# Solutions for Chapter 6: Descriptive Statistics

## Full solutions for Probability and Statistics for Engineers and Scientists | 4th Edition

ISBN: 9781111827045

Solutions for Chapter 6: Descriptive Statistics

Solutions for Chapter 6
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##### ISBN: 9781111827045

Since 73 problems in chapter 6: Descriptive Statistics have been answered, more than 5900 students have viewed full step-by-step solutions from this chapter. Chapter 6: Descriptive Statistics includes 73 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: Probability and Statistics for Engineers and Scientists, edition: 4. Probability and Statistics for Engineers and Scientists was written by Patricia and is associated to the ISBN: 9781111827045.

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).

• Bayesâ€™ theorem

An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).

• Biased estimator

Unbiased estimator.

• Bivariate distribution

The joint probability distribution of two random variables.

• Categorical data

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

• Cause-and-effect diagram

A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

• Center line

A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

• Conditional mean

The mean 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.

• Covariance

A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

• Degrees of freedom.

The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

• Demingâ€™s 14 points.

A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

• Density function

Another name for a probability density function

• Designed experiment

An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

• Eficiency

A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

• Error mean square

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

• Estimate (or point estimate)

The numerical value of a point estimator.

• False alarm

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

• 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

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