MHB Data Normalization Methods: Understanding and Choosing the Right One

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Data normalization is essential for combining different categories numerically by ensuring they have a similar range. Two recommended methods are standardization, which involves subtracting the mean and dividing by the standard deviation, and min-max scaling, where each value is divided by the highest value in the dataset. Understanding these methods is crucial for effective data analysis, as they help in preparing data for machine learning algorithms. The discussion emphasizes the importance of knowing how to calculate the mean and standard deviation for the first method, and understanding the results of scaling for the second. Proper application of these techniques enhances data comparability and accuracy in analysis.
aruwin
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Hello, please help me with this problem. The question is in the picture. I don't know how to normzalize the data. How to know which method should be used? I need two methods of normalization here. HELP!
Please teach me in detail.
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aruwin said:
Hello, please help me with this problem. The question is in the picture. I don't know how to normzalize the data. How to know which method should be used? I need two methods of normalization here. HELP!
Please teach me in detail.
View attachment 1755

Hi aruwin! :)

Two different normalization methods might be:
  1. Approximate with a normal distribution.
    Subtract the mean, and divide the result by the standard deviation.
  2. Scale to the same range.
    For instance by dividing each score by the highest one.
 
I like Serena said:
Hi aruwin! :)

Two different normalization methods might be:
  1. Approximate with a normal distribution.
    Subtract the mean, and divide the result by the standard deviation.
  2. Scale to the same range.
    For instance by dividing each score by the highest one.
Thanks for replying but could you please tell me why do you pick those two methods? And tell me how to use them.
 
aruwin said:
Thanks for replying but could you please tell me why do you pick those two methods? And tell me how to use them.

Well, we need to combine the different categories numerically.
For that they have to be "normalized", that is, they need to have a similar range.
I'm just mentioning 2 methods to do it.

How to use it?
Well, for method 1, do you know how to calculate a mean? And a standard deviation?
And for method 2, what do you get if you divide each price by the highest price?
 
There is a nice little variation of the problem. The host says, after you have chosen the door, that you can change your guess, but to sweeten the deal, he says you can choose the two other doors, if you wish. This proposition is a no brainer, however before you are quick enough to accept it, the host opens one of the two doors and it is empty. In this version you really want to change your pick, but at the same time ask yourself is the host impartial and does that change anything. The host...

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