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> > Introduction to Probability and Statistics for Engineers and Scientists 5

Introduction to Probability and Statistics for Engineers and Scientists 5th Edition - Solutions by Chapter

Introduction to Probability and Statistics for Engineers and Scientists | 5th Edition | ISBN: 9780123948113 | Authors: Sheldon M. Ross

Full solutions for Introduction to Probability and Statistics for Engineers and Scientists | 5th Edition

ISBN: 9780123948113

Introduction to Probability and Statistics for Engineers and Scientists | 5th Edition | ISBN: 9780123948113 | Authors: Sheldon M. Ross

Introduction to Probability and Statistics for Engineers and Scientists | 5th Edition - Solutions by Chapter

The full step-by-step solution to problem in Introduction to Probability and Statistics for Engineers and Scientists were answered by , our top Statistics solution expert on 01/09/18, 07:40PM. Introduction to Probability and Statistics for Engineers and Scientists was written by and is associated to the ISBN: 9780123948113. Since problems from 15 chapters in Introduction to Probability and Statistics for Engineers and Scientists have been answered, more than 4735 students have viewed full step-by-step answer. This expansive textbook survival guide covers the following chapters: 15. This textbook survival guide was created for the textbook: Introduction to Probability and Statistics for Engineers and Scientists, edition: 5.

Key Statistics Terms and definitions covered in this textbook
  • Asymptotic relative eficiency (ARE)

    Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • Causal variable

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

  • Combination.

    A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

  • Components of variance

    The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

  • Conditional mean

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

  • Conditional probability

    The probability of an event given that the random experiment produces an outcome in another event.

  • Conidence coeficient

    The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

  • Consistent estimator

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

  • Cook’s distance

    In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

  • Correlation matrix

    A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .

  • Crossed factors

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

  • Density function

    Another name for a probability density function

  • Dependent variable

    The response variable in regression or a designed experiment.

  • Design matrix

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

  • 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.

  • Exhaustive

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

  • Exponential random variable

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

  • Fisher’s least signiicant difference (LSD) method

    A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.

  • Fixed factor (or fixed effect).

    In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

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