 7.71: Consider the hospital emergency room data from Exercise 6104. Esti...
 7.72: Consider the compressive strength data in Table 62. What proportio...
 7.73: PVC pipe is manufactured with a mean diameter of 1.01 inch and a st...
 7.74: Suppose that samples of size n 25 are selected at random from a nor...
 7.75: A synthetic fiber used in manufacturing carpet has tensile strength...
 7.76: Consider the synthetic fiber in the previous exercise. How is the s...
 7.77: The compressive strength of concrete is normally distributed with 2...
 7.78: Consider the concrete specimens in the previous exercise. What is t...
 7.79: A normal population has mean 100 and variance 25. How large must th...
 7.710: Suppose that the random variable X has the continuous uniform distr...
 7.711: Suppose that X has a discrete uniform distribution A random sample ...
 7.712: The amount of time that a customer spends waiting at an airport che...
 7.713: A random sample of size n1 16 is selected from a normal population ...
 7.714: A consumer electronics company is comparing the brightness of two d...
 7.715: The elasticity of a polymer is affected by the concentration of a r...
 7.716: A computer software package was used to calculate some numerical su...
 7.717: A computer software package was used to calculate some numerical su...
 7.718: Let X1 and X2 be independent random variables with mean and varianc...
 7.719: Suppose that we have a random sample X1, X2, . . . , Xn from a popu...
 7.720: Suppose we have a random sample of size 2n from a population denote...
 7.721: Let denote a random sample from a population having mean and varian...
 7.722: Suppose that and are unbiased estimators of the parameter . We know...
 7.723: Suppose that and are estimators of the parameter . We know that . W...
 7.724: Suppose that , , and are estimators of . We know that , and . Compa...
 7.725: Let three random samples of sizes n1 20, n2 10, and n3 8 be taken f...
 7.726: (a) Show that is a biased estimator of . (b) Find the amount of bia...
 7.727: Let be a random sample of size n from a population with mean and va...
 7.728: Data on pulloff force (pounds) for connectors used in an automobil...
 7.729: Data on oxide thickness of semiconductors are as follows: 425, 431,...
 7.730: Suppose that X is the number of observed successes in a sample of n...
 7.731: and are the sample mean and sample variance from a population with ...
 7.732: Two different plasma etchers in a semiconductor factory have the sa...
 7.733: Of randomly selected engineering students at ASU, owned an HP calcu...
 7.734: Let X be a geometric random variable with parameter p. Find the max...
 7.735: Consider the Poisson distribution with parameter Find the maximum l...
 7.736: Let X be a random variable with the following probability distribut...
 7.737: Consider the shifted exponential distribution When 0, this density ...
 7.738: Consider the probability density function Find the maximum likeliho...
 7.739: Let X1, X2, , Xn be uniformly distributed on the interval 0 to a. S...
 7.740: Consider the probability density function (a) Find the value of the...
 7.741: The Rayleigh distribution has probability density function f 1x2 x ...
 7.742: Let X1, X2, , Xn be uniformly distributed on the interval 0 to a. R...
 7.743: Consider the Weibull distribution (a) Find the likelihood function ...
 7.744: Reconsider the oxide thickness data in Exercise 729 and suppose th...
 7.745: Suppose that X is a normal random variable with unknown mean and kn...
 7.746: Suppose that X is a normal random variable with unknown mean and kn...
 7.747: Suppose that X is a Poisson random variable with parameter . Let th...
 7.748: Suppose that X is a normal random variable with unknown mean and kn...
 7.749: The weight of boxes of candy is a normal random variable with mean ...
 7.750: The time between failures of a machine has an exponential distribut...
 7.751: Transistors have a life that is exponentially distributed with para...
 7.752: Suppose that a random variable is normally distributed with mean an...
 7.753: Suppose that X is uniformly distributed on the interval from 0 to 1...
 7.754: A procurement specialist has purchased 25 resistors from vendor 1 a...
 7.755: A random sample of 36 observations has been drawn from a normal dis...
 7.756: A random sample of n 9 structural elements is tested for compressiv...
 7.757: A normal population has a known mean 50 and known variance 2 2. A r...
 7.758: A random sample of size n 16 is taken from a normal population with...
 7.759: A manufacturer of semiconductor devices takes a random sample of 10...
 7.760: Let X be a random variable with mean and variance 2 . Given two ind...
 7.761: A random variable x has probability density function Find the maxim...
 7.762: Let Show that is the maximum likelihood estimator for
 7.763: Let 0 1, and 0 Show that is the maximum likelihood estimator for an...
 7.764: You plan to use a rod to lay out a square, each side of which is th...
 7.765: An electric utility has placed special meters on 10 houses in a sub...
 7.766: A lot consists of N transistors, and of these, M (M N) are defectiv...
 7.767: When the sample standard deviation is based on a random sample of s...
 7.768: A collection of n randomly selected parts is measured twice by an o...
 7.769: Consistent Estimator. Another way to measure the closeness of an es...
 7.770: Order Statistics. Let X1, X2, , Xn be a random sample of size n fro...
 7.771: Let X1, X2, , Xn be a random sample of a continuous random variable...
 7.772: Let X be a random variable with mean and variance 2 , and let X1, X...
 7.773: When the population has a normal distribution, the estimator is som...
 7.774: Censored Data. A common problem in industry is life testing of comp...
Solutions for Chapter 7: Sampling Distributions and Point Estimation of Parameters
Full solutions for Applied Statistics and Probability for Engineers  5th Edition
ISBN: 9780470053041
Solutions for Chapter 7: Sampling Distributions and Point Estimation of Parameters
Get Full SolutionsThis textbook survival guide was created for the textbook: Applied Statistics and Probability for Engineers, edition: 5. Chapter 7: Sampling Distributions and Point Estimation of Parameters includes 74 full stepbystep solutions. Applied Statistics and Probability for Engineers was written by and is associated to the ISBN: 9780470053041. Since 74 problems in chapter 7: Sampling Distributions and Point Estimation of Parameters have been answered, more than 22483 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions.

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).

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

Backward elimination
A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).

Conditional mean
The mean 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.

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.

Convolution
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.

Correction factor
A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

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).

Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.

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

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

Discrete random variable
A random variable with a inite (or countably ininite) range.

Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

Gaussian distribution
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications