Lean Six Sigma Training Video | Lean Six Sigma - Analyze Phase

• published: 23 Sep 2015
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http://www.simplilearn.com/quality-management/lean-six-sigma-black-belt-training?utm_campaign=LSSGB-analyze-phase-cBWfcAty98o&utm;_medium=SC&utm;_source=youtube Analyze Phase The fourth lesson of the Lean Six Sigma Green Belt Course offered by Simplilearn. This lesson will cover the details of the analyze phase. In the Lean Six Sigma process, you begin with the define phase where you define the problem and then the current process performance is measured. Next is the analyze phase, where processes and data are analyzed to identify the root cause of the problem. After completing this lesson you will be able to: -Explain the patterns of variation -Describe the classes of distributions -Discuss Multi-Vari studies and its causes -Explain correlation and its types -Discus the various hypothesis tests -Discuss the application of F-test, t-test, ANOVA and Chi-squared (Pronounce as: khai squared) Patterns of variation In this topic, we will learn about the patterns of variation in detail. Classes of Distributions When data obtained from the measurement phase is plotted on a chart, it is observed that it exhibits a variety of distributions, depending on the data type and its source. These distribution patterns will help you understand the data better. Probability, statistics, and inferential statistics are the methods used to describe the parameters for the classes of distributions. Click each method to learn more. Probability is based on the assumed model of distribution and it is used to find the chances of a certain outcome or event to occur. Statistics uses the measured data to determine a model to describe the data used. Inferential statistics describe the population parameters based on the sample data using a particular model. Types of Distributions Let us discuss the types of distributions. There are two types of distributions, discrete distribution, and Continuous distribution. Discrete distribution includes binomial distribution and poisson distribution. Continuous distribution includes: Normal Distribution, Chi-square Distribution, t Distribution, and F Distribution. Watch full video to know more about the following topics: Discrete Probability Distribution, Binomial Distribution, Calculating Binomial Distribution—Example, Poisson Distribution, Poisson Distribution—Formula, Calculating Poisson Distribution—Example, Continuous Probability Distribution, Normal Distribution, Calculating Normal Distribution—Example, Z-Table Usage, Z-Table, Chi-Square Distribution, t–Distribution, F-Distribution, Exploratory Data Analysis, Multi-Vari Studies, Create Multi-Vari Chart, Simple Linear Correlation, Correlation Levels, Regression, Key Concepts of Regression, Simple Linear Regression (SLR), Least Squares Method in SLR, SLR Example, Multiple Linear Regression, Key Concepts of Multiple Linear Regression, Difference between Correlation and Causation, HYPOTHESIS TESTING, Statistical and Practical Significance of Hypothesis test, Null Hypothesis vs. Alternate Hypothesis, Type I and Type II Error, Important Points to remember about Type I and Type II Errors, Power of Test, Determinants of Sample Size - Continuous Data, Standard Sample Size Formula - Continuous Data, Standard Sample Size Formula - Discrete Data, Hypothesis Testing Roadmap, Hypothesis Test For Means (Theoretical) – Example, Hypothesis Test for Variance-Example, Hypothesis Test for Proportions-Example, Comparison of Means of Two Processes, Paired Comparison Hypothesis Test For Means (Theoretical), Paired-Comparison Hypothesis Test for Variance – f-Test Example, Hypothesis Test For Equality of Variance – F-Test Example, Hypothesis Tests: f-Test For Independent Groups, F-Test Assumptions, F-Test Interpretations, Hypothesis Tests: t-Test for Independent Groups, 2-Sample t-Test, Assumptions of 2-Sample Independent t-Test, 2-Tailed vs. 1-Tailed Probability, 2-Sample Independent t-Test Results and Interpretations, Paired t-Test, Sample Variance, Sample Variance—Example, ANOVA - Comparison of More Than Two Means, ANOVA Example, Chi Square Distribution, Chi Square Test – An Example, Hypothesis Testing with Non-Normal Data, Mann-Whitney Test, Mann-Whitney Test-Example, Kruskal-Wallis Test, Mood’s Median Test, Friedman Test, 1 Sample Sign Test, 1 Sample Wilcoxon, Characteristics of 1 Sample Wilcoxon, ase will be discussed in the next lesson. For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0