Solutions for Chapter 3-4: Exploratory Data Analysis

Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition | ISBN: 9780073386102 | Authors: Allan G Bluman Professor Emeritus

Full solutions for Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition

ISBN: 9780073386102

Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition | ISBN: 9780073386102 | Authors: Allan G Bluman Professor Emeritus

Solutions for Chapter 3-4: Exploratory Data Analysis

Solutions for Chapter 3-4
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Textbook: Elementary Statistics: A Step by Step Approach 8th ed.
Edition: 8
Author: Allan G Bluman Professor Emeritus
ISBN: 9780073386102

This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach 8th ed., edition: 8. Chapter 3-4: Exploratory Data Analysis includes 18 full step-by-step solutions. Elementary Statistics: A Step by Step Approach 8th ed. was written by Patricia and is associated to the ISBN: 9780073386102. Since 18 problems in chapter 3-4: Exploratory Data Analysis have been answered, more than 11741 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
  • 2 k factorial experiment.

    A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

  • Axioms of probability

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

  • Block

    In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

  • Causal variable

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

  • Central composite design (CCD)

    A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a second-order model.

  • 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.

  • 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.

  • 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 variance.

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

  • 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 .

  • 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.

  • Crossed factors

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

  • Curvilinear regression

    An expression sometimes used for nonlinear regression models or polynomial regression models.

  • Decision interval

    A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.

  • Defect concentration diagram

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

  • 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.

  • Density function

    Another name for a probability density function

  • Event

    A subset of a sample space.

  • Exponential random variable

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

  • Goodness of fit

    In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

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