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Kevin Pedde replied to Nimish's discussion Transformation of data in the group Analytical Techniques

"Like a quadratic or cubic of that variable. You will see a parabolic or cubic shape in the residuals."

Aug 22, 2012

Nimish replied to Nimish's discussion Transformation of data in the group Analytical Techniques

"Hi Keven, Thank you for your reply!! One quick question what do you mean by a higher order item? Cheers, nimish"

Aug 22, 2012

Kevin Pedde replied to Nimish's discussion Transformation of data in the group Analytical Techniques

"Hi Nimish, If you are performing simple linear regression I would examine the residuals of the model output. From there you can determine if you need to add higher order terms or need to transform the independent variable. If you plot…"

Aug 21, 2012

Sergio Del Vecchio replied to Nimish's discussion Transformation of data in the group Analytical Techniques

"a rule of thumb I recently was made aware of......... if the data you are attempting to model has more than a 10-fold difference between the min and max values, then it may be worth natural logging this data to create a more Normal distribution."

Aug 19, 2012

Nimish added a discussion to the group Analytical Techniques

Transformation of data

Hi,I'm currently working on cleaning of data for a regression model. The data has variables such as the average interest rate, stock market indices etc. Could you please let me know whether it would make sense to transform the data? I did a simple calculation of the variance and the standard deviation of the variables; noticed that it was pretty high for these indicators. Hence I was wondering would it make sense to transform the data?Thanking you in advance.Regards,NimishSee More

Aug 19, 2012

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Why is it so hard to count this way?

Map of Data Science University Programs

Use PRESS, not R squared to judge predictive power of regression

Text mining: alternative to Wordflex?

Audience expansion

How to develop churn prediction model for telecom company?

Strategy for building a “good” predictive model

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