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Solutions for Chapter 7.3: ASSESSING NORMALITY

Statistics: Informed Decisions Using Data | 4th Edition | ISBN: 9780321757272 | Authors: Michael Sullivan, III

Full solutions for Statistics: Informed Decisions Using Data | 4th Edition

ISBN: 9780321757272

Statistics: Informed Decisions Using Data | 4th Edition | ISBN: 9780321757272 | Authors: Michael Sullivan, III

Solutions for Chapter 7.3: ASSESSING NORMALITY

This expansive textbook survival guide covers the following chapters and their solutions. Since 14 problems in chapter 7.3: ASSESSING NORMALITY have been answered, more than 149801 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Statistics: Informed Decisions Using Data , edition: 4. Chapter 7.3: ASSESSING NORMALITY includes 14 full step-by-step solutions. Statistics: Informed Decisions Using Data was written by and is associated to the ISBN: 9780321757272.

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

    In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I 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

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • 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.

  • Conditional mean

    The mean of the conditional probability distribution of a random variable.

  • Conditional probability density function

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

  • Contingency table.

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

  • Counting techniques

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

  • 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.

  • Decision interval

    A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.

  • Defects-per-unit control chart

    See U chart

  • Discrete distribution

    A probability distribution for a discrete random variable

  • Dispersion

    The amount of variability exhibited by data

  • Error propagation

    An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

  • Exhaustive

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

  • 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.

  • Finite population correction factor

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

  • 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 random variable

    A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

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