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Textbooks / Statistics / Understandable Statistics 9

Understandable Statistics 9th Edition - Solutions by Chapter

Understandable Statistics | 9th Edition | ISBN: 9780618949922 | Authors: Charles Henry Brase, Corrinne Pellillo Brase

Full solutions for Understandable Statistics | 9th Edition

ISBN: 9780618949922

Understandable Statistics | 9th Edition | ISBN: 9780618949922 | Authors: Charles Henry Brase, Corrinne Pellillo Brase

Understandable Statistics | 9th Edition - Solutions by Chapter

Solutions by Chapter
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Textbook: Understandable Statistics
Edition: 9
Author: Charles Henry Brase, Corrinne Pellillo Brase
ISBN: 9780618949922

This expansive textbook survival guide covers the following chapters: 57. Understandable Statistics was written by and is associated to the ISBN: 9780618949922. Since problems from 57 chapters in Understandable Statistics have been answered, more than 25314 students have viewed full step-by-step answer. This textbook survival guide was created for the textbook: Understandable Statistics, edition: 9. The full step-by-step solution to problem in Understandable Statistics were answered by , our top Statistics solution expert on 01/04/18, 09:09PM.

Key Statistics Terms and definitions covered in this textbook
  • Adjusted R 2

    A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

  • 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

  • Attribute

    A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

  • 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.

  • Backward elimination

    A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

  • 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.

  • 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.

  • Causal variable

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

  • Completely randomized design (or experiment)

    A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

  • Conditional mean

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

  • Conditional variance.

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

  • Conidence interval

    If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

  • Convolution

    A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

  • 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.

  • Design matrix

    A matrix that provides the tests that are to be conducted in an experiment.

  • Distribution free method(s)

    Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

  • 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.

  • Event

    A subset of a sample space.

  • Experiment

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

  • Gaussian distribution

    Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

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