jmp software module 4 assignment

Module 4 Assignment

In this assignment, you will apply what you’ve learned in this module about the designs of experiments to a sample data set and scenario.

Assignment Instructions

Consider the following: The ABC Company wants to optimize the response (click rate) to their online ads. After a brainstorming session, thirteen factors were identified as potentially having an effect on the response (click) rate. The table below lists the 13 factors and the two levels that should be considered. This data is available in the DOE Assignment JMP file attached below.

Identified Response Factors

	Teaser Offer	Telephone Number	Graphic	Font Size	Advertising Chanel	Message Type	Headline	Layout	Product selection	Gift Offer	Produc Info	Color Schema	Discount	Number of Clicks
Level 1	Yes	Yes	Yes	Large	Yes	A	Headline 1	Standard	Feature A	Yes	Version A	A	Yes
Level 2	No	No	No	Small	No	B	Headline 2	Creative	Feature B	No	Version B	B	No
1	Yes	Yes	Yes	Large	Yes	A	Headline 1	Standard	Feature A	Yes	Version A	A	Yes	52
2	No	Yes	No	Large	Yes	B	Headline 2	Creative	Feature A	Yes	Version B	A	No	38
3	Yes	No	No	Small	Yes	B	Headline 1	Standard	Feature B	Yes	Version B	B	No	42
4	Yes	Yes	Yes	Small	No	B	Headline 1	Creative	Feature A	No	Version B	B	Yes	134
5	No	Yes	Yes	Large	No	B	Headline 1	Creative	Feature B	Yes	Version B	B	Yes	104
6	No	No	No	Large	Yes	A	Headline 1	Creative	Feature A	No	Version A	B	Yes	60
7	No	No	Yes	Small	Yes	A	Headline 2	Creative	Feature B	Yes	Version B	A	Yes	61
8	No	No	Yes	Large	No	B	Headline 2	Standard	Feature B	No	Version A	B	No	68
9	Yes	No	No	Large	Yes	B	Headline 1	Standard	Feature B	No	Version B	A	Yes	57
10	No	Yes	No	Small	Yes	A	Headline 1	Creative	Feature B	No	Version A	B	No	30
11	Yes	No	No	Small	No	B	Headline 2	Creative	Feature A	No	Version A	A	Yes	108
12	No	Yes	No	Small	No	B	Headline 1	Standard	Feature A	Yes	Version A	A	No	39
13	Yes	No	Yes	Small	No	A	Headline 1	Creative	Feature B	Yes	Version A	A	No	40
14	Yes	Yes	No	Large	No	A	Headline 2	Creative	Feature B	No	Version B	A	No	49
15	Yes	Yes	Yes	Small	Yes	A	Headline 2	Standard	Feature A	No	Version B	B	No	37
16	Yes	Yes	No	Large	No	A	Headline 2	Standard	Feature B	Yes	Version A	B	Yes	99
17	No	Yes	Yes	Small	Yes	B	Headline 2	Standard	Feature B	No	Version A	A	Yes	86
18	No	No	Yes	Large	No	A	Headline 1	Standard	Feature A	No	Version B	A	No	43
19	Yes	No	Yes	Large	Yes	B	Headline 2	Creative	Feature A	Yes	Version A	B	No	47
20	No	No	No	Small	No	A	Headline 2	Standard	Feature A	Yes	Version B	B	Yes	104

Discuss how you approach the problems and answer the questions along the way.

1. How many treatments do you need at a minimum to estimate 13 main effects and the overall mean? Find a design using JMP DOE>Classical Designs>Screening Designs add 13 factors and find a design.

• What is the minimum number of treatments (runs)?
• What is the fractional factorial design with the smallest number of runs you can use for our problem?
• Which other design could you choose?

2. Once you have a design matrix you would carry out the treatments and collect the response for each treatment (run). To exercise the analysis of a design I provided you with a 20 run design (which is the Packett-Burman design shown ion your list) which has responses provided in the JMP file. Use the design matrix provided in the DOE_Assignment_5_Click(2).jmp file with the number of clicks as the response variable. Use DOE>Classical>Two Level Screening> Fit Two Level Screening . Which factors are statistically significant at p<=0.05? Highlight the statistically significant factor rows (use individual p) and click run model. Interpret the output.

3. To evaluate the current design matrix with just the few main factors you determined to be significant, use DOE>Design Diagnostics>Evaluate Design. Select only the statistically significant factors for the evaluation. Look at the Alias Matrix to see what the problems are with using the same 20 runs to estimate the interaction effects (confounding of main effects and interaction effects). Specifically, we are interested in the 2-factor interaction between the biggest effects. What main factors are confounded with this interaction and what is the magnitude? Interpret the finding. (Note: find the column of the 2-factor interaction which had the largest effect. Then see what row has a number different from zero and what the main effect in that row is. The larger the absolute value the larger is the confounding. )

4. Now we want to evaluate the data based on our discovery that the 2-factor interaction is confounded with another important factor. Go back to the open window you had before (DOE>Classical>Two Level Screening> Fit Two Level Screening ) and select the statistically significant factors plus the 2-factor interaction of the two biggest effects. Click Run Model again. Interpret the output. What is likely happening?

5. Make a final selection on the window (DOE>Classical>Two Level Screening> Fit Two Level Screening) of what you think is the true likely factors and or interactions. Then click Run Model again. Interpret final model.

Prepare the report using the following formatting guidelines:

• 1 page, single-spaced report using 0.5 margins and two-column format
• 1 page for appendix
• Include title of report, then FirstName, LastName, ISDS course #, Assignment #, date (00/00/00)
• 10 pt Font Calibri or Times New Roman
• Justified as sample report
• Create headings for each section
• List any references used (e.g. Module 1 Resources)
• Include a title for your report e.g. “Text Analysis of Workers Compensation Claims” and create headings for each section
• Include supporting relevant figures from the analysis in your Appendix
• Submit as pdf with filename first name initial last name and assignment number (for instance HSchneider#1)

Be sure to review the Assignment Rubric and Assignment Example attached below. If you have any questions, please post in the Module Questions Forum.