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Solutions for Chapter 6.2: Clasification Of Stochastic Processes

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 6.2: Clasification Of Stochastic Processes

Chapter 6.2: Clasification Of Stochastic Processes includes 4 full step-by-step solutions. Probability and Statistics with Reliability, Queuing, and Computer Science Applications was written by and is associated to the ISBN: 9781119285427. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Probability and Statistics with Reliability, Queuing, and Computer Science Applications , edition: 2. Since 4 problems in chapter 6.2: Clasification Of Stochastic Processes have been answered, more than 2650 students have viewed full step-by-step solutions from this chapter.

Key Statistics Terms and definitions covered in this textbook
  • 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

  • Arithmetic mean

    The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

  • Attribute control chart

    Any control chart for a discrete random variable. See Variables control chart.

  • Bayes’ estimator

    An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.

  • Bernoulli trials

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

  • Biased estimator

    Unbiased estimator.

  • Conditional probability

    The probability of an event given that the random experiment produces an outcome in another event.

  • Conidence coeficient

    The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

  • Control limits

    See Control chart.

  • 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.

  • Correction factor

    A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

  • Correlation matrix

    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 .

  • Cumulative normal distribution function

    The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.

  • Decision interval

    A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.

  • Deming

    W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

  • Erlang random variable

    A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

  • Estimator (or point estimator)

    A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

  • Factorial experiment

    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.

  • 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.

  • Frequency distribution

    An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

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