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Solutions for Chapter 9.5: Elementary Statistics 12th Edition

Elementary Statistics | 12th Edition | ISBN: 9780321836960 | Authors: Mario F. Triola

Full solutions for Elementary Statistics | 12th Edition

ISBN: 9780321836960

Elementary Statistics | 12th Edition | ISBN: 9780321836960 | Authors: Mario F. Triola

Solutions for Chapter 9.5

Solutions for Chapter 9.5
4 5 0 265 Reviews
Textbook: Elementary Statistics
Edition: 12
Author: Mario F. Triola
ISBN: 9780321836960

Chapter 9.5 includes 33 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: Elementary Statistics, edition: 12. Since 33 problems in chapter 9.5 have been answered, more than 358629 students have viewed full step-by-step solutions from this chapter. Elementary Statistics was written by and is associated to the ISBN: 9780321836960.

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

    A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

  • Average

    See Arithmetic mean.

  • 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.

  • Biased estimator

    Unbiased estimator.

  • Bivariate normal distribution

    The joint distribution of two normal random variables

  • Cause-and-effect diagram

    A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

  • 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.

  • 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.

  • Continuous uniform random variable

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

  • Contrast

    A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

  • Curvilinear regression

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

  • Deining relation

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

  • Deming

    W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

  • Discrete distribution

    A probability distribution for a discrete random variable

  • Empirical model

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

  • Error of estimation

    The difference between an estimated value and the true value.

  • Experiment

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

  • F-test

    Any test of signiicance involving the F distribution. The most common F-tests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

  • 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

  • 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.