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Solutions for Chapter 28: Multiple Regression

Stats Modeling the World | 4th Edition | ISBN: 9780321854018 | Authors: David E. Bock, Paul F. Velleman, Richard D. De Veaux

Full solutions for Stats Modeling the World | 4th Edition

ISBN: 9780321854018

Stats Modeling the World | 4th Edition | ISBN: 9780321854018 | Authors: David E. Bock, Paul F. Velleman, Richard D. De Veaux

Solutions for Chapter 28: Multiple Regression

Solutions for Chapter 28
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Textbook: Stats Modeling the World
Edition: 4
Author: David E. Bock, Paul F. Velleman, Richard D. De Veaux
ISBN: 9780321854018

This textbook survival guide was created for the textbook: Stats Modeling the World, edition: 4. Since 27 problems in chapter 28: Multiple Regression have been answered, more than 20102 students have viewed full step-by-step solutions from this chapter. Chapter 28: Multiple Regression includes 27 full step-by-step solutions. Stats Modeling the World was written by and is associated to the ISBN: 9780321854018. This expansive textbook survival guide covers the following chapters and their solutions.

Key Statistics Terms and definitions covered in this textbook
  • a-error (or a-risk)

    In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

  • 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.

  • Bias

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

  • Biased estimator

    Unbiased estimator.

  • 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.

  • Cause-and-effect diagram

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

  • 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.

  • Conidence level

    Another term for the conidence coeficient.

  • Consistent estimator

    An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

  • Correlation

    In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

  • Critical region

    In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

  • Defect concentration diagram

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

  • Discrete uniform random variable

    A discrete random variable with a inite range and constant probability mass function.

  • Dispersion

    The amount of variability exhibited by data

  • 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.

  • Factorial experiment

    A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

  • Frequency distribution

    An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

  • 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

  • Generator

    Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.

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