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Solutions for Chapter 3-4: Data Description

Elementary Statistics: A Step by Step Approach | 7th Edition | ISBN: 9780073534978 | Authors: Allan G. Bluman

Full solutions for Elementary Statistics: A Step by Step Approach | 7th Edition

ISBN: 9780073534978

Elementary Statistics: A Step by Step Approach | 7th Edition | ISBN: 9780073534978 | Authors: Allan G. Bluman

Solutions for Chapter 3-4: Data Description

Solutions for Chapter 3-4
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Textbook: Elementary Statistics: A Step by Step Approach
Edition: 7
Author: Allan G. Bluman
ISBN: 9780073534978

This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7. This expansive textbook survival guide covers the following chapters and their solutions. Elementary Statistics: A Step by Step Approach was written by and is associated to the ISBN: 9780073534978. Since 18 problems in chapter 3-4: Data Description have been answered, more than 35814 students have viewed full step-by-step solutions from this chapter. Chapter 3-4: Data Description includes 18 full step-by-step solutions.

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

  • Axioms of probability

    A set of rules that probabilities deined on a sample space must follow. See Probability

  • Causal variable

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

  • 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 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 level

    Another term for the conidence coeficient.

  • Counting techniques

    Formulas used to determine the number of elements in sample spaces and events.

  • 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.

  • 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.

  • Degrees of freedom.

    The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

  • Error propagation

    An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

  • 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.

  • Error variance

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

  • Experiment

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

  • 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.

  • Forward selection

    A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.

  • Fraction defective

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

  • Geometric random variable

    A discrete random variable that is the number of Bernoulli trials until a success occurs.

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