- 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 22.214.171.124: 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 126.96.36.199 : 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 188.8.131.52: Machine Repairman Mdoel
- Chapter 184.108.40.206 : Wireless Handoff Performance Model.
- Chapter 8.3.1: The Pure Birth Process
- Chapter 220.127.116.11: Death Process with a Linear Rate.
- Chapter 8.4.1: Availability Models
- Chapter 18.104.22.168 : The MMPP/M/1 Queue.
- Chapter 8.5: Markov Chains With Absorbing States
- Chapter 22.214.171.124: Successive Overrelaxation (SOR).
- Chapter 126.96.36.199 : 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
`-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).
a-error (or a-risk)
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 chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.
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
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.
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
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.
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.
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 .
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment
The variance of an error term or component in a model.
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.
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.
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 .