Robust Parameter Design ( Rpd )

These three drivers had the best R square values and that is why I chose them. This method is a considerable upgrade from all of the other methods and gives us the best available outcome. This outcome takes multiple variables and combines them into one equation. The multiple regressions R square(.8615) is higher than the single regression so there is some conflict with the method.

results from the model were both accurate and reliable. The multiple R and the R

Referring to Figure 6.2.3 and 6.3.3, it was proven that our model has problems that the sample data used does not represent the whole population. Therefore, this is one of the flaws in our research. A more constructive suggestion to eliminate this problem would be to extend the research with a larger sample size with longer time horizon. And if the sample size is large enough, the time series issue can be neglected.

In the appendix section, we display all betas for respondent 1 and 5. The results vary considerably depending on the beta and the respondent. This pattern and behavior of the model suggest that 100,000 iterations could not be sufficient to have a robust model.

a. Using solver to solve the embedded model in the Excel sheet or by trying different values for h the optimum value will be obtained as “h=4”

This paper comprises an appreciation of data representation, its visualization, an outline description of behavior, plus an indication of the use of the equation in engineering.

Problem 2.4: Consider the sample from a bank database shown in Figure 2.14; it was selected randomly from a larger database to be the training set. Personal Loan indicates whether a solicitation for a personal loan was accepted and is the response variable. A campaign is planned for a similar solicitation in the future, and the bank is looking for a model that will identify likely responders. Examine the data carefully and indicate what your next step would be.

In order for me to find the appropriate model I need to evaluate them by using ln(Y) and Ŷ calculations. I analyzed the Linear: Ŷ= 10.4446 + .755 R2= .9719 , squared: Ŷ= 11.31 + .5951(x1) + .0062(x12) R2= .9745 , cubic: Ŷ= 12.0508 + .3627 (x1) + .0264(x12) - .00051 (x13) R2= .9751 , log log: ln(Y)= exp(2.0403 + ln(x1) + (.0597/2)^2 R2=.9072 , logarithmic: Ŷ = 2.1503 + ln(x1) R2= .8523 , and exponential: ln(Y) = exp(2.4924 + .0383(x1) + (.0375/2)^2 R2= .9634. After analyzing the R2’s I narrowed it down to Cubic. Cubic has the highest R2 and is the most promising of the models. Based on my results from (b) and (c) also regards to how I achieved Mileage Cubic to be the best

14. Is this multiple regression model better than the linear model that we generated in parts 1-10? Explain. No the linear regression has a p-value less than alpha so we reject Ho and concluded that

* Test the utility of this regression model (use a two tail test with α =.05).

Quality Associates, Inc., a consulting firm advises its clients about sampling and statistical procedures that can be used to control their manufacturing processes. In one particular application a client gave Quality Associates a sample of 800 observations taken during a time in which the client’s process was operating satisfactorily. The sample standard deviation of this data was 0.21; hence with so much data, the population standard deviation was assumed to be 0.21. Quality Associates then suggested that random samples of size 30 be taken periodically to monitor the process on an ongoing basis. By analyzing the new samples, the client could quickly learn whether the process was operating satisfactorily. When the process was not operating satisfactorily, corrective action could be taken to eliminate the problem. The design specification indicated the mean for the process should be 12. The hypothesis test suggested by Quality Associates follows.

Willowbrook School, a 20 year-old nonprofit educational institution, is committed to providing a high quality, cost effective, safe and culturally progressive education to families with children ranging from preschool age through grade six.

* Explain your choices and elaborate on how the models can best be applied to the selected problems.

The procedure for this model is to collect several periods of history relating to the independent and dependent variables themselves, establish the relationship that minimizes mean squared error of forecast vs actual using linear or non-linear and singular or multiple regression analysis.

The statistical process control system involving both acceptance sampling and automated process control was to be implemented. SPC involved testing for productions within a pre-specified range. If the production went beyond the range, the production process had to be shut down maintenance was to be called to perform maintenance and recalibration. As a part of the process, the operators were to take six random measurements of a process characteristic during the course of their shift and then plot the mean measured value.