In this webinar, we explore the impact that chart selection and sample size have on chart sensitivity (the ability to detect changes).

Let's make a comparison. Let's check our product's key characteristics against specifications, compare vendor life data, or compare the newest design changes to prior performance. We can and should do comparisons well using the appropriate statistical approach.
Let's examine a handful of parametric and non-parametric comparison tools, including various hypothesis tests. Plus how to best use these tools and when to set them aside and explore another approach.
Statistics is a part of what a reliability professional does; thus, we all should know the range, power, and applicability of the methods available. Let's explore how to make comparisons well.
This Accendo Reliability webinar was originally broadcast on 11 June 2024.
Sample Size for Hypothesis Testing of μ article
Single Sample Z-test Hypothesis Testing for Mean with Known Variance article
Hypothesis Testing article
Hypothesis Test Selection article
Hypothesis Test Selection Flowchart article
In this webinar, we explore the impact that chart selection and sample size have on chart sensitivity (the ability to detect changes).
Let's explore some cases where effectively using statistical process control will enhance your product's reliability performance.
Let's discuss process variation and how to measure, monitor, and control processes to minimize the differences from one part to the next.
Progressive manufacturers use control charts to listen to their processes to detect and rectify potentially harmful changes.
if you need to learn a bit more about manufacturing and how we measure quality in general? Join us for this webinar.
You may have heard of Statistical Process Control, 6-sigma, Shewhart, the Japanese Economic Miracle, X̅ R Charts or perhaps none of them.
A process is considered capable' if it creates products that, with production natural variation, are still within what good' looks like.
Statistical control charting (SPC) is a method for monitoring and determining whether a process is in control or stable.
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