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Solutions for Chapter 4-5: Probability and Counting Rules

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 4-5: Probability and Counting Rules

Solutions for Chapter 4-5
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Textbook: Elementary Statistics: A Step by Step Approach
Edition: 7
Author: Allan G. Bluman
ISBN: 9780073534978

Since 118 problems in chapter 4-5: Probability and Counting Rules have been answered, more than 32587 students have viewed full step-by-step solutions from this chapter. Elementary Statistics: A Step by Step Approach was written by and is associated to the ISBN: 9780073534978. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 4-5: Probability and Counting Rules includes 118 full step-by-step solutions.

Key Statistics Terms and definitions covered in this textbook
  • 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).

  • Adjusted R 2

    A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

  • Analytic study

    A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

  • Bias

    An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

  • Block

    In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

  • 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.

  • Conditional probability

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

  • Conidence level

    Another term for the conidence coeficient.

  • 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.

  • Enumerative study

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

  • Error of estimation

    The difference between an estimated value and the true value.

  • 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.

  • Exhaustive

    A property of a collection of events that indicates that their union equals the sample space.

  • False alarm

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

  • Finite population correction factor

    A term in the formula for the variance of a hypergeometric random variable.

  • Fisher’s least signiicant difference (LSD) method

    A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.

  • Forward selection

    A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.

  • Frequency distribution

    An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

  • Generating function

    A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function

  • Geometric random variable

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

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