- Chapter 20.1: The following payoff table was developed. Let P(S1) .30, P(S2) .50,...
- Chapter 20.2: Wilhelms Cola Company plans to market a new lime-flavored cola this...
- Chapter 20.3: Refer to Exercise 1. Develop an opportunity loss table. Determine t...
- Chapter 20.4: Refer to Exercise 2, involving Wilhelms Cola Company. Develop an op...
- Chapter 20.5: Refer to Exercises 1 and 3. Compute the expected opportunity losses
- Chapter 20.6: Refer to Exercises 2 and 4. Compute the expected opportunity losses.
- Chapter 20.7: Refer to Exercises 1, 3, and 5. Compute the expected value of perfe...
- Chapter 20.8: Refer to Exercises 2, 4, and 6. Compute the expected value of perfe...
- Chapter 20.9: Refer to Exercise 1. Revise the probabilities as follows: P(S1) .50...
- Chapter 20.10: Refer to Exercise 2. Reverse the probabilities; that is, let P(S1) ...
- Chapter 20.11: Blackbeards Phantom Fireworks is considering introducing two new bo...
- Chapter 20.12: A financial executive for Fidelity Investments lives in Boston but ...
- Chapter 20.13: Thomas Manufacturing Company has $100,000 available to invest. John...
- Chapter 20.14: The quality assurance department at Malcomb Products must either in...
- Chapter 20.15: Dude Ranches Incorporated was founded on the idea that many familie...
- Chapter 20.16: The combined experience at several other lodges was found to be: Nu...
- Chapter 20.17: Casual Furniture World has had numerous inquiries regarding the ava...
- Chapter 20.18: Tim Waltzer owns and operates Waltzers Wrecks, a discount car renta...
- Chapter 20.19: You sign up for a cell phone plan and are presented with this chart...
- Chapter 20.20: Youre about to drive to New York. If your cars engine is out of tun...
Solutions for Chapter Chapter 20: An Introduction to Decision Theory
Full solutions for Statistical Techniques in Business and Economics | 15th Edition
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
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
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.
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.
The joint probability distribution of two random variables.
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.
Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).
An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.
The variance of the conditional probability distribution of a random variable.
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 square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.
Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.
Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.
Defects-per-unit control chart
See U chart
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.
A subset of effects in a fractional factorial design that deine the aliases in the design.
Error of estimation
The difference between an estimated value and the true value.
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.
Fisher’s least signiicant difference (LSD) method
A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.
In statistical quality control, that portion of a number of units or the output of a process that is defective.