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# Solutions for Chapter 14-3: Control Charts for Attributes

## Full solutions for Elementary Statistics | 12th Edition

ISBN: 9780321836960

Solutions for Chapter 14-3: Control Charts for Attributes

Solutions for Chapter 14-3
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##### ISBN: 9780321836960

Elementary Statistics was written by and is associated to the ISBN: 9780321836960. Since 13 problems in chapter 14-3: Control Charts for Attributes have been answered, more than 191101 students have viewed full step-by-step solutions from this chapter. Chapter 14-3: Control Charts for Attributes includes 13 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Elementary Statistics, edition: 12.

Key Statistics Terms and definitions covered in this textbook
• `-error (or `-risk)

In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).

• Alias

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

• 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

• Bimodal distribution.

A distribution with two modes

• Categorical data

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

• 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.

• Coeficient of determination

See R 2 .

• Combination.

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

• 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 distribution

The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

• Continuity correction.

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

• Correlation

In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

• Cumulative normal distribution function

The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.

• Density function

Another name for a probability density 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.

• Exponential random variable

A series of tests in which changes are made to the system under study

• 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.

• Gamma function

A function used in the probability density function of a gamma random variable that can be considered to extend factorials

• 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

• Hat matrix.

In multiple regression, the matrix H XXX X = ( ) ? ? -1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .

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