- 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 126.96.36.199: 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 188.8.131.52 : 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 184.108.40.206: Machine Repairman Mdoel
- Chapter 220.127.116.11 : Wireless Handoff Performance Model.
- Chapter 8.3.1: The Pure Birth Process
- Chapter 18.104.22.168: Death Process with a Linear Rate.
- Chapter 8.4.1: Availability Models
- Chapter 22.214.171.124 : The MMPP/M/1 Queue.
- Chapter 8.5: Markov Chains With Absorbing States
- Chapter 126.96.36.199: Successive Overrelaxation (SOR).
- Chapter 188.8.131.52 : 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
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study
Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.
Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.
Bivariate normal distribution
The joint distribution of two normal random variables
Central composite design (CCD)
A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a second-order model.
Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.
Chi-square (or chi-squared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.
Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.
The variance of the conditional probability distribution of a random variable.
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.
Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment
The amount of variability exhibited by data
A study in which a sample from a population is used to make inference to the population. See Analytic study
A subset of a sample space.
The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.
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