000			02742cam a22003258i 4500
001			21658586
005			20240926111541.0
006			m |o d |
007			cr |||||||||||
008			200730s2020 nju ob 001 0 eng c
010			_a 2020034056
020			_z9781119578727
040			_cTBS
041			_aeng
050	0	0	_aQA278.2 _b.M65 2021
100			_aMontgomery, Douglas C. _eauthor _914101
245	1	0	_aIntroduction to linear regression analysis _c/ Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining.
250			_aSixth edition.
260			_aHoboken, NJ : John Wiley & Sons, 2021.
300			_axvi, 673 pages : illustrations, tables, charts (black and white) ; 27 cm.
490	0		_a Wiley series in probability and statistics.
504			_aIncludes bibliographical references and index.
505			_a1. Introduction — 2. Simple linear regression — 3. Multiple linear regression — 4. Model adequacy checking — 5. Transformations and weighting to correct model inadequacies — 6. Diagnostics for leverage and influence — 7. Polynomial regression models — 8. Indicator variables — 9. Multicollinearity — 10. Variable selection and model building — 11. Validation of regression models — 12. Introduction to nonlinear regression — 13. Generalized linear models — 14. Regression analysis of time series data — 15. Other topics in the use of regression analysis — Appendix — Index.
520			_aIntroduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book. The new edition focuses on four key areas of improvement over the fifth edition: New exercises and data sets ; New material on generalized regression techniques ; The inclusion of JMP software in key areas Carefully condensing the text where possible. Introduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today’s cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
526			_aB3 Functional Competences: Data Analytics
650		0	_aRegression analysis _95701
700			_aPeck, Elizabeth A. _d1953- _eauthor _923473
700			_aVining, G. Geoffrey _d1954- _eauthor _923474
942			_2lcc
999			_c3261 _d3261