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Solutions for Chapter 4.7: Elementary Statistics 12th Edition

Elementary Statistics | 12th Edition | ISBN: 9780321836960 | Authors: Mario F. Triola

Full solutions for Elementary Statistics | 12th Edition

ISBN: 9780321836960

Elementary Statistics | 12th Edition | ISBN: 9780321836960 | Authors: Mario F. Triola

Solutions for Chapter 4.7

Solutions for Chapter 4.7
4 5 0 346 Reviews
Textbook: Elementary Statistics
Edition: 12
Author: Mario F. Triola
ISBN: 9780321836960

Since 19 problems in chapter 4.7 have been answered, more than 390645 students have viewed full step-by-step solutions from this chapter. Elementary Statistics was written by and is associated to the ISBN: 9780321836960. This textbook survival guide was created for the textbook: Elementary Statistics, edition: 12. Chapter 4.7 includes 19 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions.

Key Statistics Terms and definitions covered in this textbook
  • Attribute

    A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

  • Binomial random variable

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

  • Bivariate normal distribution

    The joint distribution of two normal random variables

  • 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.

  • Coeficient of determination

    See R 2 .

  • Conditional probability distribution

    The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

  • Contingency table.

    A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

  • Control chart

    A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the in-control value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be in-control, or free from assignable causes. Points beyond the control limits indicate an out-of-control process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

  • 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 .

  • 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.

  • 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

  • Defects-per-unit control chart

    See U chart

  • 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.

  • Designed experiment

    An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

  • Dispersion

    The amount of variability exhibited by data

  • Distribution free method(s)

    Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

  • Eficiency

    A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

  • Enumerative study

    A study in which a sample from a population is used to make inference to the population. See Analytic study

  • Error mean square

    The error sum of squares divided by its number of degrees of freedom.

  • 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