Solutions for Chapter 9: First Course in Probability 8th Edition

First Course in Probability | 8th Edition | ISBN: 9780136033134 | Authors: Norman S. Nise

Full solutions for First Course in Probability | 8th Edition

ISBN: 9780136033134

First Course in Probability | 8th Edition | ISBN: 9780136033134 | Authors: Norman S. Nise

Solutions for Chapter 9

Since 11 problems in chapter 9 have been answered, more than 2845 students have viewed full step-by-step solutions from this chapter. First Course in Probability was written by Sieva Kozinsky and is associated to the ISBN: 9780136033134. Chapter 9 includes 11 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: First Course in Probability, edition: 8.

Key Statistics Terms and definitions covered in this textbook
  • Average run length, or ARL

    The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.

  • Axioms of probability

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

  • 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.

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • Chance cause

    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.

  • Coeficient of determination

    See R 2 .

  • Comparative experiment

    An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

  • Conditional probability

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

  • Continuous random variable.

    A random variable with an interval (either inite or ininite) of real numbers for its range.

  • 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.

  • Curvilinear regression

    An expression sometimes used for nonlinear regression models or polynomial regression models.

  • 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.

  • Discrete uniform random variable

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

  • Dispersion

    The amount of variability exhibited by data

  • Error of estimation

    The difference between an estimated value and the true value.

  • Event

    A subset of a sample space.

  • First-order model

    A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model

  • 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.

  • 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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