Distribute numbers log easily

Aug 6th, 2022
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How to distribute numbers log

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we need to understand normal distribution before we move on to log normal distribution. here I have peoples highest database and if you plot that on a histogram it looks like a bell curve so if you are aware about normal distribution you know that this shape is called bell curve and this is normal distribution there are many examples of normal distribution in real life such as the test score, employee performance and so on but lets think about peoples income database here most of the people have income around 50 000 here in US but on the higher end you could have people like Jeff Bezos, Elon Musk you know they could be earning a lot more than the regular population so this curve is right skewed actually it looks very different than normal distribution because on the right hand side this tail it kind of never ends you know because people might earn 1 billion 2 billion it could be like the income could be really high whereas if youre thinking about lets say employees performance it

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The lognormal distribution is used to describe load variables, whereas the normal distribution is used to describe resistance variables. However, a variable that is known as never taking on negative values is normally assigned a lognormal distribution rather than a normal distribution.
The lognormal distribution differs from the normal distribution in several ways. A major difference is in its shape: the normal distribution is symmetrical, whereas the lognormal distribution is not. Because the values in a lognormal distribution are positive, they create a right-skewed curve.
Thus, if the random variable X is log-normally distributed, then Y = ln(X) has a normal distribution. Equivalently, if Y has a normal distribution, then the exponential function of Y, X = exp(Y), has a log-normal distribution. A random variable which is log-normally distributed takes only positive real values.
The lognormal Distribution function comes under the Statistical functions in MS Excel, which is one of the most important functions for financial analysis. The lognormal Distribution function is used to calculate the probability or cumulative lognormal distribution for the given value x. Lognormal.
For example, the following phenomenon can all be modeled with a lognormal distribution: Milk production by cows. Lives of industrial units with failure modes that are characterized by fatigue-stress. Amounts of rainfall.
For example, the following phenomenon can all be modeled with a lognormal distribution: Milk production by cows. Lives of industrial units with failure modes that are characterized by fatigue-stress. Amounts of rainfall.
The method is simple: you use the RAND function to generate X ~ N(, ), then compute Y = exp(X). The random variable Y is lognormally distributed with parameters and . This is the standard definition, but notice that the parameters are specified as the mean and standard deviation of X = log(Y).
One key difference between the two is that lognormal distributions contain only positive numbers, whereas normal distribution can contain negative values. Another key difference between the two is the shape of the graph. Normally distributed data forms a symmetric bell-shaped graph, as seen in the previous graphs.
Lognormal distribution of a random variable If X is a random variable and Y=ln(X) is normally distributed, then X is said to be distributed lognormally. Similarly, if Y has a normal distribution, then the exponential function of Y will be having a lognormal distribution, i.e. X=exp(Y).
Lognormal distribution formulas Mean of the lognormal distribution: exp( + / 2) Median of the lognormal distribution: exp() Mode of the lognormal distribution: exp( - ) Variance of the lognormal distribution: [exp() - 1] exp(2 + ) Skewness of the lognormal distribution: [exp() + 2] [exp() - 1]

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