Statistics, Hypothesis Testing, & Regression Modeling

Using Mintab software

A little background, motivation, course overview, and a welcome from the instructor, Steven Wachs

The seminar was great, as usual. The books are excellent resources when I am back on the job.
— D.J. Gray Intier Automotive

Get started with the Statistics, Hypothesis Testing, & Regression Modeling course today

This course has 11 modules, 53 lessons, and approximately 36 hours of material, examples, and exercises. Steven is available to answer your questions and discuss course topics. Once purchased, you have full access for six months. The course is on-demand, so you can engage with the course in a way that fits your schedule and interests.

The course format is voice-over slides along with a ‘live’ view of Steven showing how to use Minitab with examples and when reviewing exercise solutions.

The course includes the recorded presentations, a Participant Guide (copies of the slides), Supplemental Textbooks, and the Minitab files that include the datasets for examples and exercises. You can download the guide, textbook, and Mintab files in the third lesson of the Course Introduction module. You may also view the guide and textbook within that lesson online.

The course does include some statistical background and theory, yet the emphasis is on applying statistical methods using Minitab software. We will examine the data setup, analysis, and interpretation over the course of the full course.

Steve Wachs was a wonderful instructor.
Gwen Case, Schrader-Bridgeport Int’l Inc.

Enroll in the Statistics, Hypothesis Testing, & Regression Modeling course.

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Training Objectives

The objective of the curriculum is to provide participants with the analytical tools and methods necessary to:

Note: Relevant modules are shown in parentheses.

Describe and summarize data effectively with descriptive statistics and graphical methods (1,2)
Utilize appropriate probability distributions to describe data (1)
Correctly compare groups with respect to means, variability, and proportions by testing hypotheses (e.g. whether the groups have a statistically significant difference) (3,4,5)
Estimate key statistics and quantify uncertainty (confidence intervals) (6)
Apply Equivalence Testing to determine if groups are the same from a practical perspective (3,4)
Characterize expected process variation based on sample data (tolerance intervals) (3)
Determine appropriate sample sizes to achieve adequate power for hypothesis tests and equivalence tests (6)
Determine appropriate sample sizes for estimation of key statistics (6)
Handle discrete data by using common discrete data distributions (7)
Conduct Chi-Square tests for relationships between categorical variables (7)
Apply Non-Parametric Hypothesis Tests when assumptions for parametric tests are violated (8)
Assess whether continuous variables have a significant relationship (correlation) (9)
Develop, validate, and utilize predictive models for continuous responses (9,10)
Develop and validate Regression Models with Binary, Ordinal, or Nominal responses (11)

Steven Wachs, Course Instructor

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

Obvious experience level was great. Appreciate the real world applications and examples.
former student

Why is This Course Important?

Summarizing Raw Data using relevant and informative statistics is required for effective decision-making. Characterizing continuous data using a good-fitting probability distribution (model) allows us to make inferences from the entire population of data. Graphical Analysis has many uses, including performing exploratory data analysis, effectively summarizing raw data, and presenting and illustrating results that non-statisticians can understand.

Hypothesis Testing is a critical tool in decision-making because it allows us to consider the inherent variation when comparing groups of data to each other or a single group versus a target. For example: Is the process quality improving over time? Are two manufacturing lines producing products of equal quality? Are two suppliers supplying components that have practically equivalent means and variances? Hypothesis testing allows us to control the risks of making errors when making decisions. Furthermore, selecting appropriate sample size is critical to ensure tests have adequate power so that appropriate conclusions may be drawn.

Often, data does not meet the necessary assumptions to use traditional (parametric) tests. In these cases, it’s important that alternate valid methods be utilized (such as nonparametric hypothesis tests).

Predictive models are invaluable for understanding relationships between variables, anticipating outcomes, and optimizing products and processes. Regression Modeling is one important technique for developing predictive models from available data. Handling discrete data is less straightforward than handling continuous data, and different methods must be utilized. This course covers important methods for assessing relationships between categorical variables and developing predictive models with binary, ordinal, and nominal responses.