 7.3.1: Customers arrive at a postoffice at a Poisson rate of three per min...
 7.3.2: Find the median of an exponential random variable with rate . Recal...
 7.3.3: Let X be an exponential random variable with mean 1. Find the proba...
 7.3.4: The time between the first and second heart attacks for a certain g...
 7.3.5: Guests arrive at a hotel, in accordance with a Poisson process, at ...
 7.3.6: Let X be an exponential random variable with parameter . Find P !D ...
 7.3.7: Suppose that, at an Italian restaurant, the time, in minutes, betwe...
 7.3.8: Suppose that the time it takes for a novice secretary to type a doc...
 7.3.9: The profit is $350 for each computer assembled by a certain person....
 7.3.10: Mr. Jones is waiting to make a phone call at a train station. There...
 7.3.11: In a factory, a certain machine operates for a period which is expo...
 7.3.12: In data communication, messages are usually combinations of charact...
 7.3.13: The random variable X is called double exponentially distributed if...
 7.3.14: Let X, the lifetime (in years) of a radio tube, be exponentially di...
 7.3.15: Prove that if X is a positive, continuous, memoryless random variab...
Solutions for Chapter 7.3: Exponential Random Variables
Full solutions for Fundamentals of Probability, with Stochastic Processes  3rd Edition
ISBN: 9780131453401
Solutions for Chapter 7.3: Exponential Random Variables
Get Full SolutionsThis expansive textbook survival guide covers the following chapters and their solutions. Since 15 problems in chapter 7.3: Exponential Random Variables have been answered, more than 13173 students have viewed full stepbystep solutions from this chapter. This textbook survival guide was created for the textbook: Fundamentals of Probability, with Stochastic Processes, edition: 3. Fundamentals of Probability, with Stochastic Processes was written by and is associated to the ISBN: 9780131453401. Chapter 7.3: Exponential Random Variables includes 15 full stepbystep solutions.

`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).

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

Average
See Arithmetic mean.

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.

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.

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

Continuity correction.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

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
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.

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.

Density function
Another name for a probability density function

Dependent variable
The response variable in regression or a designed experiment.

Dispersion
The amount of variability exhibited by data

Eficiency
A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

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

Factorial experiment
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

False alarm
A signal from a control chart when no assignable causes are present

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

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