Learn how to model variations in manufacturing processes and develop methods to reduce and control deterministic variations to achieve consistent process quality.
An Intermediate-Level Course by MITx
Welcome to the world of statistical process control in manufacturing! This intermediate-level course, offered by MITx, is an essential component of the Principles of Manufacturing MicroMasters program. Designed to equip you with powerful statistical methods for achieving world-class manufacturing quality, this course delves into the inherent randomness present in all manufacturing processes.
The skills acquired in this course have immediate real-world applications. Graduates have reported using these concepts daily in their professional lives, from driving quality improvements to assessing process stability and vendor performance. You'll be equipped to:
By enrolling in this course, you're not just learning theory – you're gaining practical skills that are in high demand in today's competitive manufacturing industry. Join us to develop the engineering and management skills needed for competence and success in modern manufacturing!
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This lecture series discusses how the concept of probability can be used to handle, control, and exploit uncertainty in the real-world. It is an undergraduate-level lecture series on probability, but is entirely different from the usual courses on probability theory. The lectures cover the basics of probability theory including the relevant mathematics, but instead of focusing on mathematics, the lectures explain how probability theory can help understand real-world uncertainty using various examples. The examples are used to describe how uncertainty can be exploited to implement modern randomized algorithms such as Markov chain Monte Carlo and deep learning.
This course introduces the basic tools and methods of statistical analysis, which can be applied to a wide variety of situations and data encountered in the areas of business and economics. Topics discussed include descriptive statistics, probability, sampling distributions, estimation, and hypothesis testing. By the end of the semester students should be able to compute and interpret basic descriptive statistical measures, understand the basic concepts of probability, and apply techniques of statistical inference.
