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Solutions for Chapter 1: Overview and Descriptive Statistics

Probability and Statistics for Engineering and the Sciences (with Student Suite Online) | 7th Edition | ISBN: 9780495382171 | Authors: Jay L. Devore

Full solutions for Probability and Statistics for Engineering and the Sciences (with Student Suite Online) | 7th Edition

ISBN: 9780495382171

Probability and Statistics for Engineering and the Sciences (with Student Suite Online) | 7th Edition | ISBN: 9780495382171 | Authors: Jay L. Devore

Solutions for Chapter 1: Overview and Descriptive Statistics

Solutions for Chapter 1
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Textbook: Probability and Statistics for Engineering and the Sciences (with Student Suite Online)
Edition: 7
Author: Jay L. Devore
ISBN: 9780495382171

Since 83 problems in chapter 1: Overview and Descriptive Statistics have been answered, more than 20955 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Probability and Statistics for Engineering and the Sciences (with Student Suite Online), edition: 7. This expansive textbook survival guide covers the following chapters and their solutions. Probability and Statistics for Engineering and the Sciences (with Student Suite Online) was written by and is associated to the ISBN: 9780495382171. Chapter 1: Overview and Descriptive Statistics includes 83 full step-by-step solutions.

Key Statistics Terms and definitions covered in this textbook
  • Asymptotic relative eficiency (ARE)

    Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

  • Bayes’ estimator

    An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.

  • Biased estimator

    Unbiased estimator.

  • 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.

  • Chi-square (or chi-squared) random variable

    A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

  • Combination.

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

  • Comparative experiment

    An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

  • Continuity correction.

    A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

  • Continuous uniform random variable

    A continuous random variable with range of a inite interval and a constant probability density function.

  • Correction factor

    A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

  • 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.

  • 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

  • Discrete uniform random variable

    A discrete random variable with a inite range and constant probability mass function.

  • Dispersion

    The amount of variability exhibited by data

  • Estimator (or point estimator)

    A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

  • Expected value

    The expected value of a random variable X is its long-term average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.

  • First-order model

    A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model

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