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# Solutions for Chapter 14.1: Goodness-of-Fit Tests When Category Probabilities Are Completely Specified

## Full solutions for Probability and Statistics for Engineering and the Sciences | 9th Edition

ISBN: 9781305251809

Solutions for Chapter 14.1: Goodness-of-Fit Tests When Category Probabilities Are Completely Specified

Solutions for Chapter 14.1
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##### ISBN: 9781305251809

This expansive textbook survival guide covers the following chapters and their solutions. Chapter 14.1: Goodness-of-Fit Tests When Category Probabilities Are Completely Specified includes 11 full step-by-step solutions. Since 11 problems in chapter 14.1: Goodness-of-Fit Tests When Category Probabilities Are Completely Specified have been answered, more than 82143 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Probability and Statistics for Engineering and the Sciences, edition: 9. Probability and Statistics for Engineering and the Sciences was written by and is associated to the ISBN: 9781305251809.

Key Statistics Terms and definitions covered in this textbook
• 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 control chart

Any control chart for a discrete random variable. See Variables control chart.

• Average

See Arithmetic mean.

• Axioms of probability

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

• Categorical data

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

• Causal variable

When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable

• Conditional probability mass function

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

• Conditional variance.

The variance of the conditional probability distribution of a random variable.

• Continuous uniform random variable

A continuous random variable with range of a inite interval and a constant probability density function.

• 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 .

• Covariance matrix

A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

• Cumulative normal distribution function

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

• Cumulative sum control chart (CUSUM)

A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

• Distribution function

Another name for a cumulative distribution function.

• Experiment

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

• False alarm

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

• First-order model

A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model

• Forward selection

A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.

• Gamma random variable

A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

• Goodness of fit

In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

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