 118.1142: Refer to the NFL team performance data in Exercise 114. (a) Calcul...
 118.1143: 1143. Refer to the data in Exercise 115 on house selling price y ...
 118.1144: . Exercise 116 presents data on y steam usage and x average monthl...
 118.1145: Refer to the gasoline mileage data in Exercise 117. (a) What propo...
 118.1146: . Consider the data in Exercise 118 on y green liquor Na2S concent...
 118.1147: 1147. Refer to Exercise 119, which presented data on blood pressu...
 118.1148: Exercise 1110 presents data on wear volume y and oil viscosity x. ...
 118.1149: 1149. Refer to Exercise 1111, which presented data on chloride co...
 118.1150: Consider the rocket propellant data in Exercise 1112. (a) Calculat...
 118.1151: Show that an equivalent way to define the test for significance of ...
 118.1152: Suppose that a simple linear regression model has been fit to n 25 ...
 118.1153: . Consider the rocket propellant data in Exercise 11 12. Calculate...
 118.1154: Studentized Residuals. Show that the variance of the ith residual i...
Solutions for Chapter 118: ADEQUACY OF THE REGRESSION MODEL
Full solutions for Applied Statistics and Probability for Engineers  3rd Edition
ISBN: 9780471204541
Solutions for Chapter 118: ADEQUACY OF THE REGRESSION MODEL
Get Full SolutionsApplied Statistics and Probability for Engineers was written by and is associated to the ISBN: 9780471204541. Chapter 118: ADEQUACY OF THE REGRESSION MODEL includes 13 full stepbystep solutions. This textbook survival guide was created for the textbook: Applied Statistics and Probability for Engineers , edition: 3. This expansive textbook survival guide covers the following chapters and their solutions. Since 13 problems in chapter 118: ADEQUACY OF THE REGRESSION MODEL have been answered, more than 22595 students have viewed full stepbystep solutions from this chapter.

aerror (or arisk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).

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

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

Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

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.

Chisquare (or chisquared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

Conditional mean
The mean 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

Counting techniques
Formulas used to determine the number of elements in sample spaces and events.

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 .

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 offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

Defect
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

Deming’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

Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

Error propagation
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.

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.

Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.