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Probability and statistics for data science : math + R + data / Norman Matloff.

By: Material type: TextTextLanguage: English Publication details: Boca Raton : CRC Press, Taylor & Francis Group, c2020.Description: xxxii, 412 pages ; illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781138393295 (paperback)
Subject(s): LOC classification:
  • QA273 M42 2020
Online resources:
Contents:
About the author -- To the instructor -- To the reader -- I: Fundamentals of probability -- Basic probability models -- Monte Carlo simulation -- Discrete random variables: expected value -- Discrete random variable: variance -- Discrete parametric distribution families -- Continuous probability models -- II: Fundamentals of statistics -- Statistics prologue -- Fitting continuous models -- The Family of normal distributions -- Introduction to statistical inference -- III: Multivariate analysis -- Multivariate distributions -- The Multivariate normal family of distributions -- Mixture distributions -- Multivariate description and dimension reduction -- Predictive modeling -- Model parsimony and overfitting -- Introduction to discrete time Markov Chains -- IV: Appendices -- A R quick start -- Matrix algebra -- Bibliography -- Index.
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Holdings
Item type Current library Collection Call number Materials specified URL Status Notes Date due Barcode
Books Books Ladislao N. Diwa Memorial Library Reserve Section Non-fiction RUS QA273 M42 2020 (Browse shelf(Opens below)) Link to resource Room use only 79474 00082686

Includes bibliographical references and index.

About the author -- To the instructor -- To the reader -- I: Fundamentals of probability -- Basic probability models -- Monte Carlo simulation -- Discrete random variables: expected value -- Discrete random variable: variance -- Discrete parametric distribution families -- Continuous probability models -- II: Fundamentals of statistics -- Statistics prologue -- Fitting continuous models -- The Family of normal distributions -- Introduction to statistical inference -- III: Multivariate analysis -- Multivariate distributions -- The Multivariate normal family of distributions -- Mixture distributions -- Multivariate description and dimension reduction -- Predictive modeling -- Model parsimony and overfitting -- Introduction to discrete time Markov Chains -- IV: Appendices -- A R quick start -- Matrix algebra -- Bibliography -- Index.

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