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# Solutions for Chapter 12: More About Regression

## Full solutions for The Practice of Statistics | 5th Edition

ISBN: 9781464108730

Solutions for Chapter 12: More About Regression

Solutions for Chapter 12
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##### ISBN: 9781464108730

This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 5. Since 64 problems in chapter 12: More About Regression have been answered, more than 25391 students have viewed full step-by-step solutions from this chapter. The Practice of Statistics was written by and is associated to the ISBN: 9781464108730. Chapter 12: More About Regression includes 64 full step-by-step solutions.

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.

• 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

• Arithmetic mean

The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

• Attribute

A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

• Axioms of probability

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

• Bivariate distribution

The joint probability distribution of two random variables.

• 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.

• Central tendency

The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

• Coeficient of determination

See R 2 .

• Confounding

When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.

• 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.

• Correction factor

A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

• Counting techniques

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

• Discrete uniform random variable

A discrete random variable with a inite range and constant probability mass function.

• Empirical model

A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.

• Error mean square

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

• Error variance

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

• False alarm

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

• 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 .

• Geometric random variable

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

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