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Everything posted by RANSINGH SATYAJIT RAY

  1. Overview: Class Imbalance is one of the challenges faced in Classification Model prediction wherein the output from a dominating class is reflected in the output of the model there by leading to result which is just like a generalization i.e. say if 990 customer are not churning but 10 people are churning then, when we develop the classification model to determine whether some one is churning or not model will be running at higher accuracy when we are considering not churning as determining level of the classifier even when our model predicted churning people as not churning. This ki
  2. In statistical test of Hypothesis, we usually encounter p value and alpha value but why is power of the test or beta value or any term related to it not refleted? Isn't beta value equally important as alpha value? Why does Type 1 error have the edge over Type 2 error?
  3. The Process FMEA is used to not only minimise the the risk associated with a process but also define them whenever possible. It helps in identifying the known or potential failure modes and provide follow-up and corrective action before the first production run.The Process FMEA is related to Lean Six Sigma DMAIC in the following ways to reduce variation in the process and generation of waste. 1. Reduces product development time and costs. 2. Help reduces the redundancies in the process. 3. Help identify the significant characteristics 4. It helps in identifying the sequen
  4. While managing the quality driven processes and parameters, there is always a question that arise of whether variables affecting the product or service meet the requirement. For this several parameters like Cp, Cpk (When data is fairly normal) and Ppk indices when the data is non normal (after suitable transformation for stable data without out of control data points), Z Scores, DPMO, etc are used. Many of the indices (Cp, Cpk and Ppk) used require a good amount of data (in terms of number of data points per sample) for providing valid result on the other hand with respect to many
  5. Root causes are the causes beyond which we cant go. Root cause would be terminated only when we get change in system, process, technology, training, policy. Hence in that case Root cause would refer to cause in those cases like a. technology is missing, ' b. policy doesn't say anything at present, c. training process has not been initiated for something, d. changes/tweak in system are made. Few examples where causes refer to root causes: 1. (changes/tweak in system are made) We could consider causes as root causes when we are performing controlled experiments wher
  6. Is there any method to calculate OEE (Overall Equipment Effectiveness) based on the data from MTTR (Mean time to repair), MTTF (Mean time to Failure) , MTBF (Mean Time between Failure) in equipment used in non manufacturing utility industries.
  7. Thanks VK, One question does all Equipment Life data should in generally follow Weibull distribution or it is confined to specific product. Any example of its application?
  8. Is reliability and maintainability linked with Six Sigma. Is there any significant application of Weibull distribution.
  9. In the central limit theorem (CLT) establishes that, in some situations, when independent random variables are added, their properly normalised sum tends toward a normal distribution even if the original variables themselves are not normally distributed. The Question is how could this be proven as it looks very intimidating at times. Why is 30 considered the minimum sample size in some forms of statistical analysis? Is there any rationale for this.
  10. Given the skewness and Kurtosis we could predict the shape of a probability distribution. Skewness: The Lack of Symmetry in the probability distribution is called Skewness, A distribution is positive skewed when it has a long tail to the right (Right tail are + skewed) and a distribution is negative skewed if it has a long tail towards left. Further it is also interesting to know that when we check the data points using the Box plot if the mean of the dataset is greater that the median then its negative skewed and when the mean is less than median then its positive skewed. Kurto
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