- 14-4.14-1: In his book (Design and Analysis of Experiments, 5th edition, 2001 ...
- 14-4.14-2: An engineer who suspects that the surface finish of metal parts is ...
- 14-4.14-3: 14-3. An article in Industrial Quality Control (1956, pp. 58) descr...
- 14-4.14-4: An experiment was conducted to determine whether either firing temp...
- 14-4.14-5: Continuation of Exercise 14-4. Using Fishers LSD method, investigat...
- 14-4.14-6: Johnson and Leone (Statistics and Experimental Design in Engineerin...
- 14-4.14-7: 14-7. Consider a two-factor factorial experiment. Develop a formula...
- 14-4.14-8: An article in the Journal of Testing and Evaluation (Vol. 16, no. 6...
- 14-4.14-9: 14-9. An article in the IEEE Transactions on Electron Devices (Nove...
- 14-4.14-10: Consider the experiment described in Exercise 14-9. Use Fishers LSD...
Solutions for Chapter 14-4: TWO-FACTOR FACTORIAL EXPERIMENTS
Full solutions for Applied Statistics and Probability for Engineers | 3rd Edition
2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.
All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions
In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test
Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.
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
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.
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.
Any test of signiicance based on the chi-square distribution. The most common chi-square 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
Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.
The variance of the conditional probability distribution of a random variable.
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
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.
Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.
The response variable in regression or a designed experiment.
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).
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.
The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.
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.