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# Solutions for Chapter 13: One-Factor Experiments: General ## Full solutions for Probability and Statistics for Engineers and the Scientists | 9th Edition

ISBN: 9780321629111 Solutions for Chapter 13: One-Factor Experiments: General

Solutions for Chapter 13
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##### ISBN: 9780321629111

Summary of Chapter 13: One-Factor Experiments: General

This material dealing in two-sample inference represents a special case of what we call the one-factor problem.

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 the Scientists, edition: 9. Since 56 problems in chapter 13: One-Factor Experiments: General have been answered, more than 447650 students have viewed full step-by-step solutions from this chapter. Chapter 13: One-Factor Experiments: General includes 56 full step-by-step solutions. Probability and Statistics for Engineers and the Scientists was written by and is associated to the ISBN: 9780321629111.

Key Statistics Terms and definitions covered in this textbook
• 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

• Average

See Arithmetic mean.

• Backward elimination

A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

• Bimodal distribution.

A distribution with two modes

• Bivariate distribution

The joint probability distribution of two random variables.

• Comparative experiment

An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

• Conditional probability density function

The probability density function of the conditional probability distribution of a continuous random variable.

• Conditional variance.

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

• Conidence interval

If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

• Control limits

See Control chart.

• Correlation coeficient

A dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).

• Curvilinear regression

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

• Defects-per-unit control chart

See U chart

• Density function

Another name for a probability density function

• Estimate (or point estimate)

The numerical value of a point estimator.

• Extra sum of squares method

A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

• 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.

• 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 .

• Harmonic mean

The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .