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Title
Statistics in Analytical Chemistry: Part 29—Statistically Derived Detection Limits (continued)
Thursday, February 28, 2008
David Coleman
Lynn Vanatta
Part 28 (American Laboratory, Nov/Dec 2007) laid the framework for discussing detection limits. The relationship among the detection limit (DL), the probability of false positives (a), and the ...
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Statistics in Analytical Chemistry: Part 30—Statistically Derived Detection Limits (Concluded)
Thursday, July 10, 2008
David Coleman
Lynn Vanatta
In Part 29 (American Laboratory, Feb 2008), Hubaux-Vos detection limits (H-V DLs) were explained and an illustration was given.
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Statistics in Analytical Chemistry: Part 31—Receiver Operating Characteristic (ROC) Curves: Equations
Friday, September 26, 2008
David Coleman
Lynn Vanatta
Please do not despair! This article is not a series of formulas to be memorized.
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Statistics in Analytical Chemistry: Part 32—Detection Limits Via 3-Sigma
Friday, December 05, 2008
David Coleman
Lynn Vanatta
As was mentioned in the first article on detection limits (DLs) (Part 26, American Laboratory, June/July 2007), many formulas exist for calculating these values.
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Statistics in Analytical Chemistry: Part 33—Detection Limits via 3-Sigma (Concluded)
Friday, March 20, 2009
David Coleman
Lynn Vanatta
In the last installment (Part 32, American Laboratory, Nov/Dec 2008) of this series, three detection limits (DLs) were calculated (via the 3-Sigma [3s] approach, where a is set to be 0.01) for a ...
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Statistics in Analytical Chemistry: Part 34—Detection-Limit Summary
Thursday, May 28, 2009
David Coleman
Lynn Vanatta
The subject of detection limits (DLs) is a controversial one, generating many questions and almost endless debate.
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Statistics in Analytical Chemistry: Part 35—Reporting Data and Significant Digits
Thursday, August 20, 2009
David Coleman
Lynn Vanatta
"In almost all cases when dealing with a limit of detection or limit of determination, the primary purpose of determining that limit is to stay away from it.”
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Statistics in Analytical Chemistry: Part 36—Significant Digits (Concluded)
Tuesday, January 26, 2010
David Coleman
Lynn Vanatta
The previous article (Part 35, American Laboratory, Aug 2009) introduced the concept of significant digits, pointing out that a statistically sound treatment will: 1) result in a formula that depends ...
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Statistics in Analytical Chemistry: Part 37—Deviations, Errors, and Intervals (Oh My!)
Thursday, February 18, 2010
David Coleman
Lynn Vanatta
For the past seven years, this column has chiefly presented basic material related to a single theme: regression analysis of analytical data and the associated uncertainty.
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Statistics in Analytical Chemistry: Part 38—Inexact Replicates
Tuesday, May 18, 2010
David Coleman
Lynn Vanatta
In this series of articles, all of the regression discussions to date have assumed that the replicate preparations of a given concentration have been exact.
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Statistics in Analytical Chemistry: Part 39—Inexact Replicates: Example
Thursday, August 12, 2010
David Coleman
Lynn Vanatta
The previous installment (Part 38, American Laboratory, May 2010) outlined the regression-diagnostic steps needed when replicates are not exact.
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Statistics in Analytical Chemistry: Part 40—Blanks
Friday, October 29, 2010
David Coleman
Lynn Vanatta
There are indeed statistical issues related to blanks. To set the stage, a discussion of the concept itself will be helpful.
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Statistics in the Laboratory: Control Charts, Part 2
Wednesday, April 06, 2016
Stanley N. Deming
In the previous column (January/February 2016), we looked at the basic ideas behind control charts and saw how to construct a common X-bar and R chart.
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Statistics in the Laboratory: Control Charts, Part 3
Thursday, May 26, 2016
Stanley N. Deming
In the previous two columns (January/February and April 2016), we looked at the basic ideas behind control charts, saw how to construct the common X-bar and R charts and discovered that only two ...
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Statistics in the Laboratory: The Apparent Capability of an Industrial Process
Monday, January 16, 2017
Stanley N. Deming
It’s important to realize that information gets filtered and distorted by the inherent behavior of a measurement process.
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Statistics in the Laboratory: Significant Digits and the Granularity of Data
Tuesday, June 05, 2018
Stanley N. Deming
A true story: one of my former graduate students called me and said, in effect, “Help. I know I’m a good analytical chemist, but my company is making me look like a perfect analytical chemist.”
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Statistics in the Laboratory: Scedasticity
Friday, August 24, 2018
Stanley N. Deming
Homoscedastic and heteroscedastic—if nothing else, these are two good adjectives for word games.
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Statistics in the Laboratory: Standard Deviation of the Mean
Friday, February 22, 2019
Stanley N. Deming
In the last column we discussed the use of pooling to get a better estimate of the standard deviation of the measurement method, essentially the standard deviation of the raw data.
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Statistics in the Laboratory: Confidence Interval of the Mean
Wednesday, June 19, 2019
Stanley N. Deming
Estimated means are often used for making quantitative decisions (e.g., to decide if something meets specifications).
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Strategy for Improving HPLC Retention Time Prediction Accuracy Using Chromatography Simulation Software
Friday, October 01, 2010
Alexey Makarov
Zhi Chen
Fred Chan
Rekha Bhashyam
Rosario LoBrutto
Raju Vegesna
The introduction of Quality by Design, a systematic approach to product and process design and development, has shifted the paradigm in HPLC methods development from a retrospective approach to a ...
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