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# Solutions for Chapter 3: Describing Relationships

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

ISBN: 9781464108730

Solutions for Chapter 3: Describing Relationships

Solutions for Chapter 3
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##### ISBN: 9781464108730

This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 5. Chapter 3: Describing Relationships includes 19 full step-by-step solutions. Since 19 problems in chapter 3: Describing Relationships have been answered, more than 11192 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. This expansive textbook survival guide covers the following chapters and their solutions.

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).

• All possible (subsets) regressions

A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

• Alternative hypothesis

In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

• Analysis of variance (ANOVA)

A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

• Bivariate normal distribution

The joint distribution of two normal random variables

• Box plot (or box and whisker plot)

A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

• 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.

• Components of variance

The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

• Conditional probability mass function

The probability mass function of the conditional probability distribution of a discrete random variable.

• Continuity correction.

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

• 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.

• Dependent variable

The response variable in regression or a designed experiment.

• Design matrix

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

• Discrete uniform random variable

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

• Estimate (or point estimate)

The numerical value of a point estimator.

• Exhaustive

A property of a collection of events that indicates that their union equals the sample space.

• False alarm

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

• Fisher’s least signiicant difference (LSD) method

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

• Fraction defective

In statistical quality control, that portion of a number of units or the output of a process that is defective.

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