Data Normalization Methods: Understanding and Choosing the Right One

[aruwin]

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

 

[I like Serena]

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.

 

[aruwin]

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.

 

[I like Serena]

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?

 

[blue_raver22]

Data normalization is a process of organizing and transforming data into a consistent and standardized format, making it easier to analyze and compare. There are several methods of data normalization, each with its own advantages and uses. In this response, I will explain two commonly used methods of data normalization and how to choose the right one for your data.

1. Min-Max Normalization:
Min-Max normalization, also known as feature scaling, is a method of transforming data into a range between 0 and 1. This method is useful when the data has a wide range of values and you want to scale them down to a smaller range. Min-Max normalization is calculated using the formula:

X' = (X - Xmin) / (Xmax - Xmin)

Where X' is the normalized value, X is the original value, Xmin is the minimum value in the data set, and Xmax is the maximum value in the data set.

For example, let's say we have a data set of students' test scores ranging from 60 to 100. To normalize this data using Min-Max normalization, we would use the formula:

X' = (X - 60) / (100 - 60)

If a student scored 80, their normalized score would be (80-60) / (100-60) = 0.4.

2. Z-Score Normalization:
Z-Score normalization, also known as standardization, is a method of transforming data into a standard normal distribution with a mean of 0 and a standard deviation of 1. This method is useful when the data has a normal distribution and you want to compare values that are measured on different scales. Z-Score normalization is calculated using the formula:

X' = (X - μ) / σ

Where X' is the normalized value, X is the original value, μ is the mean of the data set, and σ is the standard deviation of the data set.

For example, let's say we have a data set of students' heights in centimeters. To normalize this data using Z-Score normalization, we would first calculate the mean and standard deviation of the data set. Let's say the mean is 165 cm and the standard deviation is 10 cm. Then, for a student who is 170 cm tall, their normalized height would be (170-165) / 10 = 0.5.

Now, how do you know which method of