Lean Six Sigma Black Belt Course

About This Course

A Comprehensive Certified Six Sigma Black Belt Training & Sure Shot Way to Become a Master of Six Sigma

As per Indeed, a job site's survey, Certified Six Sigma Black Belt salaries range between $100,000 - $200,000. Lean Six Sigma Black Belts command a  premium in Job market. CSSBB & LSSBB deliver business results, so there are 75% more likely to be promoted that one without, but with similar  domain experience.

• This Lean Six Sigma Black Belt Training will help you succeed in accredited certification exam or process to become a certified Lean Six Sigma Black Belt because the BoK is based on Global Certification Bodies such as IASC and AQ curriculums.

• Instructor is an Accredited Training Associate.

• Every topic is application based. It starts with a business scenario and Six Sigma concepts are introduced subsequently.

• There are 75+ Data Files & Practices Files for you to download.

• You can follow the step-by-step instructions as you see in the lecture and mirror the instructor. It is a great way to master advanced statistical and analytics tools covered in Lean Six Sigma Black Belt body of knowledge

• Over Templates

• Over 40 Minitab Instruction Videos on advanced Six Sigma Black Belt Level topics are included in this Online Black Belt Course

Student Testimonials:

"I passed six sigma black belt certification exam. Black belt course and practice tests played pivotal role for cracking the test in one go. Thanks Nil !" - Sandeep J.

"This training material assisted me in the preparation of ASQ CSSBB exam. There are a lot of real-world examples included. Great Work! "- Temesgen E.

"Very thorough, will absolutely help a serious professional reach "create" level of proficiency - "Mastery" - Matthew M.

"The training was full of knowledge on whole plethora of Six Sigma application. The lessons were very informative in very simple languages. I recommend all to pursue this course under Udemy. Special Thanks to mentor Mr. Nilakantasrinivasan Janakiraman Sir". - Mofidur R.

CERTIFIED LEAN SIX SIGMA BLACK BELT Body of knowledge covered in this course are: 

• Black Belt leadership        

  • Expectations from a Black Belt role in market   

  • Leadership Qualities   

  • Organizational Roadblocks & Change Management Techniques   

  • Mentoring Skills

     

• Basic Six Sigma Metrics               

  • CTQ Tree, Big Y , CTX   

  • Including DPU, DPMO, FTY, RTY, Cycle Time, Takt time   

  • Sigma scores with XL, Z tables, Minitab   

  • Target setting techniques   

  • Role of Benchmarking

     

• Business Process Management System      

  • BPMS and its elements   

  • Benefits of practicing BPMS (Process centricity and silos)   

  • BPMS Application scenarios   

  • BSC Vs Six Sigma

       

• MSA             

  • Performing Variable GRR using ANOVA/X-bar R method   

  • Precision, P/T , P/TV, Cont %, No. of Distinct Categories   

  • Crossed & Nested Designs   

  • Procedure to conduct Continuous MSA   

  • Performing Discrete GRR using agreement methods for binary and ordinal data   

  • Agreement & Disagreement Scores for part, operator, standard   

  • Kappa Scores Computation for ordinal data and criteria for acceptance of gage

     

• Statistical Techniques      

  • Probability Curve, Cumulative Probability, Inverse Cumulative Probability (Example and procedure), Shape, Scale and Location  parameters   

  • Types of Distributions ( Normal, Weibull, Exponential, Binomial, Poisson) & their interpretation and application   

  • Identifying distributions from data   

  • Central Limit Theorem - Origin, Standard Error, Relevance to Sampling   

  • Example & Application of Central Limit Theorem

     

• Sampling Distributions     

  • Degrees of Freedom   

  • t-distribution - Origin, relevance, pre-requisites, t-statistic computation   

  • Chi-square distribution - Origin, relevance, pre-requisites, Chi-square statistic computation, Approximation to discrete data   

  • F-distribution - Origin, relevance, pre-requisites, F-Statistic and areas of applications   

  • Point & Interval estimates - Confidence and Predictive estimates for Sampling distributions   

  • Application of Confidence Estimates in decision making

       

• Sampling of Estimates      

  • Continuous and Discrete Sample Size Computation for sampling of estimates   

  • Impact of Margin of Error, standard deviation, confidence levels, proportion defective and population on sample size   

  • Sample Size correction for finite population   

  • Scenarios to optimize Sample Size such as destructive tests, time constraints

       

• Advanced Graphical Methods     

  • Depicting 1 or 2 variables (with example and procedure)   

  • Dot Plot   

  • Box Plot   

  • Interval Plot   

  • Stem-and-Leaf Plot   

  • Time Series & Run Chart   

  • Scatter Plot   

  • Marginal Plot   

  • Line Plots   

  • Depicting 3 variables  (with example and procedure)   

  • Contour Plot   

  • 3D scatter Plot   

  • 3D Surface Plot   

  • Depicting > 3 Variables  (with example and procedure)   

  • Matrix Plot   

  • Multi Vary Chart

     

• Inferential Statistics          

  • Advanced Introduction to Hypothesis Tests   

  • Significance and implications of 1 tail and 2 tail   

  • Types of Risks - Alpha and Beta Risks   

  • Significance & computation of test statistic, critical statistic,  p-value

       

• Sample Size for Hypothesis Tests          

  • Sample Size computation for hypothesis tests   

  • Power Curve   

  • Scenarios to optimize Sample Size, Alpha, Beta, Delta such as destructive tests

     

• Hypothesis Tests               

  • 1Z, 1t, 2t, Parried t Test - Pre-requisites, Components & interpretations   

  • One and Two Sample Proportion   

  • Chi-square Distribution   

  • Ch-square Test for Significance & Good of Fit  - Components & interpretations

     

• ANOVA & GLM       

  • ANOVA - Pre-requisites, Components & interpretations   

  • Between and Within Variation, SS, MS, F statistic   

  • 2-way ANOVA - Pre-requisites, Interpretation of results   

  • Balanced, unbalanced and Mixed factors models   

  • GLM - Introduction, Pre-requisites, Components & Interpretations

       

• Correlation & Regression             

  • Linear Correlation - Theory and computation of r value   

  • Non-linear Correlation - Spearman's Rho application and relevance   

  • Partial Correlation - Computing the impact of two independent variables   

  • Regression - Multi-linear  Components & interpretations   

  • Confidence and Prediction Bands, Residual Analysis, Building Prediction Models   

  • Regression – Logistic(Logit) & Prediction - Components & interpretations with example

       

• Dealing with Non-normal data     

  • Identifying Non-normal data   

  • Box Cox & Johnson Transformation

       

• Process Capability            

  • Process Capability for Normal data   

  • Within Process Capability, Sub-grouping of data   

  • Decision Tree for Type of Process Capability Study   

  • Process Capability of Non-normal data - Weibull, Binomial, Poisson Process Capability and interpretation of results

       

• Non Parametric Tests       

  • Mann-Whitney   

  • Kruskal-Wallis   

  • Mood’s Median   

  • Sample Sign   

  • Sample Wilcoxon

     

• Experimental Design         

  • DOE terms, (independent and dependent variables, factors, and levels, response, treatment, error, etc.)   

  • Design principles (power and sample size, balance, repetition, replication, order, efficiency, randomization, blocking,  interaction, confounding, resolution, etc.)   

  • Planning Experiments (Plan, organize and evaluate experiments by determining the objective, selecting factors, responses and  measurement methods, choosing the appropriate design,   

  • One-factor experiments (Design and conduct completely randomized, randomized block and Latin square designs and evaluate their  results)   

  • Two-level fractional factorial experiments (Design, analyze and interpret these types of experiments and describe how confounding  affects their use)   

  • Full factorial experiments (Design, conduct and analyze full factorial experiments)

     

• Advanced Control Charts             

  • X-S chart   

  • CumSum Chart   

  • EWMA Chart    

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Note: We are not a representative of ASQ®, IASSC®

ASQ® is the registered trademark of the American Society for Quality.

IASSC® is the registered trademark of the International Association for Six Sigma Certification.

We are an independent training provider. We are neither currently associated nor affiliated with the above mentioned. The name and title of the certification exam mentioned in this course are the trademarks of the respective certification organization. The Fair Use of these terms are for describing the relevant exam and the body of knowledge associated.

• Prepare for Lean Six Sigma Black Belt Certifications (CSSBB , LSSBB)

• Perform advanced data analysis using Minitab with 75+ datasets

• Downloadable material for future reference in each section covering Body of knowledge of accreditation bodies