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## Overview

**This course is part of our Lean Six Sigma Green Belt program, which consists of four courses designed to prepare you for the International Association of Six Sigma Certification (IASSC) Green Belt exam. We recommend you take all four courses in ****the program to be fully prepared for the exam. **

Hypothesis testing allows you to make decisions about problems based upon statistically significant data. Depending on the nature of the hypothesis and data available, different tests should be applied.

In this course, you will learn about 20 different statistical tests. You will understand when to use each test, and when not to use them. You will also identify the level of risk associated with different statistics, and how best to work with them.

The training features plenty of opportunities to practice with examples, exercises and quizzes to test your knowledge. By the end of the course, you will have learned how to apply these hypothesis tests in your business processes.

The course is designed from the standpoint of making sound business decisions, not deriving proofs behind the formulas or statistics. You won't need to do any advanced math, as popular programs like Excel and Minitab will do that for you. While the computer crunches the numbers, you will learn how to read and interpret the test results to understand the messages in your data.

Highlights:

- 22 practical tutorials with videos, reference guides, exercises and quizzes.
- Designed to prepare you in part for the IASSC Green Belt exam. To prepare in full, you should also take the Lean Six Sigma Principles, Statistical Process Control, and Measurement Systems Analysis courses part of our four course Lean Six Sigma Green Belt program.
- Understand the concepts of hypotheses in problem solving.
- Identify the hypothesis testing process, and best practices that should be applied.
- Learn key decision factors for selecting which hypothesis test to use.
- Apply statistical analysis principles such as inferential statistics.
- Learn how to read residual graphs and conduct regression analysis.
- Conduct statistical hypothesis tests including T Tests, F Tests, ANOVA, and more.
- Aligned to the IASSC Lean Six Sigma Green Belt Body of Knowledge.
- The only method to earn an IASSC certification is to successfully sit for and pass an official IASSC certification™ exam, which can be taken through IASSC. We do not provide access to IASSC Certification exams.
- Earn 3 PDUs or contact hours toward your Project Management education for certification with PMI.

Once enrolled, our friendly support team and tutors are here to help with any course related inquiries.

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## Summary

## Syllabus

### Creating an Hypothesis

# 1

#### Concept of Hypothesis Testing

**Video time: 04m 56s**

# 2

#### Hypothesis Test Process

Effective hypothesis testing is a disciplined process. From writing the process, to designing the study or experiments, and finally analyzing the data, there are proven best practices that should be applied. This lesson presents and explains the hypothesis testing process as used in Lean Six Sigma.

**Video time: 05m 48s**

# 3

#### Writing Hypotheses

A well written hypothesis contains two elements, the Null hypothesis and the Alternate hypothesis. Writing a clear hypothesis that can be quickly analyzed with a statistical test is a skill that will be illustrated and practiced in this lesson.

**Video time: 07m 01s**

# 4

#### Hypothesis Tests

**Video time: 07m 42s**

### Statistical Analysis Principles for Hypothesis Testing

# 1

#### Inferential Statistics

**Video time: 06m 18s**

# 2

#### Confidence Intervals

**Video time: 07m 03s**

# 3

#### Alpha and Beta Risk

**Video time: 06m 37s**

# 4

#### The P Value

**Video time: 05m 25s**

# 5

#### Normal Versus Non-Normal

One of the most important criteria for selecting an hypothesis test is based upon whether the data is normal or non-normal. The normality question does not prove or disprove the hypothesis, rather it steers the nature of the analysis. This lesson reviews this concept and its application in hypothesis testing.

**Video time: 07m 32s**

# 6

#### Uni-, Bi-, Multi-Variate Tests

Different tests are designed to test different quantities of test samples or test parameters. The correct test will ensure a meaningful analysis.

**Video time: 05m 59s**

# 7

#### Classes of Distribution

Data sets are often displayed in distributions. Different distributions are indicative of different physical phenomena. The ability to recognize a distribution will aid in the identification of process performance issues.

**Video time: 08m 01s**

### Regression Analysis

# 1

#### Correlation

**Video time: 05m 52s**

# 2

#### Simple Linear Regression

Simple linear regression analysis creates an equation that correlates two factors. This equation both assists in understanding problems, and it can also be used to manage the problem or process going forward. This lesson shows how to calculate this line with the help of either Excel or Minitab.

**Video time: 06m 01s**

# 3

#### Residual Analysis

A statistical analysis or test creates a mathematical model to fit the data in the sample. The real world data seldom precisely fits the model. The differences between the model and the actual data is known as residuals. The residuals in any analysis, whether a regression analysis or another statistical analysis, will indicate how well the statistical model fits the data. When the residuals indicate a bad fit, a different analytical approach should be selected. This lesson explains how to read residual graphs and analysis.

**Video time: 06m 19s**

# 4

#### Multiple Linear Regression

**Video time: 04m 59s**

# 5

#### Non-Linear Regression

**Video time: 06m 31s**

### Applying Statistical Hypothesis Tests

# 1

#### Test of Proportions

The One-Sample and Two-Sample Test of Proportions are used with discrete data. These tests determine whether the percentage of a particular attribute being studied is similar to or different from the selected target value. These tests are illustrated using both Excel and Minitab.

**Video time: 06m 25s**

# 2

#### Chi Square Test

**Video time: 06m 19s**

# 3

#### One Sample Tests

One sample tests are tests of a single dataset that compares the descriptive statistics of that data set against target values.

**Video time: 05m 31s**

# 4

#### Variance Tests

Many of the hypothesis test approaches will change depending upon whether the continuous data has equal variances or unequal variances between data sets. Therefore, the F Test or Bartlett's Test must be completed to determine if variances are equal. The F test can be done with either Excel or Minitab. The Bartlett's Test can only be done using Minitab. This lesson explains these tests and how to read the statistic.

**Video time: 06m 11s**

# 5

#### T Tests

T Tests compare the mean of two data sample to each other.

**Video time: 05m 21s**

# 6

#### ANOVA

**Video time: 06m 39s**

# 7

#### One-Sample Sign / One-Sample Wilcoxon

**Video time: 05m 54s**

# 8

#### Levene's Test & Mann-Whitney Test

**Video time: 04m 19s**

# 9

#### Mood's Median, Kruskal-Wallis, and Friedman

**Video time: 06m 57s**