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Solutions for Chapter 10.2.3.2: Sampling from the Exponential Distribution.

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 10.2.3.2: Sampling from the Exponential Distribution.

Solutions for Chapter 10.2.3.2
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This expansive textbook survival guide covers the following chapters and their solutions. Since 3 problems in chapter 10.2.3.2: Sampling from the Exponential Distribution. have been answered, more than 3410 students have viewed full step-by-step solutions from this chapter. Probability and Statistics with Reliability, Queuing, and Computer Science Applications was written by 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. Chapter 10.2.3.2: Sampling from the Exponential Distribution. includes 3 full step-by-step solutions.

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

  • Attribute

    A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

  • Average

    See Arithmetic mean.

  • Backward elimination

    A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

  • Bimodal distribution.

    A distribution with two modes

  • Binomial random variable

    A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

  • Categorical data

    Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

  • 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 tendency

    The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

  • 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.

  • Combination.

    A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

  • Completely randomized design (or experiment)

    A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

  • Consistent estimator

    An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

  • 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.

  • Crossed factors

    Another name for factors that are arranged in a factorial experiment.

  • 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.

  • Defects-per-unit control chart

    See U chart

  • Discrete random variable

    A random variable with a inite (or countably ininite) range.

  • Discrete uniform random variable

    A discrete random variable with a inite range and constant probability mass function.

  • Error propagation

    An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

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