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Solutions for Chapter 17: Probability Models

Stats: Modeling The World | 3rd Edition | ISBN: 9780131359581 | Authors: David E. Bock

Full solutions for Stats: Modeling The World | 3rd Edition

ISBN: 9780131359581

Stats: Modeling The World | 3rd Edition | ISBN: 9780131359581 | Authors: David E. Bock

Solutions for Chapter 17: Probability Models

Solutions for Chapter 17
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Textbook: Stats: Modeling The World
Edition: 3
Author: David E. Bock
ISBN: 9780131359581

This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Stats: Modeling The World , edition: 3. Chapter 17: Probability Models includes 42 full step-by-step solutions. Since 42 problems in chapter 17: Probability Models have been answered, more than 40969 students have viewed full step-by-step solutions from this chapter. Stats: Modeling The World was written by and is associated to the ISBN: 9780131359581.

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.

  • Biased estimator

    Unbiased estimator.

  • 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 limit theorem

    The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

  • Conditional probability density function

    The probability density function of the conditional probability distribution of a continuous random variable.

  • Conidence interval

    If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

  • Conidence level

    Another term for the conidence coeficient.

  • Continuity correction.

    A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

  • Continuous distribution

    A probability distribution for a continuous random variable.

  • 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 .

  • Degrees of freedom.

    The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

  • Dependent variable

    The response variable in regression or a designed experiment.

  • Discrete uniform random variable

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

  • Empirical model

    A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.

  • Fraction defective control chart

    See P chart

  • 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.

  • Frequency distribution

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

  • Gamma function

    A function used in the probability density function of a gamma random variable that can be considered to extend factorials

  • Gamma random variable

    A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

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