机读格式显示(MARC)
- 000 02038cam a2200373 i 4500
- 008 170313s2017 flua b 001 0 eng
- 020 __ |a 9781138066465 : |c CNY1013.05
- 035 __ |a (OCoLC)975370313
- 040 __ |a DLC |b eng |e rda |c DLC |d BTCTA |d YDX |d BDX |d OCLCO |d OCLCQ |d OCLCF |d YDX |d CHVBK |d OCLCO |d FIE
- 050 00 |a QA278.2 |b .M377 2017
- 100 1_ |a Matloff, Norman S., |e author.
- 245 10 |a Statistical regression and classification : |b from linear models to machine learning / |c Norman Matloff.
- 260 __ |a Boca Raton, FL : |b CRC Press, Taylor & Francis Group, |c c2017
- 300 __ |a xxxviii, 489 pages ; |c 25 cm.
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 490 1_ |a Chapman & Hall/CRC: Texts in Statistical Science Series
- 504 __ |a Includes bibliographical references and index.
- 520 __ |a The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. -- |c Provided by publisher.
- 650 _0 |a Regression analysis.
- 650 _0 |a Vector analysis.
- 830 _0 |a Texts in statistical science.