000 | 03486cam a2200529 i 4500 | ||
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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 |
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020 |
_a9781000069631 _qelectronic book |
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020 |
_a100006963X _qelectronic book |
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020 |
_a9781003025764 _qelectronic book |
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020 |
_a1003025765 _qelectronic book |
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020 |
_z9780367458522 _qhardcover |
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020 |
_a9781000069525 _q(electronic bk. : Mobipocket) |
||
020 |
_a1000069524 _q(electronic bk. : Mobipocket) |
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020 |
_a9781000069419 _q(electronic bk. : PDF) |
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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 |
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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 |