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Statistics Through Applications 2nd Edition - Solutions by Chapter

Statistics Through Applications | 2nd Edition | ISBN: 9781429219747 | Authors: Daren S. Starnes

Full solutions for Statistics Through Applications | 2nd Edition

ISBN: 9781429219747

Statistics Through Applications | 2nd Edition | ISBN: 9781429219747 | Authors: Daren S. Starnes

Statistics Through Applications | 2nd Edition - Solutions by Chapter

This expansive textbook survival guide covers the following chapters: 10. Since problems from 10 chapters in Statistics Through Applications have been answered, more than 5056 students have viewed full step-by-step answer. The full step-by-step solution to problem in Statistics Through Applications were answered by Sieva Kozinsky, our top Statistics solution expert on 11/10/17, 06:04PM. This textbook survival guide was created for the textbook: Statistics Through Applications, edition: 2. Statistics Through Applications was written by Sieva Kozinsky and is associated to the ISBN: 9781429219747.

Key Statistics Terms and definitions covered in this textbook
  • 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

  • Alternative hypothesis

    In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

  • Causal variable

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

  • Cause-and-effect diagram

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

  • Central composite design (CCD)

    A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a second-order model.

  • Conditional probability density function

    The probability density function of the conditional probability distribution of a continuous random variable.

  • Continuous random variable.

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

  • Contour plot

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

  • 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.

  • Deining relation

    A subset of effects in a fractional factorial design that deine the aliases in the design.

  • Deming

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

  • Design matrix

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

  • 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.

  • 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.

  • 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.

  • 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.

  • Fraction defective control chart

    See P chart

  • Fractional factorial experiment

    A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

  • Gamma function

    A function used in the probability density function of a gamma random variable that can be considered to extend factorials

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