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Solutions for Chapter 8: Multiple and Logistic Regression

OpenIntro Statistics | 3rd Edition | ISBN: 9781943450039 | Authors: David M Diez; Christopher D Barr; Mine Çetinkaya-Rundel

Full solutions for OpenIntro Statistics | 3rd Edition

ISBN: 9781943450039

OpenIntro Statistics | 3rd Edition | ISBN: 9781943450039 | Authors: David M Diez; Christopher D Barr; Mine Çetinkaya-Rundel

Solutions for Chapter 8: Multiple and Logistic Regression

Solutions for Chapter 8
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Textbook: OpenIntro Statistics
Edition: 3
Author: David M Diez; Christopher D Barr; Mine Çetinkaya-Rundel
ISBN: 9781943450039

This expansive textbook survival guide covers the following chapters and their solutions. Since 18 problems in chapter 8: Multiple and Logistic Regression have been answered, more than 45583 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: OpenIntro Statistics, edition: 3. Chapter 8: Multiple and Logistic Regression includes 18 full step-by-step solutions. OpenIntro Statistics was written by and is associated to the ISBN: 9781943450039.

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.

  • Additivity property of x 2

    If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.

  • 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.

  • Asymptotic relative eficiency (ARE)

    Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

  • Average

    See Arithmetic mean.

  • Axioms of probability

    A set of rules that probabilities deined on a sample space must follow. See Probability

  • 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.

  • Conditional probability

    The probability of an event given that the random experiment produces an outcome in another event.

  • Conditional probability mass function

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

  • Conidence level

    Another term for the conidence coeficient.

  • Continuous uniform random variable

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

  • Counting techniques

    Formulas used to determine the number of elements in sample spaces and events.

  • Cumulative normal distribution function

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

  • Cumulative sum control chart (CUSUM)

    A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

  • 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).

  • Expected value

    The expected value of a random variable X is its long-term average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.

  • Finite population correction factor

    A term in the formula for the variance of a hypergeometric random variable.

  • 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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