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LEADER 00000cam a2200565Ma 4500
003 OCoLC
005 20240129213017.0
006 m o d
007 cr cn |||
008 040821s2020 xx go 0o0 0 eng d
010 2020028117
019 1256670430
020 9781119615866
020 1119615860
020 9781119615903|q(e-book)
020 1119615909
024 8 9781119615866
035 (OCoLC)1266268576|z(OCoLC)1256670430
040 TOH|beng|cTOH|dOCLCO|dUKAHL|dVT2|dOCLCF|dOCLCO|dOCLCQ
|dOCLCO
049 INap
082 00 519.5/36
082 00 519.5/36|223
099 eBook O'Reilly for Public Libraries
100 1 Pardoe, Iain,|eauthor.
245 10 Applied Regression Modeling, 3rd Edition /|cPardoe, Iain.
|h[O'Reilly electronic resource]
250 3rd edition.
264 1 |bWiley,|c2020.
300 1 online resource (336 pages)
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file
365 |b128.95
520 Master the fundamentals of regression without learning
calculus with this one-stop resource The newly and
thoroughly revised 3rd Edition of Applied Regression
Modeling delivers a concise but comprehensive treatment of
the application of statistical regression analysis for
those with little or no background in calculus.
Accomplished instructor and author Dr. Iain Pardoe has
reworked many of the more challenging topics, included
learning outcomes and additional end-of-chapter exercises,
and added coverage of several brand-new topics including
multiple linear regression using matrices. The methods
described in the text are clearly illustrated with multi-
format datasets available on the book's supplementary
website. In addition to a fulsome explanation of
foundational regression techniques, the book introduces
modeling extensions that illustrate advanced regression
strategies, including model building, logistic regression,
Poisson regression, discrete choice models, multilevel
models, Bayesian modeling, and time series forecasting.
Illustrations, graphs, and computer software output appear
throughout the book to assist readers in understanding and
retaining the more complex content. Applied Regression
Modeling covers a wide variety of topics, like: Simple
linear regression models, including the least squares
criterion, how to evaluate model fit, and estimation/
prediction Multiple linear regression, including testing
regression parameters, checking model assumptions
graphically, and testing model assumptions numerically
Regression model building, including predictor and
response variable transformations, qualitative predictors,
and regression pitfalls Three fully described case studies,
including one each on home prices, vehicle fuel efficiency,
and pharmaceutical patches Perfect for students of any
undergraduate statistics course in which regression
analysis is a main focus, Applied Regression Modeling also
belongs on the bookshelves of non-statistics graduate
students, including MBAs, and for students of vocational,
professional, and applied courses like data science and
machine learning.
542 |fCopyright © 2021 by John Wiley & Sons|g2021
550 Made available through: Safari, an O'Reilly Media Company.
588 0 Online resource; Title from title page (viewed December 3,
2020).
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Regression analysis.
650 0 Statistics.
650 2 Regression Analysis
650 6 Analyse de régression.
650 6 Statistique.
650 7 statistics.|2aat
650 7 Regression analysis|2fast
650 7 Statistics|2fast
710 2 O'Reilly for Higher Education (Firm),|edistributor.
710 2 Safari, an O'Reilly Media Company.
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781119615866/?ar
|zAvailable at O'Reilly for Public Libraries
938 Askews and Holts Library Services|bASKH|nAH37737097
994 92|bJFN