- SE6.SE6.1: [10 10 5] A sample of size n is drawn from a normal population with...
- SE6.SE6.5: [5 5 10] A sample of size n is drawn from a normal population with ...
- SE6.SE6.2: [10 10] A sample of size n is drawn from a normal population with u...
- SE6.SE6.6: [15 15 5] A random sample of size n 10 from a normal population wit...
- SE6.SE6.3: [5 10 10] A (large) random sample of 350 spare parts contains 30 de...
- SE6.SE6.7: [5 10 15] Among 210 randomly selected credit card customers, 142 in...
- SE6.SE6.4: [10 20] Let X1, X2 be the sample means of two independent samples o...
- SE6.SE6.8: [10 5] Two independent samples of sizes n1 and n2 are drawn from no...
Solutions for Chapter SE6: Sample Exams
Full solutions for Probability and Statistics for Engineering and the Sciences (with Student Suite Online) | 7th Edition
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).
See Arithmetic mean.
Central composite design (CCD)
A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level 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 second-order model.
The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.
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
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the in-control 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 in-control, or free from assignable causes. Points beyond the control limits indicate an out-of-control process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.
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).
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.
Another name for a probability density function
The response variable in regression or a designed experiment.
A matrix that provides the tests that are to be conducted in an experiment.
A probability distribution for a discrete random variable
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.
Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.
Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.
The expected value of a random variable X is its long-term 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.
Any test of signiicance involving the F distribution. The most common F-tests are (1) testing hypotheses about the variances or standard deviations of two independent normal distributions, (2) testing hypotheses about treatment means or variance components in the analysis of variance, and (3) testing signiicance of regression or tests on subsets of parameters in a regression model.
Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.
In statistical quality control, that portion of a number of units or the output of a process that is defective.