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Solutions for Chapter 8: The Practice of Statistics 4th Edition

The Practice of Statistics | 4th Edition | ISBN: 9781429245593 | Authors: Daren S. Starnes; Dan Yates; David S. Moore

Full solutions for The Practice of Statistics | 4th Edition

ISBN: 9781429245593

The Practice of Statistics | 4th Edition | ISBN: 9781429245593 | Authors: Daren S. Starnes; Dan Yates; David S. Moore

Solutions for Chapter 8

This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 4. Since 9 problems in chapter 8 have been answered, more than 21760 students have viewed full step-by-step solutions from this chapter. Chapter 8 includes 9 full step-by-step solutions. The Practice of Statistics was written by and is associated to the ISBN: 9781429245593.

Key Statistics Terms and definitions covered in this textbook
  • Alias

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

  • Axioms of probability

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

  • Biased estimator

    Unbiased estimator.

  • Conidence coeficient

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

  • Contingency table.

    A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

  • Continuous distribution

    A probability distribution for a continuous random variable.

  • Continuous random variable.

    A random variable with an interval (either inite or ininite) of real numbers for its range.

  • 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.

  • 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 .

  • Covariance matrix

    A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

  • 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.

  • Critical value(s)

    The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

  • Deming

    W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

  • Designed experiment

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

  • Dispersion

    The amount of variability exhibited by data

  • Erlang random variable

    A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

  • Error of estimation

    The difference between an estimated value and the true value.

  • Exhaustive

    A property of a collection of events that indicates that their union equals the sample space.

  • False alarm

    A signal from a control chart when no assignable causes are present

  • Forward selection

    A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.