 75.733: PVC pipe is manufactured with a mean diameter of 1.01 inch and a st...
 75.734: Suppose that samples of size n 25 are selected at random from a nor...
 75.735: A synthetic fiber used in manufacturing carpet has tensile strength...
 75.736: . Consider the synthetic fiber in the previous exercise. How is the...
 75.737: 737. The compressive strength of concrete is normally distributed ...
 75.738: Consider the concrete specimens in the previous example. What is th...
 75.739: 739. A normal population has mean 100 and variance 25. How large m...
 75.740: Suppose that the random variable X has the continuous uniform distr...
 75.741: 741. Suppose that X has a discrete uniform distribution A random s...
 75.742: The amount of time that a customer spends waiting at an airport che...
 75.743: 743. A random sample of size n1 16 is selected from a normal popul...
 75.744: A consumer electronics company is comparing the brightness of two d...
 75.745: The elasticity of a polymer is affected by the concentration of a r...
 75.746: Suppose that a random variable is normally distributed with mean an...
 75.747: Transistors have a life that is exponentially distributed with para...
 75.748: Suppose that X is uniformly distributed on the interval from 0 to 1...
 75.749: 749. A procurement specialist has purchased 25 resistors from vend...
 75.750: Consider the resistor problem in Exercise 749. What is the standar...
 75.751: 751. A random sample of 36 observations has been drawn from a norm...
 75.752: Is the assumption of normality important in Exercise 751? Why?
 75.753: 753. A random sample of n 9 structural elements is tested for comp...
 75.754: variance 2 2. A random sample of n 16 is selected from this populat...
 75.755: 755. A random sample of size n 16 is taken from a normal populatio...
 75.756: A manufacturer of semiconductor devices takes a random sample of 10...
 75.757: Let X be a random variable with mean and variance 2 . Given two ind...
 75.758: A random variable x has probability density function Find the maxim...
 75.759: Let Show that is the maximum likelihood estimator for .
 75.760: Let 0 1, and 0 Show that is the maximum likelihood estimator for an...
 75.761: A lot consists of N transistors, and of these M (M N) are defective...
 75.762: When the sample standard deviation is based on a random sample of s...
 75.763: A collection of n randomly selected parts is measured twice by an o...
 75.764: Consistent Estimator. Another way to measure the closeness of an es...
 75.765: Order Statistics. Let X1, X2, , Xn be a random sample of size n fro...
 75.766: Continuation of Exercise 765. Let X1, X2, , Xn be a random sample ...
 75.767: Continuation of Exercise 765. Let X1, X2, , Xn be a random sample ...
 75.768: Continuation of Exercise 765. Let X1, X2, , Xn be a random sample ...
 75.769: Let X1, X2, , Xn be a random sample of a continuous random variable...
 75.770: Let X be a random variable with mean and variance 2 , and let X1, X...
 75.771: When the population has a normal distribution, the estimator is som...
 75.772: Censored Data. A common problem in industry is life testing of comp...
 75.S71: Suppose that X is a normal random variable with unknown mean and kn...
 75.S72: Suppose that X is a normal random variable with unknown mean and kn...
 75.S73: . Let the prior distribution for be a gamma distribution with param...
 75.S74: Suppose that X is a normal random variable with unknown mean and kn...
 75.S75: The weight of boxes of candy is a normal random variable with mean ...
 75.S76: The time between failures of a machine has an exponential distribut...
Solutions for Chapter 75: SAMPLING DISTRIBUTIONS OF MEANS
Full solutions for Applied Statistics and Probability for Engineers  3rd Edition
ISBN: 9780471204541
Solutions for Chapter 75: SAMPLING DISTRIBUTIONS OF MEANS
Get Full SolutionsSince 46 problems in chapter 75: SAMPLING DISTRIBUTIONS OF MEANS have been answered, more than 19336 students have viewed full stepbystep solutions from this chapter. 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. Chapter 75: SAMPLING DISTRIBUTIONS OF MEANS includes 46 full stepbystep solutions. Applied Statistics and Probability for Engineers was written by and is associated to the ISBN: 9780471204541.

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

Analytic study
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study

Assignable cause
The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

Average
See Arithmetic mean.

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

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.

Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.

Coeficient of determination
See R 2 .

Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.

Contingency table.
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria

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

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).

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.

Deming
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.

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

Enumerative study
A study in which a sample from a population is used to make inference to the population. See Analytic study

Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.

Event
A subset of a sample space.

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

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.