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Solutions for Chapter 8: Inference for Proportions

Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card | 8th Edition | ISBN: 9781464158933 | Authors: David S. Moore, George P. McCabe, Bruce A. Craig

Full solutions for Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card | 8th Edition

ISBN: 9781464158933

Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card | 8th Edition | ISBN: 9781464158933 | Authors: David S. Moore, George P. McCabe, Bruce A. Craig

Solutions for Chapter 8: Inference for Proportions

Solutions for Chapter 8
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Textbook: Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card
Edition: 8
Author: David S. Moore, George P. McCabe, Bruce A. Craig
ISBN: 9781464158933

Since 101 problems in chapter 8: Inference for Proportions have been answered, more than 31571 students have viewed full step-by-step solutions from this chapter. Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card was written by and is associated to the ISBN: 9781464158933. Chapter 8: Inference for Proportions includes 101 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: Introduction to the Practice of Statistics: w/CrunchIt/EESEE Access Card, edition: 8.

Key Statistics Terms and definitions covered in this textbook
  • Analytic study

    A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

  • 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.

  • 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.

  • Bias

    An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

  • Box plot (or box and whisker plot)

    A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

  • 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.

  • Components of variance

    The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

  • Conditional probability density function

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

  • Continuity correction.

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

  • Continuous uniform random variable

    A continuous random variable with range of a inite interval and a constant probability density function.

  • 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 .

  • Defect

    Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

  • Design matrix

    A matrix that provides the tests that are to be conducted in an experiment.

  • Discrete uniform random variable

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

  • Distribution free method(s)

    Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

  • Empirical model

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

  • Fisher’s least signiicant difference (LSD) method

    A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.

  • Gaussian distribution

    Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

  • Generating function

    A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function

  • 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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