BADM 7020 LSU Mod 4 ABC Company Factors & Parameter Estimates for 4 Biggest Factors Paper 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 pDesign 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. Name
Assignment 4 Example
Introduction and Business Understanding
A credit card company sent offers to potential customers
as a way to increase revenue with a goal of optimizing
the customer response rate. Although they identified
many variables, one challenge was understanding which
were the most important to optimizing the response rate.
To support this optimization, Design of Experiment
(DOE) was used to determine which factors were
important.
Data Understanding: Nineteen two-level factors were
identified by the credit card company as possibly having
an impact on customer Response Rate, which was the
continuous response variable. The full list is in Appendix
A. All two-level factors in the file were treated as nominal
predictor variables.
Experimental Designs: Several DOE designs were
considered using all 19 factors with the Screening Designs
platform under Classical Designs option. A full factorial
design for the 19 factors, would require 219 runs. This was
not practical for an experiment and therefore not evaluated.
JMP provided multiple options to reduce the design to a
fractional factorial, including minimum runs, default runs,
and user selection. The minimum represented the number
of factors plus one, which was 20. The default provided a
balanced design with more than the minimum, but less than
the full factorial. The default design was 24 runs. Both the
20 and 24 run options were evaluated in the Distribution
platform and it was determined that a 20 runs would be less
costly and also provided a balanced design.
Identifying Significant Factors: After selecting a design
with 20 runs, the data file was used to identify the
statistically significant factors at a significance level of p=
0.05. Fit Two Level Screening under the DOE platform
was used to analyze the design. Three factors (interest
rate, sticker and SECOND_BUCKSLIP) were statistically
significant at 0.05, but COPY_MESSAGE was borderline
statistically significant (p=0.0512). The remaining 15
factors had p-values greater than 0.16.
Next the four biggest effects were selected and the model
run (see Appendix C). All four factors are statistically
significant at PClassical>Two Level Screening> Fit Two Level Screening
Select all factors as X variable and Number of Clicks as Y variable. (Note: you do not include Treatment,
which is not a factor!) Click OK.
Select (highlight) all the statistically significant factors and click Run Model. Interpret the
output.
Step 3: Use DOE>Design Diagnostics>Evaluate Design to evaluate the design.
The rule of thumb in experimental designs is that if the main effect is not large then the
interactions are not large either. Therefore select only statistically significant Factors into X and
Number of Clicks into Y.
You get a lengthy output but we are only interested in the Alias Matrix.
The Alias Matrix you will see has your main effects in the first column and the interactions in
the first row. The matrix entries tell you about the magnitude of confounding. The large the
absolute value the larger the confounding between the main effect and the interaction effect.
Ignore the intercept. For instance if your first main effect listed is advertising Chanel and the
first interaction between Advertising Chanel and Message Type is zero as shown in the figure
above then there is no confounding between these two. If it were greater than zero or smaller
than zero than there is confounding.
Following the rule of thumb again we are likely to have an interaction between two factors
important when their main effects are large. Therefore look specifically at the interaction of the
two largest main effects and which main factor this interaction is confounded with. This is
where your main confounding is.
Step 4: Run DOE>Classical>Two Level Screening> Fit Two Level Screening) again with adding the
interaction you identified in Step 3
You have to highlight this interaction in addition to your statistically significant main effects.
Interpret the output. What could likely be happening?
Step 5: Make a final selection of main effects you think are truly different from zero with the
appropriate interaction and run DOE>Classical>Two Level Screening> Fit Two Level
Screening again.
The interaction plots can be obtain the Factor Profiling> Interaction Plot. They look a little
different but provide the same information as the one shown in the sample assignment.
Interpret the output.
Write your report using the sample report as guide.
Introduction & Business
Understanding
Data Understanding
Analysis Design
Analysis Factors
Confounding
Design Follow-Up
Conclusion
Format and Overall
Writing.
Provides background on
the business problem.
Explains clearly the issue
of issues of DOEs.
10points
Does demonstrate some
understanding of the
business issues. But
explanation of purpose
of DOE vague or
unclear.
7points
Describes the general
business issue at hand.
Lacking specificity or
clarity about DOE.
5points
No business
Understanding.
0points
Demonstrates superior
data understanding as it
relates to the specific
model.
10points
Demonstrates
understanding of data
issues but misses some
important details.
7points
Data understanding not
sufficiently described.
Major details are
missing.
5points
No data understanding
not sufficient.
0points
Selects correct design.
10points
Selects adequate
design. But design not
optimal.
7points
Design not adequate.
5points
No design.
0points
Identifies significant
factors.
10points
Identifies significant
factors, but list is
missing a variable.
7points
Analysis not complete.
5points
Very poor analysis.
0points
Identifies confounding
and explains it correctly.
10points
Identifies confounding
but explanation is
incomplete or incorrect.
7points
Confounding not
explained adequately.
10points
5points
No explanation of
confounding.
0points
Creates correct followup fractional factorial
design with minimum
runs necessary.
10points
Follow up design is not
optimal.
7points
Follow up design is
insufficient.
5points
Follow up design
missing.
0points
Conclusion clearly
describes the findings
and identifies the
strength and weakness
of the designs used.
20points
Conclusion describes
findings, but does not
include a clear
description of strength
and weakness of
designs.
17points
Conclusion is too vague
and general.
15points
Conclusion missing.
0points
Follows format. Text has
superior layout with
headings. Writing is
style specific and
detailed and
professional.
Follows format, and
average writing style
with some minor issues.
7points
Does not follow
required format or
writing style is not
adequate for a
professional report.
5points
Not formatted writing
style unprofessional.
0points
10points
Appendix
Appendix has
meaningful charts,
numbered and titles.
Has all charts required
for understanding.
10points
Has appendix but some
tables/graphs are
missing or labeled
wrong.
7points
Many graphs and titles
are missing.
5points
No
ISDS 7303 Module 3-7 Assignment Rubric
The following rubric will be used to grade the assignments for Modules 3-7.
Introduction &
Business
Understanding
Description demonstrates
understanding of business
issue at hand. Explains
specific issues to be
addressed in clear and
specific language.
10 points
Describes the general
business issue at hand.
Lacking specificity or clarity
5 points
Does not demonstrate
understanding of the
business issues. Explanation
is vague or unclear.
0 points
Data Understanding & Description demonstrates
Preparation
superior data preparation
and understanding. Utilizes
appropriate recoding based
on relationship between
inputs & outputs, addresses
missing values using
advanced methods and
makes transformations as
necessary.
20 points
Demonstrates understanding
of data issues. Prepared data
sufficiently.
10 points
Data understanding not
sufficient. Data preparation
not sufficient.
0 points
Analysis
Does not demonstrate
understanding of the
business issues. Explanation
is vague or unclear.
40 points
Provides adequate
preparation of data with
some analysis and coding.
20 points
No adequate solution of the
problem. Data preparation
not sufficient.
0 points
Conclusion
Conclusion recaps issues and
addresses implementation
with specific insights.
Provides specific
recommendations derived
from the model.
20 points
Conclusion does provide a
recap of the issues and
addresses implementation.
10 points
Conclusion is too broad. Does
not address specific issues of
the case.
0 points
Format
Follows report format
Follows format mostly with
exactly. Text has superior
some minor issues
layout with headings.
5 points
Appendix has meaningful
charts, numbered and titles.
References graphs and tables
in text and clearly explains
them to support findings.10
points
Does not follow required
format.
0 points
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