 13.131: Consider the computer output below. Oneway ANOVA: y versus Factor ...
 13.132: An article in Nature describes an experiment to investigate the eff...
 13.133: In Design and Analysis of Experiments, 7th edition (John Wiley & So...
 13.134: In Orthogonal Design for Process Optimization and Its Application t...
 13.135: The compressive strength of concrete is being studied, and four dif...
 13.136: The response time in milliseconds was determined for three differen...
 13.137: An electronics engineer is interested in the effect on tube conduct...
 13.138: An article in Environment International (Vol. 18, No. 4, 1992) desc...
 13.139: An article in the ACI Materials Journal (Vol. 84, 1987, pp. 213216)...
 13.1310: An article in the Materials Research Bulletin (Vol. 26, No. 11, 199...
 13.1311: A paper in the Journal of the Association of Asphalt Paving Technol...
 13.1312: An article in Quality Engineering [Estimating Sources of Variation:...
 13.1313: In the book Analysis of Longitudinal Data, 2nd ed., (2002, Oxford U...
 13.1314: An article in Journal of Food Science (2001, Vol. 66, No. 3, pp. 47...
 13.1315: An experiment was run to determine whether four specific firing tem...
 13.1316: (a) Use Fishers LSD method with 0.05 to analyze the means of the th...
 13.1317: (a) Use Fishers LSD method with 0.05 to analyze the means of the fi...
 13.1318: (a) Use Fishers LSD method with 0.01 to analyze the mean response t...
 13.1319: (a) Use Fishers LSD method with 0.05 to analyze the mean compressiv...
 13.1320: (a) Use Fishers LSD method with to analyze the mean amounts of rado...
 13.1321: (a) Use Fishers LSD method with 0.01 to analyze the five means for ...
 13.1322: (a) Apply Fishers LSD method with 0.05 to the superconducting mater...
 13.1323: (a) Apply Fishers LSD method to the air void experiment described i...
 13.1324: (a) Apply Fishers LSD method to the domain spacing data in Exercise...
 13.1325: (a) Apply Fishers LSD method to the data on protein content of milk...
 13.1326: Suppose that four normal populations have common variance 2 25 and ...
 13.1327: Suppose that five normal populations have common variance 2 100 and...
 13.1328: An article in the Journal of the Electrochemical Society (Vol. 139,...
 13.1329: A textile mill has a large number of looms. Each loom is supposed t...
 13.1330: In the book Bayesian Inference in Statistical Analysis (1973, John ...
 13.1331: An article in the Journal of Quality Technology (Vol. 13, No. 2, 19...
 13.1332: Consider the vapordeposition experiment described in Exercise 132...
 13.1333: Reconsider Exercise 1313 in which the effect of different diets on...
 13.1334: Consider the computer output below. Randomized Block ANOVA: y versu...
 13.1335: Exercise 132 introduced you to an experiment to investigate the po...
 13.1336: An article in Quality Engineering [Designed Experiment to Stabilize...
 13.1337: In The Effect of Nozzle Design on the Stability and Performance of ...
 13.1338: In Design and Analysis of Experiments, 7th edition (John Wiley & So...
 13.1339: An article in the American Industrial Hygiene Association Journal (...
 13.1340: An article in the Food Technology Journal (Vol. 10, 1956, pp. 3942)...
 13.1341: An experiment was conducted to investigate leaking current in a SOS...
 13.1342: Consider the computer output below. Oneway ANOVA: y versus Factor ...
 13.1343: Consider the computer output below. Randomized Block ANOVA: y versu...
 13.1344: An article in Lubrication Engineering (December 1990) described the...
 13.1345: An article in the IEEE Transactions on Components, Hybrids, and Man...
 13.1346: An article in the Journal of Quality Technology (Vol. 14, No. 2, 19...
 13.1347: An article in the Journal of Agricultural Engineering Research (Vol...
 13.1348: An article in Agricultural Engineering (December 1964, pp. 672673) ...
 13.1349: An article in Communications of the ACM (Vol. 30, No. 5, 1987) stud...
 13.1350: An article in Nature Genetics (2003, Vol. 34(1), pp. 8590) Treatmen...
 13.1351: Consider an ANOVA situation with treatments. Let 2 9 and 0.05, and ...
 13.1352: Consider an ANOVA situation with a 4 means 1 1, 2 5, 3 8, and 4 4. ...
 13.1353: Show that in the fixedeffects model analysis of variance E(MSE) 2 ...
 13.1354: Consider testing the equality of the means of two normal population...
 13.1355: Consider the ANOVA with a 2 treatments. Show that the MSE in this a...
 13.1356: Show that the variance of the linear combination
 13.1357: In a fixedeffects model, suppose that there are n observations for...
 13.1358: Consider the singlefactor completely randomized design with a trea...
 13.1359: Consider the singlefactor completely randomized design. Show that ...
 13.1360: Consider the randomeffect model for the singlefactor completely r...
 13.1361: Consider a randomeffects model for the singlefactor completely ra...
 13.1362: Consider the fixedeffect model of the completely randomized single...
 13.1363: Sample Size Determination. In the singlefactor completely randomize...
Solutions for Chapter 13: Design and Analysis of SingleFactor Experiments: The Analysis of Variance
Full solutions for Applied Statistics and Probability for Engineers  5th Edition
ISBN: 9780470053041
Solutions for Chapter 13: Design and Analysis of SingleFactor Experiments: The Analysis of Variance
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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

Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.

Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

Causeandeffect diagram
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.

Conidence level
Another term for the conidence coeficient.

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

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

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Discrete distribution
A probability distribution for a discrete random variable

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).

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

Estimate (or point estimate)
The numerical value of a point estimator.

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.

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model

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.

Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

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 .