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Response surface methodology (RSM): The collection of the statistical and mathematical methods useful for improving, developing, and optimizing processes with significant applications in the design, development and formulation of the new products, as well as in the improvement of the existing product designs. The extensive applications of such type of methodology are in the industrial world particularly in the situations where many input variables potentially influence some performance measure or the quality characteristic of the product or process. The basic purpose of this methodology is to model response based on the group of the experimental factors, and to determine optimal settings of the experimental factors which maximize or minimize the response. Most of the applications include fitting and checking the adequacy of the models of form. The vector β and matrix B contain parameters of the model.
Complier average causal effect (CACE): The treatment effect amid true compliers in the clinical trial. For the suitable response variable, the CACE is given by the difference in o
Hello-goodbye effect : The phenomenon initially described in psychotherapy research, but one which might arise whenever a subject is assessed on two occasions, with some interventi
An oil company is considering whether or not to bid for an offshore drilling contract. If they bid, the value would be $600m with a 65% chance of gaining the contract. The company
calculate the mean yearly value using the average unemployment rate by month
A mixture of benzene, toluene, and xylene enters a two-stage distillation process where some of the componentsare recovered. The distillation process operates at steady-state condi
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It is the multivariate normal random vector which satisfies certain conditional independence suppositions. This can be viewed as a model framework which contains a wide range of st
Nuisance parameter : The parameter of the model in which there is no scienti?c interest but whose values are generally required (but in usual are unknown) to make inferences about
Principal components analysis is a process for analysing multivariate data which transforms original variables into the new ones which are uncorrelated and account for decreasing
How is the rejection region defined and how is that related to the z-score and the p value? When do you reject or fail to reject the null hypothesis? Why do you think statisticians
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