A Life Insurance Company offers various retail Insurance products. But the business is not as good as expected.
Top management decides to set up a task force to analyze the reasons and suggest corrective measures. After a detailed study, the team submits the report indicating the need to design the claim management system scientifically.
It is found that the claims are reported at an average rate of 500 per week following Poisson distribution. The Claims Processing rate too follows Poisson distribution.
Analyze following alternatives and offer your recommendation.
1. How will the system behave if the claims management system is designed to have claims processing rate as 400 per week Poisson?
2. How will the system behave if the claims management system is designed to have claims processing rate as 500 per week Poisson?
3. How will the system behave if the claims management system is designed to have claims processing rate as 800 per week Poisson?
4. How will the system behave if the claims management system is designed to have claims processing rate as 1200 per week Poisson?
5. Management wants the average number of claims in the system at a time to not exceed 25. What do you recommend to achieve this?
Monday, March 1, 2010
Tuesday, February 16, 2010
Beyond Survival Curve
There is 'Something to Do' posted at
http://www.scribd.com/doc/26871426/An-Exercise-to-Understand-Survival-Analysis-Better
http://www.scribd.com/doc/26871426/An-Exercise-to-Understand-Survival-Analysis-Better
Thursday, February 11, 2010
Interactive Statistical calculation Pages
Depending on interest, one can explore the following website which has resource pages on various topics related to Statistics.
http://statpages.org/
It has some pages related to survival analysis in the second half.
For example,
http://biostat.hitchcock.org/BSR/Analytics/CompareTwoSurvivalDistributions.asp
http://statpages.org/
It has some pages related to survival analysis in the second half.
For example,
http://biostat.hitchcock.org/BSR/Analytics/CompareTwoSurvivalDistributions.asp
Tuesday, February 2, 2010
Use of Censored Data in Survival Analysis
Let us include some censored data in the previous exercise and do it again. All these numbers that are followed by + are censored data here. They are censored because the event of interest did not happen in these cases. Reason may be any.
Following data are related to time in days at which first claim happened in various cases under a health insurance product.
157, 300, 289, 91, 102+, 235, 28, 188, 311+, 32+, 119, 273, 78, 200+, 37, 349, 235, 96, 200+, 157, 78+, 314, 178, 135+, 263+, 62, 198+, 235, 192.
We are interested in knowing the pattern in probability of first claim happening after certain number of days.
(Apply your own concept and try finding the pattern. To draw any conclusion related to probability in this case, we need larger volume of data. But doing this exercise manually will help in understanding the concept.)
1. In how many cases, there is no claim till 150 days?
2. What is the probabilty that there will not be any claim till 150 days?
3. What is the range of days for which the probability in Q2 above remains same?
4. Identify similar ranges in which probabilty doesn't chamge.
5. Draw the probability Vs. time graph. This graph is called Survival Graph and represents the survival function.
6. Develop a method to draw such graphs from any given set of similar data.
7. Think about the impact on graph if the volume of data is increased.
Following data are related to time in days at which first claim happened in various cases under a health insurance product.
157, 300, 289, 91, 102+, 235, 28, 188, 311+, 32+, 119, 273, 78, 200+, 37, 349, 235, 96, 200+, 157, 78+, 314, 178, 135+, 263+, 62, 198+, 235, 192.
We are interested in knowing the pattern in probability of first claim happening after certain number of days.
(Apply your own concept and try finding the pattern. To draw any conclusion related to probability in this case, we need larger volume of data. But doing this exercise manually will help in understanding the concept.)
1. In how many cases, there is no claim till 150 days?
2. What is the probabilty that there will not be any claim till 150 days?
3. What is the range of days for which the probability in Q2 above remains same?
4. Identify similar ranges in which probabilty doesn't chamge.
5. Draw the probability Vs. time graph. This graph is called Survival Graph and represents the survival function.
6. Develop a method to draw such graphs from any given set of similar data.
7. Think about the impact on graph if the volume of data is increased.
Friday, January 29, 2010
An Exercise to understand Survival Analysis
Following data are related to time in days at which first claim happened in various cases under a health insurance product.
157, 300, 289, 91, 235, 28, 188, 119, 273, 78, 37, 349, 235, 96, 157, 314, 178, 62, 235, 192.
We are interested in knowing the pattern in probability of first claim happening after certain number of days.
(Apply your own concept and try finding the pattern. To draw any conclusion related to probability in this case, we need larger volume of data. But doing this exercise manually will help in understanding the concept.)
1. In how many cases, there is no claim till 150 days?
2. What is the probabilty that there will not be any claim till 150 days?
3. What is the range of days for which the probability in Q2 above remains same?
4. Identify similar ranges in which probabilty doesn't chamge.
5. Draw the probability Vs. time graph. This graph is called Survival Graph and represents the survival function.
6. Develop a method to draw such graphs from any given set of similar data.
7. There may be many cases in which claim did not happen during the study period or the case got withdrawn. Data related to such cases are called censored data. Think about how to include them in similar analysis.
157, 300, 289, 91, 235, 28, 188, 119, 273, 78, 37, 349, 235, 96, 157, 314, 178, 62, 235, 192.
We are interested in knowing the pattern in probability of first claim happening after certain number of days.
(Apply your own concept and try finding the pattern. To draw any conclusion related to probability in this case, we need larger volume of data. But doing this exercise manually will help in understanding the concept.)
1. In how many cases, there is no claim till 150 days?
2. What is the probabilty that there will not be any claim till 150 days?
3. What is the range of days for which the probability in Q2 above remains same?
4. Identify similar ranges in which probabilty doesn't chamge.
5. Draw the probability Vs. time graph. This graph is called Survival Graph and represents the survival function.
6. Develop a method to draw such graphs from any given set of similar data.
7. There may be many cases in which claim did not happen during the study period or the case got withdrawn. Data related to such cases are called censored data. Think about how to include them in similar analysis.
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