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Solutions for Chapter 6.6: Elementary Statistics 12th Edition

Elementary Statistics | 12th Edition | ISBN: 9780321836960 | Authors: Mario F. Triola

Full solutions for Elementary Statistics | 12th Edition

ISBN: 9780321836960

Elementary Statistics | 12th Edition | ISBN: 9780321836960 | Authors: Mario F. Triola

Solutions for Chapter 6.6

Solutions for Chapter 6.6
4 5 0 380 Reviews
Textbook: Elementary Statistics
Edition: 12
Author: Mario F. Triola
ISBN: 9780321836960

This textbook survival guide was created for the textbook: Elementary Statistics, edition: 12. Chapter 6.6 includes 24 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. Since 24 problems in chapter 6.6 have been answered, more than 353874 students have viewed full step-by-step solutions from this chapter. Elementary Statistics was written by and is associated to the ISBN: 9780321836960.

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).

  • Acceptance region

    In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

  • Addition rule

    A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).

  • 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.

  • 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.

  • Causal variable

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

  • Conidence coeficient

    The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

  • Conidence level

    Another term for the conidence coeficient.

  • Contour plot

    A two-dimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

  • Correlation matrix

    A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .

  • Crossed factors

    Another name for factors that are arranged in a factorial experiment.

  • Cumulative normal distribution function

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

  • Dependent variable

    The response variable in regression or a designed experiment.

  • Design matrix

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

  • Dispersion

    The amount of variability exhibited by data

  • Distribution function

    Another name for a cumulative distribution function.

  • Fraction defective

    In statistical quality control, that portion of a number of units or the output of a process that is defective.

  • Frequency distribution

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

  • 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 .