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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 73223 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
  • a-error (or a-risk)

    In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

  • Additivity property of x 2

    If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • Chi-square test

    Any test of signiicance based on the chi-square distribution. The most common chi-square tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

  • Conditional probability

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

  • Consistent estimator

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

  • Continuity correction.

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

  • Counting techniques

    Formulas used to determine the number of elements in sample spaces and events.

  • 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.

  • 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.

  • Eficiency

    A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

  • Error mean square

    The error sum of squares divided by its number of degrees of freedom.

  • Error sum of squares

    In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.

  • Error variance

    The variance of an error term or component in a model.

  • 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.

  • Gamma function

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

  • Geometric random variable

    A discrete random variable that is the number of Bernoulli trials until a success occurs.

  • Goodness of fit

    In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.