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# Solutions for Chapter 13: Nonparametric Statistics

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

ISBN: 9780073534978

Solutions for Chapter 13: Nonparametric Statistics

Solutions for Chapter 13
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##### ISBN: 9780073534978

Since 49 problems in chapter 13: Nonparametric Statistics have been answered, more than 36282 students have viewed full step-by-step solutions from this chapter. Chapter 13: Nonparametric Statistics includes 49 full step-by-step solutions. This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach, edition: 7. This expansive textbook survival guide covers the following chapters and their solutions. 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
• Alias

In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

• Assignable cause

The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

• 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.

• Average

See Arithmetic mean.

• Axioms of probability

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

• C chart

An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defects-per-unit or U chart.

• 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.

• Correlation matrix

A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .

• Crossed factors

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

• Curvilinear regression

An expression sometimes used for nonlinear regression models or polynomial regression models.

• Defect

Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

• Defect concentration diagram

A quality tool that graphically shows the location of defects on a part or in a process.

• Deining relation

A subset of effects in a fractional factorial design that deine the aliases in the design.

• Discrete random variable

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

• Empirical model

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

• F-test

Any test of signiicance involving the F distribution. The most common F-tests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.

• False alarm

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

• Fractional factorial experiment

A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.

• 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.