| Titre : | A modern introduction to probability and statistics : understanding why and how | | Type de document : | texte imprimé | | Auteurs : | F. M. Dekking, Auteur ; C. Kraaikamp, Auteur ; H. P. Lopuhaa, Auteur | | Editeur : | New York : Springer-Verlag | | Année de publication : | 2005 | | Collection : | Springer Texts in Statistics | | Importance : | 487 p. | | Présentation : | couv. ill. en coul. | | Format : | 24 cm. | | ISBN/ISSN/EAN : | 978-1-85233-896-2 | | Langues : | Anglais (eng) | | Catégories : | MATHEMATIQUES
| | Index. décimale : | 04-05 Probabilité et statistique | | Résumé : | Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study.
A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap.
This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects. | | Note de contenu : | Contents
1 Why Probability and Statistics?
2 Outcomes, Events and Probability
3 Conditional Probability and Independence
4 Discrete Random Variables
5 Continuous Random Variables
6 Simulation
7 Expectation and Variance
8 Computations with Random Variables
9 Joint Distributions and Independence
10 Covariance and Correlation
11 More Computations with More Random Variables
12 The Poisson Process
13 The Law of Large Numbers
14 The Central Limit Theorem
15 Exploratory Data Analysis: Graphical Summaries
16 Exploratory Data Analysis: Numerical Summaries
17 Basic Statistical Models
18 The Bootstrap
19 Unbiased Estimators
20 Efficiency and Mean Squared Error
21 Maximum Likelihood
22 The Method of Least Squares
23 Confidence Intervals for the Mean
24 More on Confidence Intervals
25 Testing Hypotheses: Essentials
26 Testing Hypotheses: Elaboration
27 The t-test
28 Comparing Two Samples
A: Summary of distributions
B: Tables of the normal and t-distributions
C: Answers to selected exercices
D: Full solutions to selected exercises
-Index |
A modern introduction to probability and statistics : understanding why and how [texte imprimé] / F. M. Dekking, Auteur ; C. Kraaikamp, Auteur ; H. P. Lopuhaa, Auteur . - New York : Springer-Verlag, 2005 . - 487 p. : couv. ill. en coul. ; 24 cm.. - ( Springer Texts in Statistics) . ISSN : 978-1-85233-896-2 Langues : Anglais ( eng) | Catégories : | MATHEMATIQUES
| | Index. décimale : | 04-05 Probabilité et statistique | | Résumé : | Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study.
A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap.
This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects. | | Note de contenu : | Contents
1 Why Probability and Statistics?
2 Outcomes, Events and Probability
3 Conditional Probability and Independence
4 Discrete Random Variables
5 Continuous Random Variables
6 Simulation
7 Expectation and Variance
8 Computations with Random Variables
9 Joint Distributions and Independence
10 Covariance and Correlation
11 More Computations with More Random Variables
12 The Poisson Process
13 The Law of Large Numbers
14 The Central Limit Theorem
15 Exploratory Data Analysis: Graphical Summaries
16 Exploratory Data Analysis: Numerical Summaries
17 Basic Statistical Models
18 The Bootstrap
19 Unbiased Estimators
20 Efficiency and Mean Squared Error
21 Maximum Likelihood
22 The Method of Least Squares
23 Confidence Intervals for the Mean
24 More on Confidence Intervals
25 Testing Hypotheses: Essentials
26 Testing Hypotheses: Elaboration
27 The t-test
28 Comparing Two Samples
A: Summary of distributions
B: Tables of the normal and t-distributions
C: Answers to selected exercices
D: Full solutions to selected exercises
-Index |
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