Solutions for Chapter 8: Mathematical Statistics with Applications 7th Edition

Mathematical Statistics with Applications | 7th Edition | ISBN: 9780495110811 | Authors: Dennis Wackerly, William Mendenhall Richard L. Scheaffer

Full solutions for Mathematical Statistics with Applications | 7th Edition

ISBN: 9780495110811

Mathematical Statistics with Applications | 7th Edition | ISBN: 9780495110811 | Authors: Dennis Wackerly, William Mendenhall Richard L. Scheaffer

Solutions for Chapter 8

Solutions for Chapter 8
4 5 0 308 Reviews
Textbook: Mathematical Statistics with Applications
Edition: 7th
Author: Dennis Wackerly, William Mendenhall Richard L. Scheaffer
ISBN: 9780495110811

Chapter 8 includes 136 full step-by-step solutions. Mathematical Statistics with Applications was written by Sieva Kozinsky and is associated to the ISBN: 9780495110811. Since 136 problems in chapter 8 have been answered, more than 56110 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Mathematical Statistics with Applications , edition: 7th. This expansive textbook survival guide covers the following chapters and their solutions.

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).

  • Average

    See Arithmetic mean.

  • 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’ theorem

    An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).

  • Bias

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

  • C chart

    An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defects-per-unit or U chart.

  • 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.

  • Continuity correction.

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

  • Covariance matrix

    A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

  • Critical value(s)

    The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

  • Curvilinear regression

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

  • 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.

  • Discrete random variable

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

  • Erlang random variable

    A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

  • Event

    A subset of a sample space.

  • Experiment

    A series of tests in which changes are made to the system under study

  • Fixed factor (or fixed effect).

    In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

  • Forward selection

    A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.

  • Gamma random variable

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

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