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Regression Analysis

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Produktnummer: 184b2a2defb7d24598b8758e68ba9a38ab
Autor: Sen, Ashish Srivastava, Muni
Themengebiete: Bootstrapping Estimator Measure Normal distribution Random variable Regression analysis best fit
Veröffentlichungsdatum: 13.07.1990
EAN: 9780387972114
Sprache: Englisch
Seitenzahl: 348
Produktart: Gebunden
Verlag: Springer US
Untertitel: Theory, Methods, and Applications
Produktinformationen "Regression Analysis"
Any method of fitting equations to data may be called regression. Such equations are valuable for at least two purposes: making predictions and judging the strength of relationships. Because they provide a way of em pirically identifying how a variable is affected by other variables, regression methods have become essential in a wide range of fields, including the social sciences, engineering, medical research and business. Of the various methods of performing regression, least squares is the most widely used. In fact, linear least squares regression is by far the most widely used of any statistical technique. Although nonlinear least squares is covered in an appendix, this book is mainly about linear least squares applied to fit a single equation (as opposed to a system of equations). The writing of this book started in 1982. Since then, various drafts have been used at the University of Toronto for teaching a semester-long course to juniors, seniors and graduate students in a number of fields, including statistics, pharmacology, engineering, economics, forestry and the behav ioral sciences. Parts of the book have also been used in a quarter-long course given to Master's and Ph.D. students in public administration, urban plan ning and engineering at the University of Illinois at Chicago (UIC). This experience and the comments and criticisms from students helped forge the final version.
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