 37.371: 371. Suppose the random variable X has a geometric distribution wi...
 37.372: Suppose the random variable X has a geometric distribution with a m...
 37.373: 373. The probability of a successful optical alignment in the asse...
 37.374: In a clinical study, volunteers are tested for a gene that has been...
 37.375: 375. Assume that each of your calls to a popular radio station has...
 37.376: In Exercise 370, recall that a particularly long traffic light on ...
 37.377: 377. A trading company has eight computers that it uses to trade o...
 37.378: In Exercise 366, recall that 20 parts are checked each hour and th...
 37.379: 379. Consider a sequence of independent Bernoulli trials with p 0....
 37.380: Show that the probability density function of a negative binomial r...
 37.381: 381. Suppose that X is a negative binomial random variable with p ...
 37.382: The probability is 0.6 that a calibration of a transducer in an ele...
 37.383: 383. An electronic scale in an automated filling operation stops t...
 37.384: A faulttolerant system that processes transactions for a financial...
 37.385: Derive the expressions for the mean and variance of a geometric ran...
Solutions for Chapter 37: GEOMETRIC AND NEGATIVE BINOMIAL DISTRIBUTIONS
Full solutions for Applied Statistics and Probability for Engineers  3rd Edition
ISBN: 9780471204541
Solutions for Chapter 37: GEOMETRIC AND NEGATIVE BINOMIAL DISTRIBUTIONS
Get Full SolutionsChapter 37: GEOMETRIC AND NEGATIVE BINOMIAL DISTRIBUTIONS includes 15 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. Applied Statistics and Probability for Engineers was written by and is associated to the ISBN: 9780471204541. Since 15 problems in chapter 37: GEOMETRIC AND NEGATIVE BINOMIAL DISTRIBUTIONS have been answered, more than 22689 students have viewed full stepbystep solutions from this chapter.

Bivariate distribution
The joint probability distribution of two random variables.

Bivariate normal distribution
The joint distribution of two normal random variables

Block
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.

Conditional variance.
The variance 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

Conidence level
Another term for the conidence coeficient.

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

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.

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 .

Crossed factors
Another name for factors that are arranged in a factorial experiment.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

Degrees of freedom.
The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

Density function
Another name for a probability density function

Distribution function
Another name for a cumulative distribution function.

Event
A subset of a sample space.

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

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .

Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .