Probability and Statistics

By:

DeGroot, Morris H

Contributor(s):

Schervish, Mark J

Material type: Text

TextPublication details: Pearson India Education Services Pvt. Ltd. New Delhi 2021Edition: 4thDescription: 829 pISBN:

9789332573871

Subject(s):

DDC classification:

519.2 DEG

Summary: The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Probability & Statistics, Fourth Edition, was written for a one- or two-semester probability and statistics course. This course is offered primarily at four-year institutions and taken mostly by sophomore and junior level students majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.

List(s) this item appears in: Operation & quantitative Techniques | HR & OB

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.2 DEG (Browse shelf(Opens below))	1	Available		000994
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.2 DEG (Browse shelf(Opens below))	2	Available		000995

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Previous	519.1 CHE Introduction to stochastic processes	519.2 AMI Thinking probabilistically:	519.2 BIS Probability and statistics	519.2 DEG Probability and Statistics	519.2 DEG Probability and Statistics	519.2 HOG Probability and statistical inference	519.2 KUL Introduction to modeling and analysis of stochastic systems	Next

Table of Content
1. Introduction to Probability

2. Conditional Probability

3. Random Variables and Distributions

4. Expectation

5. Special Distributions

6. Large Random Samples

7. Estimation

8. Sampling Distributions of Estimators

9. Testing Hypotheses

10. Categorical Data and Nonparametric Methods

11. Linear Statistical Models

The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data.

Probability & Statistics, Fourth Edition, was written for a one- or two-semester probability and statistics course. This course is offered primarily at four-year institutions and taken mostly by sophomore and junior level students majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.

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