. Six Sigma with R | Paper Helicopter template
cut
fold ↑ fold ↓
tape?
cut
fold↓↓
cut
fold↑↑
cuttape?
tape?
clip?
min
(6.5cm)
std
(8cm)
max
(9.5cm)
←bodylength→
← body width →min
(4cm)
min
(4cm)
max
(6cm)
max
(6cm)
min
(6.5cm)
std
(8cm)
max
(9.5cm)
←wingslength→
49. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Enjoy the Case Study!
Seminario EIO UCLM 49/66
50. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Enjoy the Case Study!
Seminario EIO UCLM 49/66
51. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Book Production
Reproducible Research
Written applying reproducible research
techniques. All figures (except screen
captures) are generated while compiling the
book using R code.
Seminario EIO UCLM 50/66
52. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
The DMAIC Cycle
Seminario EIO UCLM 51/66
53. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Six Sigma Roles
In Six Sigma, everyone in the organization has
a role in the project. Six Sigma methodology
uses an intuitive categorization of these roles.
Seminario EIO UCLM 52/66
54. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Six Sigma Roles
In Six Sigma, everyone in the organization has
a role in the project. Six Sigma methodology
uses an intuitive categorization of these roles.
Seminario EIO UCLM 52/66
55. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Why 6 and why Sigma
Sigma refers to the Z-Score of the process:
Z = min
(USL − x)
σ
,
(x − LSL)
σ
; ZLT = ZST −1,5.
CTQ
Frequency
Short Term
Long Term
1.5σ 4.5σ
> (1-pnorm(4.5))*(10^6)
[1] 3.397673
DPMO
Seminario EIO UCLM 53/66
56. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
So what?
Seminario EIO UCLM 54/66
57. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
So what?
The Scientific Method
Seminario EIO UCLM 54/66
58. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
The Scientific Method and Six
Sigma
Define
Ask a question
Measure
Analyze
Improve
Control
Do some background
research
Construct a hypothesis
Test the hypothesis
with an experiment
Analyze the data and
draw conclusions
Communicate results
DMAIC Cycle Scientific Method
Seminario EIO UCLM 55/66
59. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
The Key to Success
“Six Sigma speaks the language of business”
ISO 13053-1:2011
Six Sigma methodology is a quality paradigm
that translates the involved scientific
methodology into a simple way to apply the
scientific method within every organization.
Seminario EIO UCLM 56/66
60. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
The R Project
http://www.r-project.org
Seminario EIO UCLM 57/66
61. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
The R Environment
Seminario EIO UCLM 58/66
62. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Reproducible Research
knitr, pgfSweave: enhanced options for
Sweave
exams: Automatic generation of printable
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
Seminario EIO UCLM 59/66
63. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Package RGIFT
Open format GIFT
Integration with Moodle
Automatic correction
http://cran.r-project.org/web/
packages/RGIFT/
Seminario EIO UCLM 60/66
64. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Community
Community
4131 packages at CRAN (18/11/2012)a
Bioconductor, R-forge, Github,
Omegahat.
Task views
Manuals
Publications
http:
//cran.r-project.org/web/packages/
a
4181 04/12
Seminario EIO UCLM 61/66
65. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
R Interfaces
GUI, IDE
RStudio
Eclipse + StatET
EMACS + EES
Deducer
. . .
Seminario EIO UCLM 62/66
66. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
R Interfaces (cont.)
Seminario EIO UCLM 63/66
67. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
http://r-es.org/
Seminario EIO UCLM 64/66
68. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
http://www.r-project.org/useR-2013
Seminario EIO UCLM 65/66
69. Seis Sigma con R
December, 2012
Emilio L. Cano
Statistical Training
Approaches
Examples
Application
Six Sigma with R
Six Sigma
R
Packages
Discussion
Thanks !
emilio.lopez@urjc.es
@emilopezcano
http://www.sixsigmawithr.com
Seminario EIO UCLM 66/66