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Solutions for Chapter Chapter 20: An Introduction to Decision Theory

Statistical Techniques in Business and Economics | 15th Edition | ISBN: 9780073401805 | Authors: Douglas Lind, William Marchal, Samuel Wathen

Full solutions for Statistical Techniques in Business and Economics | 15th Edition

ISBN: 9780073401805

Statistical Techniques in Business and Economics | 15th Edition | ISBN: 9780073401805 | Authors: Douglas Lind, William Marchal, Samuel Wathen

Solutions for Chapter Chapter 20: An Introduction to Decision Theory

Solutions for Chapter Chapter 20
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Textbook: Statistical Techniques in Business and Economics
Edition: 15
Author: Douglas Lind, William Marchal, Samuel Wathen
ISBN: 9780073401805

Since 20 problems in chapter Chapter 20: An Introduction to Decision Theory have been answered, more than 32529 students have viewed full step-by-step solutions from this chapter. Statistical Techniques in Business and Economics was written by and is associated to the ISBN: 9780073401805. This textbook survival guide was created for the textbook: Statistical Techniques in Business and Economics, edition: 15. Chapter Chapter 20: An Introduction to Decision Theory includes 20 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions.

Key Statistics Terms and definitions covered in this textbook
  • 2 k p - factorial experiment

    A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

  • All possible (subsets) regressions

    A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

  • 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.

  • 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.

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • Block

    In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

  • 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).

  • Comparative experiment

    An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

  • Conditional variance.

    The variance of the conditional probability distribution of a random variable.

  • Control chart

    A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the in-control value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be in-control, or free from assignable causes. Points beyond the control limits indicate an out-of-control process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

  • 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.

  • Cumulative normal distribution function

    The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.

  • Defect concentration diagram

    A quality tool that graphically shows the location of defects on a part or in a process.

  • Defects-per-unit control chart

    See U chart

  • 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.

  • Deining relation

    A subset of effects in a fractional factorial design that deine the aliases in the design.

  • Error of estimation

    The difference between an estimated value and the true value.

  • F distribution.

    The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.

  • 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.

  • Fraction defective

    In statistical quality control, that portion of a number of units or the output of a process that is defective.

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