These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
04 - Risk analysis - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
03 - Process mapping & Bottleneck Identification - ESTIEM Lean Six Sigma Gre...ESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
05 - Measurement System Analaysis (MSA) - ESTIEM Lean Six Sigma Green Belt Co...ESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
08 - Improve - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
09 - Control - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
00 - Introduction to the Course - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
01 - Initiating a Lean Six Sigma Project - ESTIEM Lean Six Sigma Green Belt C...ESTIEM
The document discusses Lean Six Sigma concepts including defining value-adding work, types of waste, and key components of a Lean Six Sigma project charter. It provides examples of value-adding versus non-value adding processes and covers the seven most common types of waste. Additionally, it explains that a project charter helps clarify the focus and importance of a project and typically includes elements such as the problem statement, goals, scope, team, timeline and deliverables. The document is intended for an instructional session on Lean Six Sigma fundamentals.
02 - Exploratory Data Analysis (EDA) - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
04 - Risk analysis - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
03 - Process mapping & Bottleneck Identification - ESTIEM Lean Six Sigma Gre...ESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
05 - Measurement System Analaysis (MSA) - ESTIEM Lean Six Sigma Green Belt Co...ESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
08 - Improve - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
09 - Control - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
00 - Introduction to the Course - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
01 - Initiating a Lean Six Sigma Project - ESTIEM Lean Six Sigma Green Belt C...ESTIEM
The document discusses Lean Six Sigma concepts including defining value-adding work, types of waste, and key components of a Lean Six Sigma project charter. It provides examples of value-adding versus non-value adding processes and covers the seven most common types of waste. Additionally, it explains that a project charter helps clarify the focus and importance of a project and typically includes elements such as the problem statement, goals, scope, team, timeline and deliverables. The document is intended for an instructional session on Lean Six Sigma fundamentals.
02 - Exploratory Data Analysis (EDA) - ESTIEM Lean Six Sigma Green Belt CourseESTIEM
These slides are part of the ESTIEM Lean Six Sigma Green Belt course which also includes 13hr of video by Gregory H. Watson.
For more info about the ESTIEM Lean Six Sigma Green Belt course visit https://internal.estiem.org/leansixsigma
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METROLAB ENGINEERING PVT. LTD. HAVE THREE DEDICATED PLANTS IN PUNE:
7500 SQ. FT. FOR ALL TOOL ROOM AND FABRICATION ACTIVITY
6500 SQ. FT. FOR ASSEMBLY AND CMM ACTIVITY
4000 SQ.FT. FOR AUTOMATION PROJECTS & ASSEMBLY LINE SOLUTION
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-ISO 9001-2008 CERTIFIED (TUV NORD –PLANT 1)
-ISO 9001-2015 CERTIFIED (TUV AUSTRIA – PLANT 2)
-WORKING WITH ALL MAJOR OEMS, TIER1 SUPPLIER
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This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
introduction to machine learning cemp.pptcojat44069
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
grace presentation power point presentationsrajece
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Certification Guidelines for ESTIEM Lean Six Sigma Green Belt course
Link to the course materials: https://internal.estiem.org/leansixsigma/course/introduction/
A green belt practitioners guide for quality champions publication v2021ESTIEM
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Lean Six Sigma is a methodology used to reduce waste and variation to improve performance. The document discusses providing ESTIEM students the opportunity to obtain a Green Belt certificate, which typically costs money and is earned 2-5 years into careers, during their studies for a bargain. It describes the benefits for local groups and participants, including gaining important knowledge, opportunities, and a certified Green Belt. It provides guidelines for local groups organizing a Lean Six Sigma course, such as requiring 3 months notice, instructor to participant ratios, types of course models, and signing a contract regarding materials and publicity.
Conference Paper - The influence of Lean Six Sigma Green Belt course on Europ...ESTIEM
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Conferene Paper - A blended learning approach to lean six sigma green belt ed...ESTIEM
The paper: A blended learning approach to lean six sigma green belt education for European students is written by Mikko Rajala, Henri Jarrett, and Jukka-Matti Turtiainen. This paper was presented during the 61st Congress of the European Organization for Quality (EOQ) in Bled, Slovenia from 11-12 October 2017
The document describes the development of the ESTIEM Lean Six Sigma Green Belt training program from 2014-2018. It began with conversations between Jukkis and Greg in 2014 about creating an online Green Belt course for ESTIEM students. Over the following years, a small core team worked to map guidelines, create video content, test initial pilots, and expand the program. They secured funding, presented the idea across ESTIEM, and collaborated with Aalto University to host the first course. Through continuous refinement and expanding their instructor network, the program grew from training a few dozen students to over 500 graduates across 30 universities by 2018.
ESTIEM Lean Six Sigma Green Belt course - Glossary of termsESTIEM
This document provides definitions for key terms related to Lean Six Sigma. It defines over 100 terms in alphabetical order, with brief 1-3 sentence descriptions of each term. Some example terms defined include ABC (Activity Based Costing), Affinity Diagram, Alpha Risk, Alternative Hypothesis, Andon, ANOVA (Analysis of Variance), Assignable Cause, Attribute, Autonomation, and Benchmarking.
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The document describes the COncORDE project, which aims to develop a pan-European system to improve coordination between emergency services. It will create a web-based platform that allows different agencies to share information and track patients in real-time. The system is designed around common elements of emergency response across Europe to facilitate cooperation despite differences between countries. It will provide decision support, triage tools, and other applications to help coordinate multi-agency emergency responses and management of resources and patients.
METROLAB ENGINEERING PVT. LTD. HAVE THREE DEDICATED PLANTS IN PUNE:
7500 SQ. FT. FOR ALL TOOL ROOM AND FABRICATION ACTIVITY
6500 SQ. FT. FOR ASSEMBLY AND CMM ACTIVITY
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Machine learning is used to optimize a model based on examples when human expertise is limited, changes over time, or needs to be adapted to specific cases. It involves using statistics to infer patterns from data in order to build models that generalize beyond the training examples. Common applications include classification, regression, clustering, and reinforcement learning. Resources for machine learning include datasets, journals, and conferences in the field.
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This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
This document introduces machine learning and provides an overview of key concepts. It discusses why machine learning is used when human expertise is limited, solutions change over time, or need to be adapted. Machine learning builds models from data to make predictions or decisions without being explicitly programmed. The document outlines applications of supervised learning techniques like classification and regression as well as unsupervised learning and reinforcement learning. It also lists resources for datasets, journals, and conferences in the machine learning field.
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07 - Analysis Regression - ESTIEM Lean Six Sigma Green Belt Course
1. European Students of Industrial Engineering and Management
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Regression Analysis
for publication
1
2. European Students of Industrial Engineering and Management
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It’s Feedback time!
2
Persons giving feedback
Link to the feedback form
(can be found on Schoology and
WhatsApp)
Instructor to give feedback toPersons giving feedback
INSTRUCTOR 1
INSTRUCTOR 2
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What is Regression Analysis?
3
4. European Students of Industrial Engineering and Management
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Regression Analysis
4
How the change in X affects
the Y measure?
E.g. temperature of the oven (X),
Baking time of bread (Y)
5. European Students of Industrial Engineering and Management
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What practical examples there
could be for the
Regression Analysis?
5
6. European Students of Industrial Engineering and Management
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Hostel situation in Europe
6
Y measure: satisfaction
x factors:
• Number of showers/toilets
• Number of roommates
• Snoring level
• Privacy
• …
7. European Students of Industrial Engineering and Management
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Regression Analysis can be used to
7
Find the optimal
value for X
Prediction
Choose the
vital X’s
8. European Students of Industrial Engineering and Management
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8
Find the optimal
value for X
Prediction
Choose the
vital X’s
9. European Students of Industrial Engineering and Management
www.estiem.org
Find the optimal
value for X
Prediction
Choose the
vital X’s
9
10. European Students of Industrial Engineering and Management
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10
Find the optimal
value for X
Prediction
Choose the
vital X’s
11. European Students of Industrial Engineering and Management
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Let´s talk about some Regression basics.
12. European Students of Industrial Engineering and Management
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Interpreting Regression Analysis Output
12
Regression
equation
13. European Students of Industrial Engineering and Management
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Interpreting Regression Analysis Output
13
Regression
equation
Y-intercept
14. European Students of Industrial Engineering and Management
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Interpreting Regression Analysis Output
14
Regression
equation
Y-intercept
Slope of the line
15. European Students of Industrial Engineering and Management
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Interpreting Regression Analysis Output
15
Plotted line of the
regression equaltion
16. European Students of Industrial Engineering and Management
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Interpreting Regression Analysis Output
16
Confidence interval of
the prediction line
17. European Students of Industrial Engineering and Management
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Interpreting Regression Analysis Output
17
Prediction interval for
the next data point
18. European Students of Industrial Engineering and Management
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Interpreting Regression Analysis Output
18
Coefficient of
Determination (R2)
adjusted
19. European Students of Industrial Engineering and Management
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Interpreting Regression Analysis Output
19
Regression
equation
Y-intercept
Slope of the line
Potted line of the
regression equaltion
Coefficient of
Determination (R2)
Confidence interval of
the regression equation
Prediction interval for
the next data point
20. European Students of Industrial Engineering and Management
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Time to work…
20
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SPONSOR MESSAGE
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It’s Feedback time!
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Persons giving feedback
Link to the feedback form
(can be found on Schoology and
WhatsApp)
Instructor to give feedback toPersons giving feedback
INSTRUCTOR 1
INSTRUCTOR 2
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It’s Feedback time!
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Feedback box
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Usage license of the
materials
https://creativecommons.org/licenses/by-nc/4.0/legalcode
Contact leansixsigma@estiem.org
for commercial usage of the materials
Materials of the trainings have been created based on the materials provided by
ESTIEM Summer Academy Professor Gregory H. Watson