 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 : MaximumLikelihood 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 SemiMarkov 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: LeastSquares 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: HIGHERDIMENSIONAL LEASTSQUARES 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 3.2.3.3: 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 nStep 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 7.6.2.3 : 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 BirthDeath Process
 Chapter 8.2.3: Finite State Space
 Chapter 8.2.3.1: Machine Repairman Mdoel
 Chapter 8.2.3.2 : Wireless Handoff Performance Model.
 Chapter 8.3.1: The Pure Birth Process
 Chapter 8.3.2.2: Death Process with a Linear Rate.
 Chapter 8.4.1: Availability Models
 Chapter 8.4.2.3 : The MMPP/M/1 Queue.
 Chapter 8.5: Markov Chains With Absorbing States
 Chapter 8.6.1.2: Successive Overrelaxation (SOR).
 Chapter 8.6.2.2 : 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: NonProductForm 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
ISBN: 9781119285427
Probability and Statistics with Reliability, Queuing, and Computer Science Applications  2nd Edition  Solutions by Chapter
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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

Attribute
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.

Backward elimination
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

Bias
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

Causal variable
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

Center line
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

Conditional probability
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.

Continuity correction.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

Contrast
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.

Covariance matrix
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 offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance 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.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Defectsperunit 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.