- 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
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
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.
The joint probability distribution of two random variables.
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 portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.
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.
A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.
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 square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .
Discrete random variable
A random variable with a inite (or countably ininite) range.
Another name for a cumulative distribution function.
The variance of an error term or component in a model.
A subset of a sample space.
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.
Fisher’s least signiicant difference (LSD) method
A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.
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.
Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .
Having trouble accessing your account? Let us help you, contact support at +1(510) 944-1054 or firstname.lastname@example.org
Forgot password? Reset it here