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# Solutions for Chapter 13: Comparing Three or More Means ## Full solutions for Statistics: Informed Decisions Using Data | 4th Edition

ISBN: 9780321757272 Solutions for Chapter 13: Comparing Three or More Means

Solutions for Chapter 13
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##### ISBN: 9780321757272

Chapter 13: Comparing Three or More Means includes 5 full step-by-step solutions. This textbook survival guide was created for the textbook: Statistics: Informed Decisions Using Data , edition: 4. Statistics: Informed Decisions Using Data was written by and is associated to the ISBN: 9780321757272. This expansive textbook survival guide covers the following chapters and their solutions. Since 5 problems in chapter 13: Comparing Three or More Means have been answered, more than 143790 students have viewed full step-by-step solutions from this chapter.

Key Statistics Terms and definitions covered in this textbook
• 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

• Bayes’ theorem

An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).

• Bias

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

• Biased estimator

Unbiased estimator.

• 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.

• 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.

• 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.

• 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

• 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).

• Counting techniques

Formulas used to determine the number of elements in sample spaces and events.

• Crossed factors

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

• Dependent variable

The response variable in regression or a designed experiment.

• Distribution function

Another name for a cumulative distribution function.

• Error mean square

The error sum of squares divided by its number of degrees of freedom.

• Estimate (or point estimate)

The numerical value of a point estimator.

• Finite population correction factor

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

• Gaussian distribution

Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

• 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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