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Solutions for Chapter 15.3: The Sign Test for a Paired Experiment

Introduction to Probability and Statistics 1 | 14th Edition | ISBN: 9781133103752 | Authors: William Mendenhall Robert J. Beaver, Barbara M. Beaver

Full solutions for Introduction to Probability and Statistics 1 | 14th Edition

ISBN: 9781133103752

Introduction to Probability and Statistics 1 | 14th Edition | ISBN: 9781133103752 | Authors: William Mendenhall Robert J. Beaver, Barbara M. Beaver

Solutions for Chapter 15.3: The Sign Test for a Paired Experiment

Introduction to Probability and Statistics 1 was written by and is associated to the ISBN: 9781133103752. Chapter 15.3: The Sign Test for a Paired Experiment includes 8 full step-by-step solutions. This textbook survival guide was created for the textbook: Introduction to Probability and Statistics 1, edition: 14. Since 8 problems in chapter 15.3: The Sign Test for a Paired Experiment have been answered, more than 9713 students have viewed full step-by-step solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions.

Key Statistics Terms and definitions covered in this textbook
  • 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

  • Average

    See Arithmetic mean.

  • Bimodal distribution.

    A distribution with two modes

  • Bivariate normal distribution

    The joint distribution of two normal random variables

  • Central tendency

    The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

  • Conditional probability density function

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

  • Conditional probability mass function

    The probability mass function of the conditional probability distribution of a discrete random variable.

  • 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.

  • 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.

  • Density function

    Another name for a probability density function

  • Discrete distribution

    A probability distribution for a discrete random variable

  • Erlang random variable

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

  • 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.

  • Experiment

    A series of tests in which changes are made to the system under study

  • Extra sum of squares method

    A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

  • 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.

  • Gamma function

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

  • Generating function

    A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function

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