Normal curve features

abhishreshthaa

New member
Normal curve features:

• Normal distributions are symmetric around their mean.

• The distribution has two parameters, mean (µ, mu) and standard deviation (σ, sigma)

• The mean, median, and mode of a normal distribution are equal.

• 68% of the area (data points) lies within one standard deviation (+/-1 sigma) of the mean.

• 95% of the area (data points) lies within two standard deviation (+/-2 sigma) of the mean.

• 99.7% of the area (data points) lies within three standard deviation (+/-3 sigma) of the mean.
 
Normal curve features:

• Normal distributions are symmetric around their mean.

• The distribution has two parameters, mean (µ, mu) and standard deviation (σ, sigma)

• The mean, median, and mode of a normal distribution are equal.

• 68% of the area (data points) lies within one standard deviation (+/-1 sigma) of the mean.

• 95% of the area (data points) lies within two standard deviation (+/-2 sigma) of the mean.

• 99.7% of the area (data points) lies within three standard deviation (+/-3 sigma) of the mean.

Hey abhi, thanks for sharing the information and i am sure it is going to help many people and i appreciate your work. Well, i have also got some important information regarding the normal curve features and would like to share it with you.
 

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jitenmazee996

Par 100 posts (V.I.P)
The normal curve, also known as the bell curve, is a symmetrical graph that shows how things are usually distributed. Most data points cluster around the middle, called the mean. It's like a hill that slopes down on both sides. This means that events are equally likely to happen on either side of the middle. One important thing about the normal curve is that it's symmetrical. If you cut it in half, each side would be a mirror image of the other. This is because the curve is centered around the mean, which is also the median. So, events have an equal chance of happening to the left or right of the middle.

The curve also uses something called standard deviation, which shows how spread out the data is. In a standard normal distribution, about 68% of the data is within one standard deviation from the mean, 95% within two standard deviations, and 99.7% within three. An easy example of the normal curve is the heights of people. If you graphed the heights of a lot of people, it would make a curve like a bell. Most people would be close to the average height, and fewer people would be very tall or very short. People use the normal curve in statistics to make predictions about the likelihood of events in different fields like quality control, finance, and health research.
 
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