Normal Distribution Tutorial - Summary
Do you know what the shape of the normal distribution curve represents? Understanding normal distribution is crucial for every data scientist or aspiring data scientist. A normal distribution creates the famous bell curve and has numerous applications in the field of data science.
The normal distribution, often called the Gaussian distribution, is a vital concept in statistics. It forms the backbone of data science. When we conduct exploratory data analysis, we begin by examining the data to determine its probability distribution, right? Well, the Normal Distribution happens to be the most prevalent probability distribution we encounter.
Understanding Normal Distribution
This bell-shaped curve is widely recognized as the Normal Distribution. Its discovery is attributed to Carl Friedrich Gauss, which is why it is also known as the Gaussian Distribution.
We can easily explain the Probability Density of the Normal Distribution using just two essential parameters: the Mean and the Standard Deviation. This curve is perfectly symmetrical around the Mean. You will notice that, for this distribution, the Mean, Median, and Mode are all the same.
Key Properties
Note
One amazing feature of a normal distribution is that it maintains its characteristic shape even after transformations, unlike other probability distributions that might change. Here are some key rules to remember about Normal Distributions:
- A Normal Distribution can be formed by multiplying two Normal Distributions.
- The Sum of two Normal Distributions is always a Normal Distribution.
- A Normal Distribution can also be created by combining or converging two Normal Distributions.
- Even the Fourier Transformation of a Normal Distribution remains a Normal Distribution.
If you want to deepen your understanding, you can download the PDF related to this topic. It offers detailed insights on normal distribution and much more! Don’t miss out!
