![]() Plot the average bias for each part on a chart. Calculate bias for each measurement (measured value minus reference value).Ħ. Have the operator measure each sample at least ten times.Ĥ. Find the reference value for each sample (determine what the actual value should be).ģ. ![]() Take at least five samples spread across the entire range of possible measurements.Ģ. How to Calculate LinearityĬalculating linearity requires the following eight steps:ġ. How good is your measurement system? Can you rely on it, or do you need better equipment or measurement methods? Performing a linearity study can help engineers and managers answer these questions and eliminate sources of measurement error. If the variation found in a study is no more than the expected measurement error, then the difference isn’t statistically significant. It’s hard to determine the cause of output differences in a study unless you know how much of that variation is due to measurement error. Helps You Determine How Much Variation Is Due to Measurement Error Knowing how a bias changes over the entire measurement range is a critical piece of information if you wish to limit the impact of measurement error on your process studies. Lets You Know How Bias Changes Over An Entire Range There are three main benefits of determining the linearity of your measurements: 1. Returning to the weight example used above, if your scale weighs heavier than the actual weight, is that difference a consistent one pound over no matter how heavy the object might be? Or does the variation from actual weight increase as the weight increases? The Benefits of Quantifying Linearity ![]() Linearity measures the consistency of a bias over the entire range of possible measurements. For example, every measure taken with your scale comes out heavier than the actual weight. Bias is a consistent mistake that occurs in measurement. Before we can understand linearity, we must understand another measurement error known as bias.
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