 8.2.15: Let m denote the true average reaction time to a certainstimulus. F...
 8.2.16: Newly purchased tires of a particular type are supposedto be filled...
 8.2.17: Answer the following questions for the tire problem inExample 8.7.a...
 8.2.18: Reconsider the paintdrying situation of Example 8.5, inwhich dryin...
 8.2.19: The melting point of each of 16 samples of a certainbrand of hydrog...
 8.2.20: Lightbulbs of a certain type are advertised as having anaverage lif...
 8.2.21: The desired percentage of SiO2 in a certain type of aluminouscement...
 8.2.22: To obtain information on the corrosionresistance propertiesof a ce...
 8.2.23: Automatic identification of the boundaries of significantstructures...
 8.2.24: Unlike most packaged food products, alcohol beveragecontainer label...
 8.2.25: Body armor provides critical protection for lawenforcement personne...
 8.2.26: The recommended daily dietary allowance for zincamong males older t...
 8.2.27: Show that for any D . 0, when the population distributionis normal ...
 8.2.28: For a fixed alternative value m9, show that b(m9) S 0as n S ` for e...
Solutions for Chapter 8.2: z Tests for Hypotheses about a Population Mean
Full solutions for Probability and Statistics for Engineering and the Sciences  9th Edition
ISBN: 9781305251809
Solutions for Chapter 8.2: z Tests for Hypotheses about a Population Mean
Get Full SolutionsThis textbook survival guide was created for the textbook: Probability and Statistics for Engineering and the Sciences, edition: 9. Since 14 problems in chapter 8.2: z Tests for Hypotheses about a Population Mean have been answered, more than 80538 students have viewed full stepbystep solutions from this chapter. Chapter 8.2: z Tests for Hypotheses about a Population Mean includes 14 full stepbystep solutions. This expansive textbook survival guide covers the following chapters and their solutions. Probability and Statistics for Engineering and the Sciences was written by and is associated to the ISBN: 9781305251809.

2 k p  factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each

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).

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

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.

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.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

Conditional probability
The probability of an event given that the random experiment produces an outcome in another event.

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

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.

Decision interval
A parameter in a tabular CUSUM algorithm that is determined from a tradeoff between false alarms and the detection of assignable causes.

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.

Deining relation
A subset of effects in a fractional factorial design that deine the aliases in the design.

Discrete distribution
A probability distribution for a discrete random variable

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.

Error mean square
The error sum of squares divided by its number of degrees of freedom.

Error variance
The variance of an error term or component in a model.

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.

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

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

Generating function
A function that is used to determine properties of the probability distribution of a random variable. See Momentgenerating function