### +Multi-vari Studies, Correlation, and Linear Regression in Six Sigma

**Overview/Description**

In the Analyze stage of the Six Sigma DMAIC process, project teams carefully analyze process output and input variables. The goal of this analysis is to narrow down the many possible inputs identified during the Measure stage. The analysis is carried out using tools that help identify a few probable root causes that are impacting process performance. This course introduces some key tools used for exploratory data analysis in Six Sigma, such as multi-vari studies, correlation analysis, and regression models. It also explains the correlation coefficient and its statistical significance. In addition, the course helps you interpret the linear regression equation, understand the steps in hypothesis testing for regression statistics and explore the use of a regression model for prediction and estimation of outcomes. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward achieving their certifications and becoming productive members of their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Multi-vari Studies, Correlation, and Linear Regression in Six Sigma**

- identify characteristics of a multi-vari analysis
- identify guidelines for creating sampling plans
- recognize actions involved in carrying out a multi-vari analysis
- match variation types with corresponding characteristics
- interpret given variation results
- use variation results to determine the focus of a multi-vari study
- identify uses of correlation analysis in Six Sigma
- make inferences about data based on a given scatter diagram
- recognize considerations for interpreting the correlation coefficient
- determine the relationship between variables given scatter diagrams
- identify characteristics of causation
- identify reasons why Six Sigma teams should determine the statistical significance of a correlation coefficient
- recognize the significance of determining causation and p-value for a set of variables
- recognize how linear regression is used during data analysis
- sequence the steps for hypothesis testing for regression statistics
- calculate the t-statistic
- calculate an outcome using the simple least-squares linear regression formula
- use the p-value method to validate a hypothesis test for a given regression equation

### +Introduction to Hypothesis Testing and Testing for Means in Six Sigma

**Overview/Description**

During the Analyze phase of a Six Sigma improvement project, the team conducts a number of statistical analyses to determine the nature of variables and their interrelationships in the process under study. Team members typically collect samples of the population data to be analyzed, and based on that sample data, they make hypotheses about the entire population. Because there is a lot at stake in forming the correct conclusions about the larger population, Six Sigma teams validate their inferences using hypothesis tests. This course introduces basic hypothesis testing concepts, terminologies, and some of the most commonly used hypothesis tests – one- and two-sample tests for means. The course also discusses the importance of sample size and power in hypothesis testing, as well as exploring issues relating to point estimators and confidence intervals in hypothesis testing. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward their certification and to become productive members on their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Introduction to Hypothesis Testing and Tests for Means in Six Sigma**

- identify the purpose of hypothesis testing
- match elements of a hypothesis test with corresponding descriptions
- identify best practices when establishing the practical significance of hypothesis testing results
- demonstrate your understanding of basic concepts related to hypothesis testing
- recognize how confidence intervals are used in hypothesis testing
- recognize the attributes of Type I and Type II errors
- classify estimates and error types
- identify factors that affect the power of a hypothesis test
- determine sample size for a given alpha risk level using margin of error formula
- determine the power and appropriate sample size for a given hypothesis test
- sequence the steps in the hypothesis testing process
- match examples of alternative hypotheses with their corresponding probability distribution graphs
- determine whether to reject a null hypothesis based on given critical values and p-values
- use steps in the hypothesis testing process
- perform a one-sample hypothesis test for mean, given a scenario
- carry out one-sample hypothesis tests for means
- test a hypothesis using a two-sample test for means (pooled)
- carry out a two-sample hypothesis test for means
- test a hypothesis using a two-sample test for means (non-pooled)
- carry out a two-sample hypothesis test for means

### +Hypothesis Tests for Variances and Proportions in Six Sigma

**Overview/Description**

As a Six Sigma project moves into the Analyze phase, team members identify possible sources of variation, underlying root causes, and areas for improvement. It is here where assumptions or hypotheses about a process, product, or service are made and validated using tests based on sample data. This course will familiarize you with some of the advanced hypothesis tests used in Six Sigma, such as test for proportions, variances, and analysis of variance (ANOVA). You will learn how to use Paired-comparison t-test and chi-square tests for validating hypotheses. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward achieving their certifications and becoming productive members of their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Hypothesis Tests for Variances and Proportions in Six Sigma**

- identify types of hypothesis tests
- distinguish between examples of paired-comparison and two-sample t-tests
- interpret the results of a given paired-comparison t-test using the critical value method
- interpret the results of a given paired-comparison t-test using the p-value method
- determine whether to accept a null hypothesis based on given paired-comparison t-test results
- conduct a one-sample test for variance, given a scenario
- conduct a one-sample test for variance
- conduct a two-sample test for variance, given a scenario
- conduct a two-sample test for variance
- recognize the required sample size for a test for proportions, given the hypothesized proportion
- conduct a one-sample test for proportion, given a scenario
- conduct a two-sample test for proportion, given a scenario
- conduct one- and two-sample tests for variance
- match ANOVA concepts to their corresponding definitions
- conduct a one-way ANOVA test, given a scenario
- conduct a one-way ANOVA test for means
- recognize valid parameters and interpretations related to chi-square tests
- use a given contingency table to perform a chi-square test
- conduct a chi-square test, given a scenario