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# Solutions for Chapter 6: Review Execises

## Full solutions for Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition

ISBN: 9780073386102

Solutions for Chapter 6: Review Execises

Solutions for Chapter 6
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##### ISBN: 9780073386102

Since 34 problems in chapter 6: Review Execises have been answered, more than 37309 students have viewed full step-by-step solutions from this chapter. Elementary Statistics: A Step by Step Approach 8th ed. was written by and is associated to the ISBN: 9780073386102. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 6: Review Execises includes 34 full step-by-step solutions. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach 8th ed., edition: 8.

Key Statistics Terms and definitions covered in this textbook

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).

• Backward elimination

A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

• Binomial random variable

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

• 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.

• Conditional probability mass function

The probability mass function of the conditional probability distribution of a discrete 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 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).

• Crossed factors

Another name for factors that are arranged in a factorial experiment.

• 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.

• Discrete distribution

A probability distribution for a discrete random variable

• Discrete random variable

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

• 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.

• 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.

• False alarm

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

• Fisherâ€™s least signiicant difference (LSD) method

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

• Generating function

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

• 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 .

• Goodness of fit

In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

• Harmonic mean

The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .

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