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Solutions for Chapter 10: The Practice of Statistics 4th Edition

The Practice of Statistics | 4th Edition | ISBN: 9781429245593 | Authors: Daren S. Starnes; Dan Yates; David S. Moore

Full solutions for The Practice of Statistics | 4th Edition

ISBN: 9781429245593

The Practice of Statistics | 4th Edition | ISBN: 9781429245593 | Authors: Daren S. Starnes; Dan Yates; David S. Moore

Solutions for Chapter 10

This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 4. The Practice of Statistics was written by and is associated to the ISBN: 9781429245593. Chapter 10 includes 10 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. Since 10 problems in chapter 10 have been answered, more than 8918 students have viewed full step-by-step solutions from this chapter.

Key Statistics Terms and definitions covered in this textbook
  • 2 k p - factorial experiment

    A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

  • Addition rule

    A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).

  • 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

  • Arithmetic mean

    The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

  • Biased estimator

    Unbiased estimator.

  • Conditional probability

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

  • Conditional variance.

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

  • Convolution

    A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

  • Correlation

    In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

  • 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 .

  • 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.

  • Deming’s 14 points.

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

  • Designed experiment

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

  • Discrete distribution

    A probability distribution for a discrete random variable

  • Discrete uniform random variable

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

  • Erlang random variable

    A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

  • 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.

  • False alarm

    A signal from a control chart when no assignable causes are present

  • Fraction defective control chart

    See P chart

  • Gamma random variable

    A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

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