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Solutions for Chapter 6: Descriptive Statistics

Applied Statistics and Probability for Engineers | 5th Edition | ISBN: 9780470053041 | Authors: Douglas C. Montgomery, George C. Runger

Full solutions for Applied Statistics and Probability for Engineers | 5th Edition

ISBN: 9780470053041

Applied Statistics and Probability for Engineers | 5th Edition | ISBN: 9780470053041 | Authors: Douglas C. Montgomery, George C. Runger

Solutions for Chapter 6: Descriptive Statistics

Solutions for Chapter 6
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Textbook: Applied Statistics and Probability for Engineers
Edition: 5
Author: Douglas C. Montgomery, George C. Runger
ISBN: 9780470053041

Since 114 problems in chapter 6: Descriptive Statistics have been answered, more than 24298 students have viewed full step-by-step solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 6: Descriptive Statistics includes 114 full step-by-step solutions. This textbook survival guide was created for the textbook: Applied Statistics and Probability for Engineers, edition: 5. Applied Statistics and Probability for Engineers was written by and is associated to the ISBN: 9780470053041.

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.

  • Alias

    In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

  • Bias

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

  • 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.

  • 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 probability mass function

    The probability mass function of the conditional probability distribution of a discrete random variable.

  • Confounding

    When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.

  • Conidence level

    Another term for the conidence coeficient.

  • Control limits

    See Control chart.

  • Cumulative normal distribution function

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

  • 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.

  • Exponential random variable

    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.

  • 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 .

  • Geometric random variable

    A discrete random variable that is the number of Bernoulli trials until a success occurs.

  • Hat matrix.

    In multiple regression, the matrix H XXX X = ( ) ? ? -1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .

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