Solutions for Chapter 11.2: Inference for Two-Way Tables

Full solutions for The Practice of Statistics | 5th Edition

ISBN: 9781464108730

Solutions for Chapter 11.2: Inference for Two-Way Tables

Solutions for Chapter 11.2
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Textbook: The Practice of Statistics
Edition: 5
Author: Daren S. Starnes, Josh Tabor
ISBN: 9781464108730

The Practice of Statistics was written by and is associated to the ISBN: 9781464108730. Since 34 problems in chapter 11.2: Inference for Two-Way Tables have been answered, more than 10489 students have viewed full step-by-step solutions from this chapter. Chapter 11.2: Inference for Two-Way Tables includes 34 full step-by-step solutions. 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: 5.

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

  • Average

    See Arithmetic mean.

  • Axioms of probability

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

  • Bimodal distribution.

    A distribution with two modes

  • Center line

    A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

  • Chi-square test

    Any test of signiicance based on the chi-square distribution. The most common chi-square tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

  • Conditional probability distribution

    The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

  • Consistent estimator

    An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

  • Continuous distribution

    A probability distribution for a continuous random variable.

  • 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

  • Defects-per-unit control chart

    See U chart

  • Degrees of freedom.

    The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

  • Deming

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

  • Dependent variable

    The response variable in regression or a designed experiment.

  • Enumerative study

    A study in which a sample from a population is used to make inference to the population. See Analytic study

  • 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.

  • False alarm

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

  • Gamma function

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

  • Generator

    Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.

  • 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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