- Chapter 1.1: Independence of Events
- Chapter 1.12: Bernoulli Trials
- Chapter 1.3: Sample Space
- Chapter 1.5: Algebra Of Events
- Chapter 1.8: Combinatorial Problems
- Chapter 1.9: Conditional Probability
- Chapter 10.2: Parameter Estimation
- Chapter 10.2.2 : Maximum-Likelihood Estimation
- Chapter 10.2.3.1 : Sampling from the Normal Distribution.
- Chapter 10.2.3.2: Sampling from the Exponential Distribution.
- Chapter 10.2.3.4: Sampling from the Bernoulli Distribution.
- Chapter 10.2.4.4: Estimation for a Semi-Markov Process.
- Chapter 10.2.5: Estimation with Dependent Samples
- Chapter 10.3.1: Tests on the Population Mean
- Chapter 10.3.2: Hypotheses Concerning Two Means
- Chapter 11.2: Least-Squares Curve Fitting
- Chapter 11.3: The Coefficients of Determination
- Chapter 11.4: Confidence Intervals In Linear Regression
- Chapter 11.6: Correlation Analysis
- Chapter 11.7: Simple Nonlinear Regression
- Chapter 11.8: HIGHER-DIMENSIONAL LEAST-SQUARES FIT
- Chapter 11.9: Analysiis And Variance
- Chapter 2: Random Variables and Their Event Spaces
- Chapter 2.5.8 : Constant Random Variable
- Chapter 2.6: Analysis of Program Mix
- Chapter 2.7: The Probability Generating Function
- Chapter 2.9: Independent Random Vaariables
- Chapter 3.2: The Exponential Contribution
- Chapter 184.108.40.206: The Exponential Contribution
- Chapter 3.4: Some Important Distributions
- Chapter 3.4.9: Defective Contribution
- Chapter 3.5: Functions of a Random Variables
- Chapter 3.6: Jointly Distributed Random Variables
- Chapter 3.7: Order Statistics
- Chapter 3.8: Distribution Of Sums
- Chapter 3.9: Functions Of Normal Random Variables
- Chapter 4: Moments
- Chapter 4.3: Expectation Based On Multiple Random Variables
- Chapter 4.5.14: The Normal Distribution
- Chapter 4.6: Computation Of Mean Time To Failure
- Chapter 4.7: Inequalities And Limit Theorems
- Chapter 5.1: Introduction
- Chapter 5.2: Mixture And Distributions
- Chapter 5.3: Conditional Expectation
- Chapter 5.4: Imperfect Fault Coverage And Reliability
- Chapter 5.5: Random Sums
- Chapter 6.1: Introduction
- Chapter 6.2: Clasification Of Stochastic Processes
- Chapter 6.3: The Bernoulli Process
- Chapter 6.4: The Poisson Process
- Chapter 6.6: Availability Analysis
- Chapter 6.7: Random Incidence
- Chapter 7.2: Computation Of n-Step Transition Probabilities
- Chapter 7.3: State Classification And Limiting Probabilitites
- Chapter 7.5: Markov Modulated Bernoulli Process
- Chapter 7.6: Irreducible Finite Chains With Aperiodic States
- Chapter 220.127.116.11 : The LRU Stack Model [SPIR 1977].
- Chapter 7.6.3: Slotted Aloha Model
- Chapter 7.7: The M/G/ 1 Queuing System
- Chapter 7.9: Finite Markov Chains With Absorbing States
- Chapter 8.1: Introduction
- Chapter 8.2: The Birth-Death Process
- Chapter 8.2.3: Finite State Space
- Chapter 18.104.22.168: Machine Repairman Mdoel
- Chapter 22.214.171.124 : Wireless Handoff Performance Model.
- Chapter 8.3.1: The Pure Birth Process
- Chapter 126.96.36.199: Death Process with a Linear Rate.
- Chapter 8.4.1: Availability Models
- Chapter 188.8.131.52 : The MMPP/M/1 Queue.
- Chapter 8.5: Markov Chains With Absorbing States
- Chapter 184.108.40.206: Successive Overrelaxation (SOR).
- Chapter 220.127.116.11 : Numerical Methods.
- Chapter 8.7.2 : Stochastic Petri Nets
- Chapter 8.7.4 : Stochastic Reward Nets
- Chapter 9.1: Intoduction
- Chapter 9.2: Open Queing Networks
- Chapter 9.3: Closed Queuing Networks
- Chapter 9.4: General Service Distribution And Mulitiple Job Types
- Chapter 9.5: Non-Product-Form Networks
- Chapter 9.6.2 : Response Time Distribution in Closed Networks
- Chapter 9.7: Summary
Probability and Statistics with Reliability, Queuing, and Computer Science Applications 2nd Edition - Solutions by Chapter
Full solutions for Probability and Statistics with Reliability, Queuing, and Computer Science Applications | 2nd Edition
Probability and Statistics with Reliability, Queuing, and Computer Science Applications | 2nd Edition - Solutions by ChapterGet Full Solutions
2 k p - factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each
All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions
A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.
The probability of an event given that the random experiment produces an outcome in another event.
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.
Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.
An expression sometimes used for nonlinear regression models or polynomial regression models.
Defects-per-unit control chart
See U chart
Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.
Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).
Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.
Estimate (or point estimate)
The numerical value of a point estimator.
Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.