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Textbooks / Statistics / Mathematical Statistics and Data Analysis 3

Mathematical Statistics and Data Analysis 3rd Edition - Solutions by Chapter

Mathematical Statistics and Data Analysis | 3rd Edition | ISBN: 9788131519547 | Authors: John A. Rice

Full solutions for Mathematical Statistics and Data Analysis | 3rd Edition

ISBN: 9788131519547

Mathematical Statistics and Data Analysis | 3rd Edition | ISBN: 9788131519547 | Authors: John A. Rice

Mathematical Statistics and Data Analysis | 3rd Edition - Solutions by Chapter

This expansive textbook survival guide covers the following chapters: 14. This textbook survival guide was created for the textbook: Mathematical Statistics and Data Analysis, edition: 3. The full step-by-step solution to problem in Mathematical Statistics and Data Analysis were answered by , our top Statistics solution expert on 01/05/18, 06:27PM. Mathematical Statistics and Data Analysis was written by and is associated to the ISBN: 9788131519547. Since problems from 14 chapters in Mathematical Statistics and Data Analysis have been answered, more than 10713 students have viewed full step-by-step answer.

Key Statistics Terms and definitions covered in this textbook
  • 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

  • Binomial random variable

    A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

  • Bivariate distribution

    The joint probability distribution of two random variables.

  • Bivariate normal distribution

    The joint distribution of two normal random variables

  • C chart

    An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defects-per-unit or U chart.

  • Central limit theorem

    The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

  • Conditional probability mass function

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

  • Covariance

    A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

  • 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.

  • Defect concentration diagram

    A quality tool that graphically shows the location of defects on a part or in a process.

  • Density function

    Another name for a probability density function

  • Distribution free method(s)

    Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

  • Estimate (or point estimate)

    The numerical value of a point estimator.

  • Experiment

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

  • False alarm

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

  • Finite population correction factor

    A term in the formula for the variance of a hypergeometric random variable.

  • Gaussian distribution

    Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

  • 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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