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Solutions for Chapter Chapter 11.3: Fundamentals of Statistics 4th Edition

Fundamentals of Statistics | 4th Edition | ISBN: 9780321838704 | Authors: Michael Sullivan,III

Full solutions for Fundamentals of Statistics | 4th Edition

ISBN: 9780321838704

Fundamentals of Statistics | 4th Edition | ISBN: 9780321838704 | Authors: Michael Sullivan,III

Solutions for Chapter Chapter 11.3

Solutions for Chapter Chapter 11.3
4 5 0 308 Reviews
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Textbook: Fundamentals of Statistics
Edition: 4
Author: Michael Sullivan,III
ISBN: 9780321838704

Chapter Chapter 11.3 includes 26 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: Fundamentals of Statistics, edition: 4. Since 26 problems in chapter Chapter 11.3 have been answered, more than 310243 students have viewed full step-by-step solutions from this chapter. Fundamentals of Statistics was written by and is associated to the ISBN: 9780321838704.

Key Statistics Terms and definitions covered in this textbook
  • Additivity property of x 2

    If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.

  • Analytic study

    A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

  • Assignable cause

    The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

  • Biased estimator

    Unbiased estimator.

  • Conditional mean

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

  • Conditional variance.

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

  • Conidence interval

    If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

  • Conidence level

    Another term for the conidence coeficient.

  • Continuous random variable.

    A random variable with an interval (either inite or ininite) of real numbers for its range.

  • Control limits

    See Control chart.

  • 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.

  • Design matrix

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

  • Error variance

    The variance of an error term or component in a model.

  • Estimate (or point estimate)

    The numerical value of a point estimator.

  • 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.

  • 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.

  • Fraction defective

    In statistical quality control, that portion of a number of units or the output of a process that is defective.

  • Frequency distribution

    An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

  • Generating function

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

  • 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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