 Chapter 10: Statistical Inference for Two Samples
 Chapter 11: Simple Linear Regression and Correlation
 Chapter 12: Multiple Linear Regression
 Chapter 13: Design and Analysis of SingleFactor Experiments: The Analysis of Variance
 Chapter 14: Design of Experiments with Several Factors
 Chapter 15: Statistical Quality Control
 Chapter 2: Probability
 Chapter 3: Discrete Random Variables and Probability Distributions
 Chapter 4: Continuous Random Variables and Probability Distributions
 Chapter 5: Joint Probability Distributions
 Chapter 6: Descriptive Statistics
 Chapter 7: Sampling Distributions and Point Estimation of Parameters
 Chapter 8: Statistical Intervals for a Single Sample
 Chapter 9: Tests of Hypotheses for a Single Sample
Applied Statistics and Probability for Engineers 5th Edition  Solutions by Chapter
Full solutions for Applied Statistics and Probability for Engineers  5th Edition
ISBN: 9780470053041
Applied Statistics and Probability for Engineers  5th Edition  Solutions by Chapter
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2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

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.

Average
See Arithmetic mean.

Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

Conditional mean
The mean of the conditional probability distribution of a random variable.

Conditional probability
The probability of an event given that the random experiment produces an outcome in another event.

Correction factor
A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

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).

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 .

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

Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass 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.

Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

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

Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.

Fisher’s least signiicant difference (LSD) method
A series of pairwise hypothesis tests of treatment means in an experiment to determine which means differ.

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

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 .