Introduction to probability and statistics for engineers and scientists /

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ross, Sheldon M.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Elsevier/Academic Press, ©2004.
Edición:3rd ed.
Temas:
Probabilities.
Mathematical statistics.
Engineering > Statistical methods.
Probability
Probabilités.
Statistique mathématique.
Ingénierie > Méthodes statistiques.
probability.
MATHEMATICS > Probability & Statistics > General.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Contents
  • Preface
  • CHAPTER 1 INTRODUCTION TO STATISTICS
  • 1.1 INTRODUCTION
  • 1.2 DATA COLLECTION AND DESCRIPTIVE STATISTICS
  • 1.3 INFERENTIAL STATISTICS AND PROBABILITY MODELS
  • 1.4 POPULATIONS AND SAMPLES
  • 1.5 A BRIEF HISTORY OF STATISTICS
  • CHAPTER 2 DESCRIPTIVE STATISTICS
  • 2.1 INTRODUCTION
  • 2.2 DESCRIBING DATA SETS
  • 2.2.1 Frequency Tables and Graphs
  • 2.2.2 Relative Frequency Tables and Graphs
  • 2.2.3 Grouped Data, Histograms, Ogives, and Stem and Leaf Plots
  • 2.3 SUMMARIZING DATA SETS
  • 2.3.1 Sample Mean, Sample Median, and Sample Mode
  • 2.3.2 Sample Variance and Sample Standard Deviation
  • 2.3.3 Sample Percentiles and Box Plots
  • 2.4 CHEBYSHEV'S INEQUALITY
  • 2.5 NORMAL DATA SETS
  • 2.6 PAIRED DATA SETS AND THE SAMPLE CORRELATION COEFFICIENT
  • CHAPTER 3 ELEMENTS OF PROBABILITY
  • 3.1 INTRODUCTION
  • 3.2 SAMPLE SPACE AND EVENTS
  • 3.3 VENN DIAGRAMS AND THE ALGEBRA OF EVENTS
  • 3.4 AXIOMS OF PROBABILITY
  • 3.5 SAMPLE SPACES HAVING EQUALLY LIKELY OUTCOMES
  • 3.6 CONDITIONAL PROBABILITY
  • 3.7 BAYES' FORMULA
  • 3.8 INDEPENDENT EVENTS
  • CHAPTER 4 RANDOM VARIABLES AND EXPECTATION
  • 4.1 RANDOM VARIABLES
  • 4.2 TYPES OF RANDOM VARIABLES
  • 4.3 JOINTLY DISTRIBUTED RANDOM VARIABLES
  • 4.3.1 Independent Random Variables
  • *4.3.2 Conditional Distributions
  • 4.4 EXPECTATION
  • 4.5 PROPERTIES OF THE EXPECTED VALUE
  • 4.5.1 Expected Value of Sums of Random Variables
  • 4.6 VARIANCE
  • 4.7 COVARIANCE AND VARIANCE OF SUMS OF RANDOM VARIABLES
  • 4.8 MOMENT GENERATING FUNCTIONS
  • 4.9 CHEBYSHEV'S INEQUALITY AND THE WEAK LAW OF LARGE NUMBERS
  • CHAPTER 5 SPECIAL RANDOM VARIABLES
  • 5.1 THE BERNOULLI AND BINOMIAL RANDOM VARIABLES
  • 5.1.1 Computing the Binomial Distribution Function
  • 5.2 THE POISSON RANDOM VARIABLE
  • 5.2.1 Computing the Poisson Distribution Function
  • 5.3 THE HYPERGEOMETRIC RANDOM VARIABLE
  • 5.4 THE UNIFORM RANDOM VARIABLE
  • 5.5 NORMAL RANDOM VARIABLES
  • 5.6 EXPONENTIAL RANDOM VARIABLES
  • *5.6.1 The Poisson Process
  • *5.7 THE GAMMA DISTRIBUTION
  • 5.8 DISTRIBUTIONS ARISING FROM THE NORMAL
  • 5.8.1 The Chi-Square Distribution
  • 5.8.2 The t-Distribution
  • 5.8.3 The F-Distribution
  • *5.9 THE LOGISTICS DISTRIBUTION
  • CHAPTER 6 DISTRIBUTIONS OF SAMPLING STATISTICS
  • 6.1 INTRODUCTION
  • 6.2 THE SAMPLE MEAN
  • 6.3 THE CENTRAL LIMIT THEOREM
  • 6.3.1 Approximate Distribution of the Sample Mean
  • 6.3.2 How Large a Sample Is Needed?
  • 6.4 THE SAMPLE VARIANCE
  • 6.5 SAMPLING DISTRIBUTIONS FROM A NORMAL POPULATION
  • 6.5.1 Distribution of the Sample Mean
  • 6.5.2 Joint Distribution of X and S2
  • 6.6 SAMPLING FROM A FINITE POPULATION
  • CHAPTER 7 PARAMETER ESTIMATION
  • 7.1 INTRODUCTION
  • 7.2 MAXIMUM LIKELIHOOD ESTIMATORS
  • *7.2.1 Estimating Life Distributions
  • 7.3 INTERVAL ESTIMATES
  • 7.3.1 Confidence Interval for a Normal Mean When the Variance is Unknown
  • 7.3.2 Confidence Intervals for the Variance of a Normal Distribution
  • 7.4 ESTIMATING THE.

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