 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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`error (or `risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).

aerror (or arisk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chisquare with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chisquare random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chisquare random variables.

Alternative hypothesis
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

Analysis of variance (ANOVA)
A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

Axioms of probability
A set of rules that probabilities deined on a sample space must follow. See Probability

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

Consistent estimator
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

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

Continuous random variable.
A random variable with an interval (either inite or ininite) of real numbers for its range.

Correlation
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

Covariance
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Error variance
The variance of an error term or component in a model.

False alarm
A signal from a control chart when no assignable causes are present

Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

Generator
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .