Most of us are familiar with the data analysis method called regression analysis. Maybe not familiar enough in some cases. The analysis allows us to identify and model trends or relationships. For example, we might examine the relationship between the speed of a production line and the number of out-of-spec items.

While regression analysis is a common method that many of us use regularly, understanding some of the limitations and options can enhance our analysis insights and accuracy. Understanding the assumptions and how to test them is a fundamental skill worth mastering.

Let's explore a few of the limitations, checks, and options when taking a look at some data. From checking assumptions to assessing the goodness of fit, and a quick exploration of various approaches beyond simple linear regression.

This Accendo Reliability webinar was originally broadcast on 14 October 2025.

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Field Data Analysis First Look article

The Variety of Statistical Tools article

Let's find the motivation to use reliability statistics and find the resources to learn the statistical tools necessary to succeed.

Let's explore R software's many capabilities concerning reliability statistics from field data analysis, to statistical process control.

Let's explore an array of distributions and the problems they can help solve in our day-to-day relaibility engineering work.

Perry discusses the basics of DOE (design of experiments) and fundamentals so you can get started with they useful product development tool.

Let's discuss the 6 basic considerations to estimate the necessary sample size to support decision making.

When we make a measurement, we inform a decision. It's important to have data that is true to the actual value.

One of the first things I learned about data analysis was to create a plot, another, and another. Let the data show you what needs attention.

If you want a really easy introduction or review of these functions that help inform a decision then check out this webinar.

Sometimes we have to work out how many of them we need (if they make up a fleet) or how many spare parts we need to keep them running.

Let's explore the ways we use, or should use, statistics as engineers. From gathering data to presenting, from analyzing to comparing.

Let's explore what residuals are, where they come from, and how to evaluate them to detect if the fitted line (model) is adequate or not.

This webinar is a light (re)introduction into common mathematical symbols used in many engineering scenarios including reliability.

Reliability is a measure of your product or system. Confidence is a measure of you. But we often forget this.

How to calculate Gage discrimination - the more useful result for a design situation, and even how to use it for destructive tests.

For those who conduct reliability data analysis or turning a jumble of dots (data points) into meaningful information

It is not just a pretty shape' that seems to work, It comes from a really cool physical phenomena that we find everywhere.

Let's examine a handful of parametric and non-parametric comparison tools, including various hypothesis tests.

You need to have a good idea of the probability distribution of the TTF of your product when it comes to reliability engineering.

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