- 13-4.13-25: 13-25. In The Effect of Nozzle Design on the Stability and Performa...
- 13-4.13-26: In Design and Analysis of Experiments, 5th edition (John Wiley & So...
- 13-4.13-27: 13-27. An article in the American Industrial Hygiene Association Jo...
- 13-4.13-28: An article in the Food Technology Journal (Vol. 10, 1956, pp. 3942)...
- 13-4.13-29: 13-29. An experiment was conducted to investigate leaking current i...
- 13-4.13-30: Consider the leakage voltage experiment described in Exercise 13-29...
- 13-4.13-31: 13-31. An article in the IEEE Transactions on Components, Hybrids, ...
- 13-4.13-32: An article in Lubrication Engineering (December 1990) describes the...
- 13-4.13-33: Apply the Fisher LSD method to the experiment in Exercise 13-32. Su...
- 13-4.13-34: An article in the Journal of Quality Technology (Vol. 14, No. 2, 19...
- 13-4.13-35: 13-35. An article in the Journal of Agricultural Engineering Resear...
- 13-4.13-36: An article in Agricultural Engineering (December 1964, pp. 672673) ...
- 13-4.13-37: 13-37. An article in Communications of the ACM (Vol. 30, No. 5, 198...
- 13-4.13-38: Consider an ANOVA situation with a 4 means 1 1, 2 5, 3 8, and 4 4. ...
- 13-4.13-39: 13-39. Consider an ANOVA situation with a 5 treatments. Let 2 9 and...
- 13-4.13-40: Show that in the fixed-effects model analysis of variance E(MSE) 2 ...
- 13-4.13-41: Consider testing the equality of the means of two normal population...
- 13-4.13-42: Consider the ANOVA with a 2 treatments. Show that the MSE in this a...
- 13-4.13-43: Show that the variance of the linear combination
- 13-4.13-44: In a fixed-effects model, suppose that there are n observations for...
- 13-4.13-45: . Consider the single-factor completely randomized design with a tr...
- 13-4.13-46: Consider the single-factor completely randomized design. Show that ...
- 13-4.13-47: Consider the random-effect model for the single-factor completely r...
- 13-4.13-48: Consider a random-effects model for the single-factor completely ra...
- 13-4.13-49: Continuation of Exercise 13-48. Use the results of Exercise 13-48 t...
- 13-4.13-50: Consider the fixed-effect model of the completely randomized single...
- 13-4.13-51: Sample Size Determination. In the singlefactor completely randomize...
Solutions for Chapter 13-4: RANDOMIZED COMPLETE BLOCK DESIGN
Full solutions for Applied Statistics and Probability for Engineers | 3rd Edition
`-error (or `-risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).
Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.
An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.
Bivariate normal distribution
The joint distribution of two normal random variables
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defects-per-unit or U chart.
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.
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
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
Continuous random variable.
A random variable with an interval (either inite or ininite) of real numbers for its range.
Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.
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.
Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.
The response variable in regression or a designed experiment.
A matrix that provides the tests that are to be conducted in an experiment.
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 of estimation
The difference between an estimated value and the true value.
A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.
An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on