Discrete Response DOE Question

In summary, the speaker is looking for assistance in optimizing the performance of an accounts receivable department through an experiment using different tools such as letters, phone calls, and sms. They are struggling with finding an example of conducting a design of experiments with a discrete response and are considering running multiple trials for each treatment. They are seeking advice and resources on this matter.
  • #1
diorio
1
0
Hi Everyone,

I am looking for some assistance on a problem that I have been working on for the last few weeks. I would like to optimise the performance of an accounts receivable department. In this dept there are several tools available to obtain a commitment to pay such as letters, phone calls, sms and others. I would like to run an experiment to determine the optimal use of these tools and I suspect that there may be some interactions between these factors.

I have researching how I can run a simple design of experiments to determine the optimal treatment. For example I could run a simple 2 level 2 factorial experiment:

Factors:
Letter Call Account
Y Y Paid
Y N ?
N Y ?
N N ?

The issue that I have is that not only are my factors discrete (Y/N), my response is discrete as well (Paid/Not Paid). I am having difficulty finding an example of how to conduct a DOE with a discrete (binomial) response.

After some researching I am starting to believe I need to run multiple trials for each treatment. For example running multiple instances of: Letter Yes / Call Yes treatment so that I can obtain a resulting proportion (example: 4 of 7 customers treated in this manner paid - 57%), I have seen some places that there are suggestions to run np>5 number of treatments. If in this instance if I expect 70% proportion of people to pay, then I believe I may need to run n=5/.7=(7 to 8) trials of each treatment.

Can anyone advise if I am on the right track?

I have searched numerous six sigma and DOE references and haven't been able to reach a definitive conclusion. Is there anyone out there that could advise me or provide a similar example or book that may assist me with a discrete response DOE?
 
Physics news on Phys.org
  • #2
You have 3 factors , i.e. letter, sms, and call, to control the incidences of get paid. Each factor has 2 level, yes/no. So you have 2x2x2=8 different treatments.

Say you decide to trial 80 customers, there are random 10 customers for each treatment. Resulting a data set of 80 rows, 4 columns. 3 columns indicate the factor used, and the last column indicates the payment.

Use ANOVA you can study the significance of the factors and their interactions.
 

1. What is a Discrete Response DOE?

A Discrete Response DOE, or Design of Experiments, is a statistical method used in scientific research to determine the relationship between a set of input variables and a discrete output response. It involves systematically varying the inputs and measuring the resulting response to identify significant factors and optimize the output.

2. What are the advantages of using a Discrete Response DOE?

One major advantage of using a Discrete Response DOE is that it allows for the identification of significant factors and their interactions, which would not be possible through traditional one-factor-at-a-time experiments. This results in more efficient and effective optimization of the response variable. Additionally, DOE reduces the number of experiments needed and provides a systematic approach to experimentation.

3. How do you choose the variables to include in a Discrete Response DOE?

The variables chosen for a Discrete Response DOE should be those that are believed to impact the response variable. It is important to include both quantitative and qualitative variables, as well as factors that may interact with each other. The number of variables should also be kept manageable, as including too many can result in a complex and difficult to interpret model.

4. What are some common types of Discrete Response DOE designs?

Some common types of Discrete Response DOE designs include full factorial, fractional factorial, and response surface methods. Full factorial designs involve testing all possible combinations of the input variables, while fractional factorial designs involve testing a subset of these combinations. Response surface methods use a mathematical model to identify the optimal settings for the input variables.

5. How do you analyze the results of a Discrete Response DOE?

The results of a Discrete Response DOE can be analyzed using statistical tools such as ANOVA (analysis of variance) and regression analysis. These methods help to identify which factors have a significant impact on the response variable and how they interact with each other. Visual aids, such as graphs and plots, can also be used to interpret the results and identify any trends or patterns.

Similar threads

B Inverse Transformation from Response Surface
    • Set Theory, Logic, Probability, Statistics
Replies
1
Views
742
I When does ## { n \choose k } ## represent set of lists?
    • Set Theory, Logic, Probability, Statistics
Replies
20
Views
1K
I Discrete Euler-Lagrange equations
    • Classical Physics
Replies
3
Views
2K
A Uncertainties on Bayes' theorem
    • Set Theory, Logic, Probability, Statistics
Replies
19
Views
1K
I What Does the Likelihood Function Tell Us?
    • Set Theory, Logic, Probability, Statistics
Replies
16
Views
1K
Medical CRISPR treatment has been greenlit in UK in global first (for sickle cell disease and beta thalassemia)
    • Biology and Medical
Replies
1
Views
745
A Discrete measurement operator definition
    • Quantum Physics
Replies
2
Views
1K
A Uncertainty on an asymmetry signal
    • Set Theory, Logic, Probability, Statistics
Replies
4
Views
1K
Current and Future AI Threats to Humanity and the Human Response
    • Computing and Technology
Replies
10
Views
2K
'Residence' time distribution function for a Pulse input
    • Materials and Chemical Engineering
Replies
14
Views
2K

Hot Threads

Recent Insights

Back
Top