Statistics Spotlight


Eight Essential Tools

by Snee, Ronald D.

The broad use of Six Sigma since its introduction in the 1980s has taught us much about how to make the best use of Six Sigma tools. Now we need to take a look back and reflect on what we’ve learned...

Column: Statistics Roundtable: One Way To Moderate Ceiling Effects

by Gunst, Richard F; Barry, Thomas E

Multiple linear regression models are ordinarily defined with a continuous (usually normally distributed) response variable. In many applications of regression modeling, however, the response variable is constrained by fixed, achievable lower and upper...

One way to moderate ceiling effects.

by Gunst, Richard F.; Barry Thomas E.

2. Since the predictor and response have the same upper and lower limits, and the extreme values on response and predictor variables most influence a regression fit, you could expect a regression fit to be forced to have a slope of 1. This means the esti...

Column: Statistics Roundtable: n (How many samples do you need to take?)

by Hare, Lynne B.

When tomato sauce is suspected of having accidental onion inclusions, how many samples are needed to find out if it's true? And how many samples are needed to find out how much onion got...

Column: Statistics Roundtable: Making a Decision Under Uncertain Circumstances

by Hunter, J. Stuart

Many quality engineers have never taken a formal course in how to make decisions under uncertainty. Instruction on how to perform statistical tests of hypotheses, such as t or chi-square tests, provides a form of decision making under uncertainty;...

Column: Statistics Roundtable: Another Data Mining Tool

by Mason, Robert L.; Young, John C.

In this column, we introduce the use of a Hotelling's T2 statistic as a data mining tool for large and small data sets composed of many variables. We will show how the T2 statistic, based on a single p-dimensional observation vector (x1, x2, ..., xp),...

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