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Solutions for Chapter Chapter 12: General Rules of Probability

Full solutions for The Basic Practice of Statistics | 4th Edition

ISBN: 9780716774785

Solutions for Chapter Chapter 12: General Rules of Probability

Solutions for Chapter Chapter 12
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Textbook: The Basic Practice of Statistics
Edition: 4
Author: David S. Moore
ISBN: 9780716774785

This textbook survival guide was created for the textbook: The Basic Practice of Statistics, edition: 4. Since 59 problems in chapter Chapter 12: General Rules of Probability have been answered, more than 10673 students have viewed full step-by-step solutions from this chapter. The Basic Practice of Statistics was written by and is associated to the ISBN: 9780716774785. This expansive textbook survival guide covers the following chapters and their solutions. Chapter Chapter 12: General Rules of Probability includes 59 full step-by-step solutions.

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).

  • 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

  • Analysis of variance (ANOVA)

    A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

  • Biased estimator

    Unbiased estimator.

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • Box plot (or box and whisker plot)

    A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

  • 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

  • Contrast

    A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

  • Convolution

    A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

  • 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.

  • Crossed factors

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

  • Cumulative distribution function

    For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.

  • Designed experiment

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

  • Error sum of squares

    In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.

  • Estimator (or point estimator)

    A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

  • 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.

  • Gamma function

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

  • Gamma random variable

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

  • 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.

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