 6.15.1: Refer to Exercise 6 in Section 5.10. Let represent the population m...
 6.15.2: Refer to Exercise 6 in Section 5.10. Let represent the population m...
 6.15.3: In the lettuce yield example presented on page 499, would it be a g...
 6.15.4: It is suspected that using premium gasoline rather than regular wil...
 6.15.5: For the lettuce yield data (page 499), it is thought that the yield...
 6.15.6: Refer to Exercise 4. Perform a randomization test to determine whet...
 6.15.7: A certain wastewater treatment method is supposed to neutralize the...
 6.15.8: This exercise requires ideas from Section 2.6. In a twosample expe...
 6.15.9: This exercise continues Exercise 9 in the Supplementary Exercises f...
 6.15.10: A population geneticist is studying the genes found at two differen...
 6.15.11: Hypothesis Testing
Solutions for Chapter 6.15: Using Simulation to Perform Hypothesis Tests
Full solutions for Statistics for Engineers and Scientists  4th Edition
ISBN: 9780073401331
Solutions for Chapter 6.15: Using Simulation to Perform Hypothesis Tests
Get Full SolutionsStatistics for Engineers and Scientists was written by and is associated to the ISBN: 9780073401331. Chapter 6.15: Using Simulation to Perform Hypothesis Tests includes 11 full stepbystep solutions. Since 11 problems in chapter 6.15: Using Simulation to Perform Hypothesis Tests have been answered, more than 238567 students have viewed full stepbystep solutions from this chapter. This textbook survival guide was created for the textbook: Statistics for Engineers and Scientists , edition: 4. This expansive textbook survival guide covers the following chapters and their solutions.

Acceptance region
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

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

Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

Bias
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.

Bivariate distribution
The joint probability distribution of two random variables.

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.

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.

Conidence coeficient
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

Consistent estimator
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.

Continuous distribution
A probability distribution for a continuous random variable.

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

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).

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.

Design matrix
A matrix that provides the tests that are to be conducted in an experiment.

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 variance
The variance of an error term or component in a model.

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

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