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An introduction to mathematical statistics and its applications /Richard J Larsen

By: Richard J Larsen ; Morris L Marx.
Contributor(s): Morris L Marx.
Material type: TextTextPublisher: Boston : Pearson ,2018Edition: Sixth edition.Description: x, 742 pages ; 26 cm.ISBN: 9780134114217.Subject(s): MathematicsGenre/Form: ;Mathematical statisticsDDC classification: 519.5 LAR
Contents:
Probability -- Random variables -- Special distributions -- Estimation -- Hypothesis testing -- Inferences based on the normal distribution -- Types of data: a brief overview -- Two-sample inferences -- Goodness-of-fit tests -- Regression -- The analysis of variance -- Randomized block designs -- Nonparametric statistics -- Factorial data
Summary: Introduction to Mathematical Statistics and Its Applications , 6th Edition is a high-level calculus student’s first exposure to mathematical statistics. This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics and experimental design, which teaches students various ways to analyze data. It gives computational-minded students a necessary and realistic exposure to identifying data models.
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Item type Current location Call number Status Notes Date due Barcode Item holds
Books Books SNU LIBRARY
519.5 LAR (Browse shelf) Not For Loan Costly 27364
Total holds: 0

Probability --
Random variables --
Special distributions --
Estimation --
Hypothesis testing --
Inferences based on the normal distribution --
Types of data: a brief overview --
Two-sample inferences --
Goodness-of-fit tests --
Regression --
The analysis of variance --
Randomized block designs --
Nonparametric statistics --
Factorial data


Introduction to Mathematical Statistics and Its Applications , 6th Edition is a high-level calculus student’s first exposure to mathematical statistics. This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics and experimental design, which teaches students various ways to analyze data. It gives computational-minded students a necessary and realistic exposure to identifying data models.

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