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Solutions for Chapter 12: Experiments and Observational Studies

Stats Modeling the World | 4th Edition | ISBN: 9780321854018 | Authors: David E. Bock, Paul F. Velleman, Richard D. De Veaux

Full solutions for Stats Modeling the World | 4th Edition

ISBN: 9780321854018

Stats Modeling the World | 4th Edition | ISBN: 9780321854018 | Authors: David E. Bock, Paul F. Velleman, Richard D. De Veaux

Solutions for Chapter 12: Experiments and Observational Studies

Solutions for Chapter 12
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Textbook: Stats Modeling the World
Edition: 4
Author: David E. Bock, Paul F. Velleman, Richard D. De Veaux
ISBN: 9780321854018

This textbook survival guide was created for the textbook: Stats Modeling the World, edition: 4. This expansive textbook survival guide covers the following chapters and their solutions. Since 47 problems in chapter 12: Experiments and Observational Studies have been answered, more than 19429 students have viewed full step-by-step solutions from this chapter. Chapter 12: Experiments and Observational Studies includes 47 full step-by-step solutions. Stats Modeling the World was written by and is associated to the ISBN: 9780321854018.

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

  • Assignable cause

    The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

  • Causal variable

    When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

  • Chi-square (or chi-squared) random variable

    A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

  • Coeficient of determination

    See R 2 .

  • Correlation matrix

    A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .

  • Counting techniques

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

  • 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.

  • Defects-per-unit control chart

    See U chart

  • Dependent variable

    The response variable in regression or a designed experiment.

  • Error mean square

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

  • Error of estimation

    The difference between an estimated value and the true value.

  • 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.

  • Experiment

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

  • Extra sum of squares method

    A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

  • False alarm

    A signal from a control chart when no assignable causes are present

  • 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

  • Generator

    Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.

  • Geometric mean.

    The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .

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