siv_santh

Hi Suresh sir, This was useful thanks. I have done the following, kindly confirm if I am on the right track with respect to the tests and associated conclusions drawn. Step 1:- To determine if reconcillication is a sustainable solution: I have calculated the proportion of released licenses (events) Vs the total number of licenses (Trials). I have also calculated for the same month the proportion of new users set up (events) Vs the total number of licenses (Trials). I have applied the 2-Proportion test (Fisher's test is not available in this Minitab version) and the test result suggests that these proportions are 'equal'. So, reconcilliation may not be a sustainable exercise. Step 2:- To determine if chanage in policy would help: I have calculated the proportion of released licenses for 90 day policy (events) Vs the total number of licenses (Trials). I have also calculated for the same month the proportion of released license for 60 day policy (events) Vs the total number of licenses (Trials). I have applied the 2-Proportion test (Fisher's test is not available in this Minitab version) and the test result suggests that the number of licenses released is marginally high. So, change of policy could be a temporary solution but not a permenant solution

Hi Suresh sir, This was useful thanks. I have done the following, kindly confirm if I am on the right track with respect to the tests and associated conclusions drawn. Step 1:- To determine if reconcillication is a sustainable solution: I have calculated the proportion of released licenses (events) Vs the total number of licenses (Trials). I have also calculated for the same month the proportion of new users set up (events) Vs the total number of licenses (Trials). I have applied the 2-Proportion test (Fisher's test is not available in this Minitab version) and the test result suggests that these proportions are 'equal'. So, reconcilliation may not be a sustainable exercise. Step 2:- To determine if chanage in policy would help: I have calculated the proportion of released licenses for 90 day policy (events) Vs the total number of licenses (Trials). I have also calculated for the same month the proportion of released license for 60 day policy (events) Vs the total number of licenses (Trials). I have applied the 2-Proportion test (Fisher's test is not available in this Minitab version) and the test result suggests that the number of licenses released is marginally high. So, change of policy could be a temporary solution but not a permenant solution.

One of the good books that I have been advised earlier is, 'The Complete Six Sigma Handbook' by Thomas Pyzdek

Hi, I am working on a project to review a policy on revoking certain user access previleges based on period of inactivity. Currently the user inactivity period is set to 90 day, based on which the access previleges are revoked. However, as there is cost involved in providing these access previleges, there is a need to revisit this policy and free more licenses that can be used to process new access requests. I have collecetd fortnightly inactivity data for the last 4 fortnights. Clearly the number of licenses that we would free by rewroking the policy to 60 day would increase by around 40% to 50%. I am looking to demonstatre this thourgh one of the tests. To my mind the test that would best help in demonstrating this is 2-sample t Test where I can compare the average number of inactive users. However the problem is that my 'Y' is Average number of inactive users, which is discrete. The count that I get hovers around 600 to 1000 inactive users. The proportion or Chi-Square tests don't seem relevant here. So, Is this count large enough for me to choose the 2-Sample t test? If yes, how do I justify this step? Regards, Shiva Kumar Bhasakaran.

Hi Mani, Using the below data in the same sample size calculator from the file cabinet, I obtained the sample size of 232. Population: 25000 Hypothesized proportion: 80% (as I have already obtained responses) Margin of error: 5% Confidence level: 95% Power of test: 50% Alternate Hypotheses: Not Equal to The required sample size is 232. So my inference comes from working backwards from here:- I have surveyed 231 users and have received one specific response from 80% of respondents. As you pointed out, one thing I need to ensure is that the responding sample is random and not biased. So, assuming randomness in the sample, the above inference should hold good? Suresh sir, any comments?

Hi Suresh Sir, No, we are unable to determine the maximum possible tickt count. Hence I could not relate the 1P test to this purpose of determining Mean monthly ticket count. What error would I commit in this specific scenario by using 1 sample T test for this data? Also, I think we are ok to work with 4 samples as per the power and sample size formula for 1sample T test, would you comment? Regards, Shiva Kumar

Hello, I have the ticket count for the last 4 months for my IT department. 16391 15525 17516 16966 This is discrete data as this is count of incidents. However, this data does not follow poission distribution and this data is observed to be normal. I am trying to demonstrate that the decreasing the average monthly ticket count for my IT team to 12000 would be a significant achievement and 12,000 can be the target for my GB project. So, I am trying to compare the mean of my sample to this constant 12000. I understand that the 1 sample T test is used for continous data and used to compare sample mean. However, since this data is Normal, am I okay to use 1 sample T test. Would this yield the desired results?

Hello Suresh Sir, Manian and Nirankar, Thanks for the information. I still have a question though. currently thourgh the response rate is less than 1%, 80% of the respondents are 'Satisfied'. So, when I use the formula for determining the Margin of error with the below data, I get a response of 5.13%. (I have used the sample size calculator available as freeware on the net for random samples) Sample Size: 231 Population: 25000 % of respondents giving one specific response (satisfied): 80% Confidence Level: 95% From this why should we not infer, "With a confidence level of 95%, we can conclude that 75% to 85% of the population (25000 users) are satisfied with the service provided". I am not sure, where am I lost in undestanding how to interpret this response, kindly help. Manian: Can you kindly confirm which test did you use to arrive at 1200?

Hello, I analyze monthly transactional CSAT responses from customers. These responses are on a scale of 1 to 5 (Discrete). The company's target is to maintain 80% of satisfactory responses and all reponses of 4 or 5 are classified as satisfactory responses. There are about 25000 such transactions that happen every month. A survey is sent out to each of these customers. We receive about 230 responses everymonth, which is less than 1% of the total transaction volume. How do I determine the sample size necessary for me to make a statistically significant statement about the company's performance on the transactional CSAT front? I did try and use 1-p power and sample size determination test but I am really not sure, how do determine the sample size, which formula/test to use and why? Kindly help.