Replies to This Discussion

Permalink Reply by Vivek Agarwal on December 9, 2014 at 5:14am

We can simply normalize data by subtracting it from mean & dividing that term by standard deviation.

(x1-mean(x1,x2,..xn))/sd(x1,x2,....xn)

I hope, it will be useful to you.

Vivek

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Permalink Reply by Sagar Diwakar Uparkar on December 11, 2014 at 8:57am

Hi Vivek,

Good evening. Firstly I thank you for your revert. But let me tell, standardisation of data will not reduce skewness. Let me give you little brief on my efforts. I'd try natural log, log to base 10, square root, inverse, Box Cox normalisation method. But I fail to make it normalise for a variable.

If you'll have any other method or suggestions then I welcome.

Once again thank you for your reply.

Have a pleasant evening.

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Permalink Reply by Vivek Agarwal on December 11, 2014 at 9:31pm

Hi Sagar,

I have got your concern. Could you please share sample data on which normalization needed. I'll also try from my end & will share you the approach.

regards,

Vivek 

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Permalink Reply by Sagar Diwakar Uparkar on December 15, 2014 at 3:17am

Dear Vivek,

Good afternoon. I hope you are doing well. Thank you for your reply, but I'm sorry that I can't share data with anyone due to confidentiality. If I could do that it would have solve my problem.

Once again thank you for your support.

Have a nice day ahead.

Warm regards,
Sagar.

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Permalink Reply by Muhammad Shahid on December 26, 2014 at 12:11am

Hi Sagar,

If I am correct, your main objective is to transform non-normal data to normal. You can use Johnson transformation.

I hope, it will help you.

Shahid

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Permalink Reply by Sagar Diwakar Uparkar on January 18, 2015 at 9:18am

Hi Muhammad,

Thank you for your revert. I'm not aware Johnson transformation but I'll try. Once again thank you so much.

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Permalink Reply by Kevin Pedde on December 29, 2014 at 2:26pm

Hi Sagar,

I'd like to help with your question but want to get some background information first.  I assume you are trying to satisfy the requirement of normality of errors in your regression model?

How are you determining that your independent variables are skewed?  What measure or method are you using?

Try plotting the residuals of the independent variable and the dependent variable and see what the pattern or shape looks like.  That can help you determine what transformation you need to use on your data.  Normalizing your data wont hurt either, it will give you more robust coefficient estimates but will change the interpretation slightly.

Let us know how it works!

Kevin

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Permalink Reply by Sagar Diwakar Uparkar on January 18, 2015 at 9:23am

Hi Kevin,

Good evening. Thank you for your revert. I'm using excel so I'm dependent on descriptive statistic. I could not analyse that since I'm lacking with statistical toils.

I'm trying to normalised data to make decision about regression model. Because in excel normality of error is bit difficult task.

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Permalink Reply by Yo Mama on January 2, 2015 at 3:16pm

Box Cox transformation, apply boxcox to see the closest transformation power you can apply to make the relationship linear, if first difference and log normalization did not work.

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Permalink Reply by Sagar Diwakar Uparkar on January 18, 2015 at 9:25am

Hi Yo Mama,

Good evening. Thank you for your revert. I'd already tried that it is not worth, since it could not normalised data.

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Permalink Reply by Mortal Kolle on February 11, 2015 at 12:15am

Hello Sagar,

I would suggest bootstrap and/or jackknife methods (in general, resampling is useful in highly non-normal data)

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Permalink Reply by Daniel Seymore on March 9, 2015 at 3:01pm

Hi Sagar

I would have taken the approached Mortal Kolle mentioned. However my starting point would also have been Box Cox, but as you mentioned this was unsuccessful.

Regards

Daniel

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