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Solutions for Chapter 1.2: Elementary Statistics: Picturing the World 5th Edition

Elementary Statistics: Picturing the World | 5th Edition | ISBN: 9780321693624 | Authors: Ron Larson

Full solutions for Elementary Statistics: Picturing the World | 5th Edition

ISBN: 9780321693624

Elementary Statistics: Picturing the World | 5th Edition | ISBN: 9780321693624 | Authors: Ron Larson

Solutions for Chapter 1.2

Solutions for Chapter 1.2
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Textbook: Elementary Statistics: Picturing the World
Edition: 5
Author: Ron Larson
ISBN: 9780321693624

This expansive textbook survival guide covers the following chapters and their solutions. Chapter 1.2 includes 25 full step-by-step solutions. Elementary Statistics: Picturing the World was written by and is associated to the ISBN: 9780321693624. Since 25 problems in chapter 1.2 have been answered, more than 11213 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Elementary Statistics: Picturing the World, edition: 5.

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.

  • All possible (subsets) regressions

    A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

  • Categorical data

    Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

  • 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.

  • Chi-square test

    Any test of signiicance based on the chi-square distribution. The most common chi-square tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

  • Combination.

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

  • Conidence coeficient

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

  • 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.

  • 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.

  • Curvilinear regression

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

  • Defect concentration diagram

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

  • Design matrix

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

  • 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.

  • Estimate (or point estimate)

    The numerical value of a point estimator.

  • 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.

  • Gamma function

    A function used in the probability density function of a gamma random variable that can be considered to extend factorials

  • Generator

    Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.

  • Geometric random variable

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

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