Measuring dispersed spot of positioning CMOS camera from star image quantitative interpretation based on a bivariate-error least squares curve fitting algorithm

2017 ◽  
Author(s):  
Fan Bu ◽  
Yuehong Qiu ◽  
Dalei Yao ◽  
Xingtao Yan
Keyword(s):  
Least Squares ◽  
Curve Fitting ◽  
Quantitative Interpretation ◽  
Cmos Camera ◽  
Star Image ◽  
Fitting Algorithm ◽  
Least Squares Curve Fitting ◽  
Curve Fitting Algorithm

Statistical characterization of the random errors in the radioimmunoassay dose--response variable.

1976 ◽  
Vol 22 (3) ◽  
pp. 350-358 ◽  
Author(s):  
D Rodbard ◽  
R H Lenox ◽  
H L Wray ◽  
D Ramseth
Keyword(s):  
Least Squares ◽  
Dose Response ◽  
Curve Fitting ◽  
Response Curve ◽  
Dose Response Curve ◽  
Random Errors ◽  
Statistical Characterization ◽  
Response Variable ◽  
Least Squares Curve Fitting ◽  
The Relationship

Abstract We have developed practical methods for evaluating the magnitude of the random errors in radioimmunoassay dose--response variables, and the relationship between this error and position on the dose--response curve. This is important: to obtain appropriate weights for each point on the dose--response curve when utilizing least-squares curve-fitting methods; to evaluate whether the standards and the unknowns are subject to error of the same magnitude; for quality-control purposes; and to study the sources of errors in radioimmunoassay. Both standards and unknowns in radioimmunoassays for cAMP and cGMP were analyzed in triplicate. The same mean (Y), sample standard deviation, sy, and variance (2-y) of the response variable were calculated for each dose level. The relationship between s 2-y and y was calculated utilizing several models. Results for standards and unknowns from several assays were pooled, and a curve smoothing procedure was used to minimize random sampling errors. This pooling increased the reliability of the analysis, and confirmed the presence of the theoretically predicted nonuniformity of variance. Thus, the calculation of results from these radioimmunoassays should utilize a weighted least-squares curve-fitting program. These analyses have been computerized, and can be used as a "pre-processor" for programs for routine analysis of results of radioimmunoassay.

A Fourier Transform Infrared Study of Mineral Matter in Coal: The Application of a Least Squares Curve-Fitting Program

1981 ◽  
Vol 35 (1) ◽  
pp. 102-106 ◽  
Author(s):  
Paul C. Painter ◽  
Susan M. Rimmer ◽  
Randy W. Snyder ◽  
Alan Davis
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Curve Fitting ◽  
Fourier Transform Infrared ◽  
Mineral Matter ◽  
Infrared Study ◽  
Least Squares Curve Fitting ◽  
Mineral Mixtures ◽  
Good Agreement

The application of Fourier transform infrared spectroscopy to the quantitative determination of mineral matter in coal is discussed. The use of a least squares curve-fitting program allows a choice between standards to be made. The results of an analysis of mineral mixtures and a coal low temperature ash are presented. The results are in good agreement with known concentrations and those obtained by other methods of analysis.

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