Analyse Phase – Introduction to Lean6Sigma Analyse Phase

Overview of all modules in this course

The L6S DMAIC Analyse Phase

4.3

Analysing Your Customer Process Requirements

L6S_ Analyse_Phase_Logo

The objective of the Analyse phase is to identify the sources of the process problems before considering solutions. We will then collect data, analyse it, and find out how the inputs and outputs are related using several tools and techniques.

  • Hypothesis testing
  • Degrees of Freedom
  • Analysis of Variance
  • Regression Analysis
  • Process Value Analysis
  • Control Impact Analysis
  • Process Mapping
  • ... and much much more
  1. 1
    Analyse Phase Exam Syllabus - In this module I described the full Analyse Phase Exam Syllabus and describe the key learning objectives. I review route calls analysis (RCA), and describe the three practical, graphical, and analytical techniques.
  2. 2
    Hypothesis Testing 1 - This is Part 1 of two modules covering an overview of hypothesis testing. I describe its purpose and how it works. I cover the importance of statistical significance and the part it plays in hypothesis testing.
  3. 3
    Hypothesis Testing 2 - This module is Part 2 of the hypothesis testing overview. I provide an example and the use of the P value, going on to explain the importance of hypothesis testing and the summary off the technique. I contrast and compare the difference between the null hypothesis and the alternative hypothesis.
  4. 4
    Hypothesis Testing 3  - In this module you and I look at the difference between statistical versus practical significance and its use within the one sample t-test and degrees of freedom.
  5. 5
    Degrees of Freedom - Statistical versus Practical Significance - Here, you will learn about the chi square test and degrees of freedom. I revised the six Sigma data types and show how degrees of freedom can be applied to the chi square test.
  6. 6
    Hypothesis Example and Approach - In this module you and I will look at several simple hypothesis tests examples. I explain the one tailed the value and provide a hypothesis example and approach.
  7. 7
    Hypothesis Test Types 1 - This is the first of two modules looking at hypothesis test types starting off with the hypothesis tests of means. We examine the hypothesis test of variances and of proportion. I introduce the hypothesis testing of discrete and of continuous data.
  8. 8
    Hypothesis Test Types 2 - This is the second part of looking at hypothesis test types. I start with contrasting the difference between discrete and continuous tests. I described the use of parametric tests and non-parametric tests, finishing up with an overview of the various test types
  9. 9
    Significance Level (Alpha) - In this module you and I examine the significance level (alpha), and its application to a null hypothesis example.
  10. 10
    Test Statistics - In this module you and I examine the test statistic and P value, then provide a detailed example. I complete this module by covering the use of P values and confidence intervals.
  11. 11
    Critical Values - This module covers hypothesis critical values and distribution, starting off by examining the critical value, and its application to hypothesis testing. We look at the bell curve and hypothesis, the hypothesis region of acceptance and significance level.
  12. 12
    Hypothesis Testing - In this module you and I examine the hypothesis Z score and two tailed test, starting off with looking at one tailed hypothesis testing. I describe two tailed hypothesis testing, the hypothesis Z score and provide an example using the Z table.
  13. 13
    Z Test and Critical Value - Here, I described the Z test and critical value, how it is applied, and how the Z score is obtained from the confidence level (Alpha). We complete this module by illustrating two examples of applying the Z test and using the critical value.
  14. 14
     Hypothesis Test Sequence Steps - In this module I'll summarise the key hypothesis test sequence steps, and then dive into further detail.
  15. 15
    Null Hypothesis Risks, Power, and Errors - Here, I cover the null hypothesis risks, power, and errors. Next, I describe the application of statistical power and its analysis. I clarify the types of alpha and beta risk errors.
  16. 16
    Test and Distribution 1 - This module is the first of two parts, where you and I look at the T Test and Distribution. We examine the t-test formula elements, the t-test noise and significance
  17. 17
    Test and Distribution 2 - This is the second part of the two modules looking at the T test and distribution. I provide an example of its application and purpose. We look at the team test statistical significance, and at frequency and probability distribution.
  18. 18
    Chi Squared Tests - This is part one of two modules based on chi square tests. In this module we look again in more detail at the use and application of chi square tests, starting off by looking at the chi square distribution model. I explain the use of the chi square test and introduce a simple worked example. We finished up looking at a more complex example and application of the chi squared test.
  19. 19
    Chi Squared Table - This is the second module of examining the use of chi squared tests. Here I explain the use of critical values and the application of the chi squared table. I demonstrate the use of chi square analysis probability, an example, and the use and application of chi squared degrees of freedom.
  20. 20
    Hypothesis Testing Examples -  This module consists of various examples of conducting hypothesis testing, and test types.
  21. 21
    Statistical Hypothesis -  Here, by examine the use of statistical hypothesis and prepare you for the coming 14 modules in this section and the topics that will be covered. I then summarise the steps and purpose of hypothesis testing, add an example of applying I statistical hypothesis test. I demonstrate the application of statistical hypothesis in a graphical form.
  22. 22
    T-distribution Tests - This module covers the use and application of t-distribution tests, starting off with the student's t-test. I showered example of the tea distribution table, the one titled 1 sample T test and the two tailed 1 sample T test. We finish off this module by looking at the two tailed 1 sample T test and its application.
  23. 23
    Sample T Tests - Here you and I will cover the one and two sample T tests. I will cover two detailed worked examples of the one sample and two sample T tests.
  24. 24
    Paired T Test - In this module you and I will look at the paired T test along with this formula and analysis. I provide a detailed worked example of how to apply the paired T test.
  25. 25
    ANOVA - In this module you and I will examine the analysis of variance, also known by its acronym of ANOVA. I describe what ANOVA is and its definition. I walk you through a detailed worked example.
  26. 26
    F-Statistic - Here, I explain what the F-statistic is and how to use it, then go on to demonstrate the degrees of freedom as it applies to the F-statistic. I provide a degrees of freedom example and how to set up the F-test.
  27. 27
    Test Applications - This module covers the chi-square test and the one-proportion test, starting off with an example application. We then cover a numeric example of the Chi Square test, the Proportions test, and a one proportion test example. We look again at proportion hypothesis testing with a one-p test worked example and a proportion hypothesis example.
  28. 28
    Two-Proportions - This module looks at the two-proportions test and provides a detailed worked example for a typical scenario.
  29. 29
    Scatter Diagrams and Regression Analysis - Here, you and I look at scatter diagrams and regression analysis. I start with the required steps, the regression equation, and explain simple linear, multilinear, and nonlinear regression techniques. We finish, by examining the differences between weak and strong relationships.
  30. 30
    Regressions Analysis - In this module, I explain regression analysis, and explain the use of Pearson’s Correlation Coefficient r. I provide an example of linear regression and the correlation coefficient. You learn how to develop the regression equation and the application of r-squared adjusted (Rsq).
  31. 31
    Analysis Technique - Here, I describe the process value analysis technique, and define the value added, value enabling, and non-value-added elements. I demonstrate how to create a plan of action to resolve the previous elements.
  32. 32
    ERSC Approach - This module covers the nine kinds of lean steps to reduce waste using the ERSC approach of eliminate, combine, rearrange, and simplify.
  33. 33
    Control Impact Analysis - Here, you and I cover the pareto chart and a technique called control impact analysis. I review the use of the pareto chart and its application to the 80/20 rule. I show how this can be applied with an example. You will learn what the control impact matrix is and how to apply it using an example.
  34. 34
    Process Mapping and Box Plots - This final module in the DMAIC Analyze Phase, looks at process mapping and box plots. We look again at the Ishikawa Diagram and the “As Is” process mapping technique. I provide a review of the box plot technique and how it can be applied. Finally, I provide an overview of the syllabus topics to be covered in the next DMAIC phase – the Improve Phase.
 Mark lesson complete