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Solutions for Chapter 9.4: General Service Distribution And Mulitiple Job Types

Probability and Statistics with Reliability, Queuing, and Computer Science Applications | 2nd Edition | ISBN: 9781119285427 | Authors: Kishor S. Trivedi

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 | ISBN: 9781119285427 | Authors: Kishor S. Trivedi

Solutions for Chapter 9.4: General Service Distribution And Mulitiple Job Types

Solutions for Chapter 9.4
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Chapter 9.4: General Service Distribution And Mulitiple Job Types includes 2 full step-by-step solutions. Since 2 problems in chapter 9.4: General Service Distribution And Mulitiple Job Types have been answered, more than 1026 students have viewed full step-by-step solutions from this chapter. Probability and Statistics with Reliability, Queuing, and Computer Science Applications was written by Patricia and is associated to the ISBN: 9781119285427. This textbook survival guide was created for the textbook: Probability and Statistics with Reliability, Queuing, and Computer Science Applications , edition: 2. This expansive textbook survival guide covers the following chapters and their solutions.

Key Statistics Terms and definitions covered in this textbook
  • 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.

  • Axioms of probability

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

  • 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

  • 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.

  • Conidence level

    Another term for the conidence coeficient.

  • Contour plot

    A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

  • Convolution

    A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

  • 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 .

  • Critical region

    In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

  • Critical value(s)

    The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

  • Deming’s 14 points.

    A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

  • Distribution function

    Another name for a cumulative distribution function.

  • Exponential random variable

    A series of tests in which changes are made to the system under study

  • Fixed factor (or fixed effect).

    In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

  • 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 .

  • Harmonic mean

    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 .

  • Hat matrix.

    In multiple regression, the matrix H XXX X = ( ) ? ? -1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .

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