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Please note: This course reflects content from an older version. You are welcome to complete this course, but the latest version is now available.
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.
- 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.
Creating an Hypothesis Free Lesson
Concept of Hypothesis Testing
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.
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.
Statistical Analysis Principles for Hypothesis Testing
Alpha and Beta Risk
The P Value
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.
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.
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.
Regression Analysis Free Lesson
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.
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.
Multiple Linear Regression
Applying Statistical Hypothesis Tests
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.
Chi Square Test
One Sample Tests
One sample tests are tests of a single dataset that compares the descriptive statistics of that data set against target values.
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.
T Tests compare the mean of two data sample to each other.