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Solutions for Chapter 13: Multiple Linear Regression and Nonlinear Regression

Probability and Statistics for Engineers and Scientists | 4th Edition | ISBN: 9781111827045 | Authors: Anthony J. Hayter

Full solutions for Probability and Statistics for Engineers and Scientists | 4th Edition

ISBN: 9781111827045

Probability and Statistics for Engineers and Scientists | 4th Edition | ISBN: 9781111827045 | Authors: Anthony J. Hayter

Solutions for Chapter 13: Multiple Linear Regression and Nonlinear Regression

Solutions for Chapter 13
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Textbook: Probability and Statistics for Engineers and Scientists
Edition: 4
Author: Anthony J. Hayter
ISBN: 9781111827045

Since 51 problems in chapter 13: Multiple Linear Regression and Nonlinear Regression have been answered, more than 11744 students have viewed full step-by-step solutions from this chapter. Probability and Statistics for Engineers and Scientists was written by and is associated to the ISBN: 9781111827045. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Probability and Statistics for Engineers and Scientists, edition: 4. Chapter 13: Multiple Linear Regression and Nonlinear Regression includes 51 full step-by-step solutions.

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.

  • Attribute control chart

    Any control chart for a discrete random variable. See Variables control chart.

  • 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.

  • Bias

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

  • 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 variance.

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

  • Conidence level

    Another term for the conidence coeficient.

  • Continuity correction.

    A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

  • 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.

  • Correlation coeficient

    A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).

  • Defect concentration diagram

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

  • Deming’s 14 points.

    A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

  • Design matrix

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

  • Designed experiment

    An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

  • Eficiency

    A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

  • Empirical model

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

  • Exponential random variable

    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.

  • Generating function

    A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function

  • Geometric random variable

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

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