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Solutions for Chapter 5.3: Binomial Probability Distributions

Essentials of Statistics | 5th Edition | ISBN: 9780321924599 | Authors: Mario F. Triola

Full solutions for Essentials of Statistics | 5th Edition

ISBN: 9780321924599

Essentials of Statistics | 5th Edition | ISBN: 9780321924599 | Authors: Mario F. Triola

Solutions for Chapter 5.3: Binomial Probability Distributions

Solutions for Chapter 5.3
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Textbook: Essentials of Statistics
Edition: 5
Author: Mario F. Triola
ISBN: 9780321924599

Chapter 5.3: Binomial Probability Distributions includes 48 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. Since 48 problems in chapter 5.3: Binomial Probability Distributions have been answered, more than 15425 students have viewed full step-by-step solutions from this chapter. Essentials of Statistics was written by and is associated to the ISBN: 9780321924599. This textbook survival guide was created for the textbook: Essentials of Statistics, edition: 5.

Key Statistics Terms and definitions covered in this textbook
  • a-error (or a-risk)

    In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

  • 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

  • Bernoulli trials

    Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

  • Bias

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

  • Cause-and-effect diagram

    A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

  • Chance cause

    The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.

  • 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.

  • 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

  • Conidence level

    Another term for the conidence coeficient.

  • 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).

  • Critical value(s)

    The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

  • Dependent variable

    The response variable in regression or a designed experiment.

  • Discrete distribution

    A probability distribution for a discrete random variable

  • Discrete random variable

    A random variable with a inite (or countably ininite) range.

  • Error variance

    The variance of an error term or component in a model.

  • F distribution.

    The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.

  • Frequency distribution

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

  • 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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