 12.7.1: Test the standard normal pseudorandom number generator on your com...
 12.7.2: Test the gamma pseudorandom number generator on your computer. Sim...
 12.7.3: Test the t pseudorandom number generator on your computer. Simulat...
 12.7.4: Let X and Y be independent random variables with X having the t dis...
 12.7.5: Consider the power calculation done in Example 9.5.5. a. Simulate v...
 12.7.6: The 2 goodnessoffit test (see Chapter 10) is based on an asymptot...
 12.7.7: In Sec. 10.2, we discussed 2 goodnessoffit tests for composite hy...
 12.7.8: In Example 12.5.6, we used a hierarchical model. In that model, the...
 12.7.9: In Example 12.5.6, we modeled the parameters 1,..., p as i.i.d. hav...
 12.7.10: Let X1,...,Xk be independent random variables such that Xi has the ...
 12.7.11: 852 Chapter 12 Simulation c. Use simulation to assess the approxima...
 12.7.12: Consider again the situation described in Exercise 11. This time, u...
 12.7.13: Suppose that our data comprise a set of pairs (Yi, xi), for i = 1,....
 12.7.14: Use the simulation scheme developed in Exercise 13 and the data in ...
 12.7.15: In Sec. 7.4, we introduced Bayes estimators. For simple loss functi...
 12.7.16: In Example 12.5.2, suppose that the State of New Mexico wishes to e...
Solutions for Chapter 12.7: Simulation
Full solutions for Probability and Statistics  4th Edition
ISBN: 9780321500465
Solutions for Chapter 12.7: Simulation
Get Full SolutionsProbability and Statistics was written by and is associated to the ISBN: 9780321500465. Chapter 12.7: Simulation includes 16 full stepbystep solutions. This expansive textbook survival guide covers the following chapters and their solutions. This textbook survival guide was created for the textbook: Probability and Statistics, edition: 4. Since 16 problems in chapter 12.7: Simulation have been answered, more than 15858 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).

Alias
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

Average run length, or ARL
The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.

Bimodal distribution.
A distribution with two modes

Central tendency
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.

Combination.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.

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

Conidence level
Another term for the conidence coeficient.

Continuous distribution
A probability distribution for a continuous random variable.

Contour plot
A twodimensional graphic used for a bivariate probability density function that displays curves for which the probability density function is constant.

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.

Critical region
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

Critical value(s)
The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.

Dispersion
The amount of variability exhibited by data

Expected value
The expected value of a random variable X is its longterm average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.

Experiment
A series of tests in which changes are made to the system under study

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.

Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.