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Solutions for Chapter 3-3: 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 3-3: Data Description

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

Since 36 problems in chapter 3-3: Data Description have been answered, more than 15317 students have viewed full step-by-step solutions from this chapter. 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. Chapter 3-3: Data Description includes 36 full step-by-step solutions. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7.

Key Statistics Terms and definitions covered in this textbook
  • Analysis of variance (ANOVA)

    A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

  • Bayes’ theorem

    An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).

  • Binomial random variable

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

  • Coeficient of determination

    See R 2 .

  • Completely randomized design (or experiment)

    A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

  • Conditional probability

    The probability of an event given that the random experiment produces an outcome in another event.

  • Conditional probability mass function

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

  • Conditional variance.

    The variance of the conditional probability distribution of a 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

  • Consistent estimator

    An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

  • Correlation coeficient

    A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).

  • 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 .

  • Cumulative sum control chart (CUSUM)

    A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

  • Defect concentration diagram

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

  • Deming’s 14 points.

    A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

  • Design matrix

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

  • 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

  • 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

  • Geometric mean.

    The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .

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