- 6.15.4E: It is suspected that using premium gasoline rather than regular wil...
- 6.15.1E: Refer to Exercise 6 in Section 5.9. Let p represent the population ...
- 6.15.2E: Refer to Exercise 6 in Section 5.9. Let ? represent the population ...
- 6.15.3E: In the lettuce yield example presented on page 494, would it be a g...
- 6.15.5E: For the lettuce yield data, (page 494) it is thought that the yield...
- 6.15.6E: Refer to Exercise 6. Perform a randomization test to determine whet...
- 6.15.7E: A certain wastewater treatment method is supposed to neutralize the...
- 6.15.8E: This exercise requires ideas from Section 2.6. In a two- sample exp...
- 6.15.9E: This exercise continues Exercise 9 in the Supplementary Exercises f...
- 6.15.10E: A population geneticist is studying the genes found at two differen...
Solutions for Chapter 6.15: Statistics for Engineers and Scientists 4th Edition
Full solutions for Statistics for Engineers and Scientists | 4th Edition
`-error (or `-risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).
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).
Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
Any test of signiicance based on the chi-square distribution. The most common chi-square 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
Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.
The mean of the conditional probability distribution of a random variable.
The probability of an event given that the random experiment produces an outcome in another event.
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.
A subset of effects in a fractional factorial design that deine the aliases in the design.
The response variable in regression or a designed experiment.
The variance of an error term or component in a model.
A subset of a sample space.
The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.
Any test of signiicance involving the F distribution. The most common F-tests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.
A signal from a control chart when no assignable causes are present
A function used in the probability density function of a gamma random variable that can be considered to extend factorials
Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.