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Showing results for tags 'regression'.
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Q 286. Why is multicollinearity a problem? How is it detected? What is the right course of action when it is found? Explain with an example. Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time -
Residual Analysis
Vishwadeep Khatri posted a question in We ask and you answer! The best answer wins.
Q 271. What is a residual in Regression? Why is it important to analyze the residuals before assessing the goodness of a Regression Model? What does it mean if Residuals are non normal or non random? Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the ne -
Quantile Regression
Vishwadeep Khatri posted a question in We ask and you answer! The best answer wins.
Q 232. Quantile Regression estimates the median (or a particular quantile) whereas Linear Regression estimates the mean. What are some of the business scenarios where quantile regression would yield better results as compared to linear regression? Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/fo -
What are the key differences between Multiple Regression using historical data and Multiple Regression based on Experimental Data (DOE)? What are the advantages of one over the other, if at all?
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Q. 145 What is the usage of R Squared and R Squared Adjusted as used in Regression Analysis. Please explain with example(s). Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening as per Indian Standard Time.
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i am doing ordinal logistic regression using minitab, please find the results as below. when i asked my friend to cross check the results in "R" for the same data set, i was surprised to see different results come up. what was even more peculiar is that the coef no's seem to be similar however Minitab was showing -ve values where "R" was throwing +ve values and vice versa. i am curious to know why this happens and which result should i consider? Coedf(Minitab) Coef( r ) overall Rating -2.86% 2.86% Parameter (A) 1.30% -1.30% Par -
Hello Friends, Recently I have attended the interview with a six sigma expert, he give me scenario and told me to find the flaw in it. Below is scenario : When Age is increase and petrol price is also increasing, the tool used is regression. The result shows as strong positive correlation. R=1 What is the flaw? -
Hi, Here is a quick query.... If for a Logistic Regression Test, the Whole Model Test is significant (p<0.05) and the Lack of Fit test is also significant (p<0.05), with just one parameter, what should be our interpretation and next steps. I was using the test to validate one of the Xs. Nirankar
