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# Solutions for Chapter 14-1: Sampling and Simulation

## Full solutions for Elementary Statistics: A Step by Step Approach | 7th Edition

ISBN: 9780073534978

Solutions for Chapter 14-1: Sampling and Simulation

Solutions for Chapter 14-1
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##### ISBN: 9780073534978

This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7. Chapter 14-1: Sampling and Simulation includes 26 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. Since 26 problems in chapter 14-1: Sampling and Simulation have been answered, more than 30221 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.

Key Statistics Terms and definitions covered in this textbook
• Additivity property of x 2

If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.

• Attribute control chart

Any control chart for a discrete random variable. See Variables control chart.

• Axioms of probability

A set of rules that probabilities deined on a sample space must follow. See Probability

• Bayesâ€™ estimator

An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.

• Categorical data

Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

• Combination.

A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

• Conditional probability

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

• Conidence coeficient

The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

• Conidence interval

If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

• Continuity correction.

A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

• Continuous uniform random variable

A continuous random variable with range of a inite interval and a constant probability density function.

• Contrast

A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

• Control limits

See Control chart.

• Design matrix

A matrix that provides the tests that are to be conducted in an experiment.

• Error of estimation

The difference between an estimated value and the true value.

• 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.

• Fixed factor (or fixed effect).

In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

• 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.

• Fraction defective control chart

See P chart

• 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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