000 03486cam a2200529 i 4500
001 9781003025764
003 FlBoTFG
005 20220531132536.0
006 m d | |
007 cr |||||||||||
008 200505s2020 flua ob 001 0 eng
040 _aOCoLC-P
_beng
_erda
_cOCoLC-P
020 _a9781000069631
_qelectronic book
020 _a100006963X
_qelectronic book
020 _a9781003025764
_qelectronic book
020 _a1003025765
_qelectronic book
020 _z9780367458522
_qhardcover
020 _a9781000069525
_q(electronic bk. : Mobipocket)
020 _a1000069524
_q(electronic bk. : Mobipocket)
020 _a9781000069419
_q(electronic bk. : PDF)
020 _a1000069419
_q(electronic bk. : PDF)
020 _z9780367493516
035 _a(OCoLC)1154086293
_z(OCoLC)1162816339
035 _a(OCoLC-P)1154086293
050 0 4 _aQA278.2
_b.W475 2020eb
072 7 _aBUS
_x061000
_2bisacsh
072 7 _aMAT
_x029000
_2bisacsh
072 7 _aPBT
_2bicssc
082 0 0 _a519.5/36
_223
100 1 _aWestfall, Peter H.,
_d1957-
_eauthor.
245 1 0 _aUnderstanding regression analysis :
_ba conditional distribution approach /
_cPeter H. Westfall, Andrea L. Arias.
264 1 _aBoca Raton, FL :
_bCRC Press, Taylor & Francis Group,
_c[2020]
300 _a1 online resource (xv, 496 pages) :
_billustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _a"A Chapman & Hall Book" -- taken from title page.
520 _a"This book unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks and decision trees under a common umbrella; namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, also explains why the assumptions of the classical regression model are wrong. This one takes a realistic approach from the outset that all models are just approximations. The emphasis is to model Nature's processes realistically, rather than to assume that Nature works in particular, constrained ways"--
_cProvided by publisher.
505 0 _a1. Introduction to Regression Models 2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary,Nominal, and OrdinalResponse Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression 18. Regression Trees19. Bookend
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aRegression analysis.
650 7 _aBUSINESS & ECONOMICS / Statistics
_2bisacsh
650 7 _aMATHEMATICS / Probability & Statistics / General
_2bisacsh
700 1 _aArias, Andrea L.,
_eauthor.
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003025764
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c73759
_d73759