# Kiran Varri

10

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• Rank
Manager SixSigma WOW Express
• Birthday 06/24/1978

## Profile Information

• Company
ITC Hotels
• Designation
Manager - WOW Express
1. ## Three Romeos And A Juliet

Great Video Sir !!! Thanks a Ton......i will be sharing this with my team too.... - Kiran Varri
2. ## DOE : Full Factorial/Fractional factorial & Response Surface Experiment

Hi All, Could someone explain the distinct difference/strengths/weaknesses when talking about DOE: Full Factorial & Fractional Factorial Experiments & Response Surface Experiment.....appreciate if explained with examples.....Cheers Kiran Varri
3. ## DOE : Response Optimizer & Contour Plot : When & Why

Hi Friends, Would appreciate if you could explain the unique similarities/differences between Response Optimizer & Contour Plot and cite any examples which work well with each in isolation..... Happy New Year 2010.... Kiran Varri
4. ## Signal, Noise & Sample size's role in Significance level calculation

As Significance level is dependent on Signal, Noise & Sample size, should i consider that to work with higher Significance Levels, i will have to either improve the Signal strength, increase Sample size or reduce Noise. A good amateur level example covering all the 3(Signal, Sample size & Noise) would be highly appreciated. Gud times...
5. ## Apt Significance Level (Corresponding p value) & Sample size

Hi Friends, I would appreciate some inputs on : Example : I am trying to test if two variants of fertilizers A & B have similar impact on the length of Sunflower stems applied in 9 plots(5 plots applied with Fertilizer A, 4 plots applied with Fertilizer . The individual stems lengths and averages are calculated. The commonly accepted Significance Level 0.05 is selected. The p value obtained is 0.03. If P value is low. The null must go. Hence, we say there is significant variance between the mean length of sunflower stems. However, what if i want to run the test at 0.01 Significance level. At 0.01 level, we have to accept Null Hypothesis. p value is greater than 0.01. To justify the Significance level of 0.01, should i not increase my sample size? From past understanding, i remember that for lower levels of Significance like 0.01, the sample size needs to be increased i.e. being very very critical. Eg : In Space Shuttle, the cost of failure is very very high. This is part 1. The next doubt i have is, for a Space Shuttle whose number of trips itself are very very few from statistical point of view, do they fulfill the 0.01 or lesser(0.001 too) significance level requirements through simulations?, as they can't test it in real manner. This is part 2. I hope i was clear with my caselet. I appreciate your inputs
6. ## VSM : Differentiation between "Ideal State" & "Future State"

Value Stream Mapping : What is the difference between "Develop Ideal State Map" & " Develop Future State Map" Step sequence is : Go Back to Garage / Choose Natural Groups / Create Current state Map / Develope Ideal State Map / Develop Future state Map / Create Action plans & Tracking Appreciate some examples...... Gud times....
7. ## Difference between PPM & DPMO

Hi Shalini, I do not think that if count of defectives is known, it makes defects per defective is 1. 1 Defect could also result in a Defective and >1 defect also could result in Defective. The Defects/Defective needs to be measured seperately if that is of interest. Hope it helps.. Kiran Varri
8. ## Sequential Test Method For Process Capability Decisions

Appreciate help in understanding Sequential Test Method For Process Capability Decisions. It's claimed that this method can save upto 50% Sampling Savings. Examples welcome... Gud times...
9. ## Help with Probability Distribution function

Probability Function : What is difference between cumulative distribution function & Probability mass function. Need help with understanding TRUE/FALSE option of NORMDIST excel function. Example : For one given value, (Value/Mean /Std.Dev/(TRUE/FALSE )) the output was .908 & .109. Help appreciated...
10. ## Regression anlaysis for optimisation

Hi Sateesh, The Regression equation is nothing but an effort to predict the output when your X's i.e. variables takes a certain set of values. You assign values to the variables and you have the predicted result as Y. So, with the equation provided, if you have control on your Xs (Variables) you can estimate your Ys.. Coming to Correlation, It just gives out if 2 variables are positively/negatively correlated i.e. related. Eg : In cricket Spin Balls vs Runs scored for a player. If negative correlation, you may choose not to select the player for a spin friendly pitch and vice versa... Hope it helps...
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