The course then shifts to binary logistic regression, which is used for binary classification. This includes an overview of how to evaluate models and interpret results. Next, the course moves onto model building, where students will learn how to handle regression equations with "wrong" predictors and use stepwise regression to find optimal models in Minitab. Students will also learn how to make predictions for new observations using confidence intervals and prediction intervals. This includes a thorough explanation of statistically significant predictors, multicollinearity, and how to handle regression models that include categorical predictors, including additive and interaction effects. The course then delves into regression analysis in detail, covering the different types of regression models and how to use Minitab to evaluate them. This is followed by an overview of the basics of supervised learning, including how to learn, the different types of regression, and the conditions that must be met to use regression models in machine learning versus classical statistics. The course begins with an introduction to machine learning, where students will gain an understanding of what machine learning is, the different types of machine learning, and the difference between supervised and unsupervised learning. The course also covers tree-based models for binary and multinomial classification. The course covers the fundamental concepts of regression analysis and binary logistic classification, including how to evaluate models and interpret results. This comprehensive course is designed to provide a detailed understanding of the basics of machine learning using Minitab, with a focus on supervised learning. Training Support: Yes, questions may be sent via email and a Lean Six Sigma Consultant will help you out.Course Title: Machine Learning Basics with Minitab Exam: 25 questions, 60% score required, 5 re-takes allowed. Timing: at least 4 hours including student's application of actual data. Delivery: Self-paced on-demand eLearning. In this training, you'll learn the basics of these tools and techniques, and continue to add to your complex problem solving toolbox. Why you should enroll for Control Charts Training & Certificate? Special cause variation, as distinct from common cause variation, refers to changes in process performance due to sporadic or rare events indicating that a process is not “in control.” The advantage of Control Charts is that they enhance the understanding of process variation making it easier to take action to reduce special cause variation and improve ongoing process performance. Successful completion earns the student an SPC Certificate, equivalent to 10 QDUs (Quality Development Units) as a partial requirement in earning a Lean Six Sigma Black Belt Certification.Ĭontrol Charts are time charts designed to display signals or warnings of special cause variation. Also, there is an online assessment at the end of the program to ensure understanding of concepts, applications of tools. Videos, handouts, practice data sets, and articles are included. The program is hands-on, as much as 50% of your time is spent working through follow-along sessions using Minitab. This Control Chart training will walk you through the details of how to select, how to prepare, analyze and apply control charts within your organization. Control Charts, a major tool of Statistical process control (SPC), help you monitor process behavior.
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