In various aspects, the lognormal distribution differs from the normal distribution. The shape of the normal and lognormal distributions differs significantly. The normal distribution is symmetrical, whereas the lognormal distribution is not. A right-skewed curve is created by the positive values in a lognormal distribution.
If the logarithm of a random variable is normally distributed, it is called lognormal. This type of distribution is frequently characterised by skewed distributions with low mean values, high variance, and all-positive values. Because log(x) is only valid for positive x values, the values must be positive.
Load variables are described using the lognormal distribution, whereas resistance variables are described using the normal distribution. A variable that never takes on negative values, on the other hand, is usually given a lognormal distribution rather than a normal distribution.
The most frequent distribution function for independent, randomly produced data is the normal distribution, commonly known as the Gaussian distribution. From survey analysis and quality control to resource allocation, the bell-shaped curve is omnipresent in statistical reports.
There are two parameters in the lognormal distribution: μ and σ. These are not the same as mean and standard deviation, which are discussed in another post, although they do define the distribution and the dependability function.
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plz provides all notes
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plzz provide notes....
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please provide notes also in pdf
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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