Introductory statistics with R / Peter Dalgaard.

By: Dalgaard, Peter
Material type: TextTextSeries: Statistics and computing: Publisher: New York : Springer, c2008Edition: 2nd edDescription: xvi, 363 p. : ill. ; 24 cmISBN: 9780387790534 (pbk. : alk. paper); 0387790535 (pbk. : alk. paper); 9780387790541; 0387790543Subject(s): Statistics -- Data processing | R (Computer program language)DDC classification: 519.5 LOC classification: QA276.45.R3 | D35 2008
Contents:
Basics -- The R environment -- Probability and distributions -- Descriptive statistics and graphics -- One- and two-sample tests -- Regression and correlation -- Analysis of variance and the Kruskal-Wallis test -- Tabular data -- Power and the computation of sample size -- Advanced data handling -- Multiple regression -- Linear models -- Logistic regression -- Survival analysis -- Rates and Poisson regression -- Nonlinear curve fitting.
List(s) this item appears in: Statistics
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Item type Current location Collection Call number Status Date due Barcode Item holds
Reference Reference Lahore Business School Library
Shelf No. 34, LBS
Not for loan and check out is restricted 519.5 DAL-I 2008 805303 (Browse shelf) Not For Loan 805303
Total holds: 0
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519.5 CAR-A 1997 802158 Applied statistical methods : 519.5 CAS-S 2002 805414 Statistical inference / 519.5 CHA-S 1998 802159 Statistics for business and economics / 519.5 DAL-I 2008 805303 Introductory statistics with R / 519.5 DAN-A 1990 800203 Applied nonparametric statistics / 519.5 DOA-A 2007 802859 Applied statistics in business and economics / 519.5 DOA-A 2007 802860 Applied statistics in business and economics /

Includes bibliographical references (p. [355]-356) and index.

Basics -- The R environment -- Probability and distributions -- Descriptive statistics and graphics -- One- and two-sample tests -- Regression and correlation -- Analysis of variance and the Kruskal-Wallis test -- Tabular data -- Power and the computation of sample size -- Advanced data handling -- Multiple regression -- Linear models -- Logistic regression -- Survival analysis -- Rates and Poisson regression -- Nonlinear curve fitting.

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