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How to distribute date 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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Why do we take the log of data?

Taking log brings the values on to a comparable scale and brings in linearity to the values. Compare the right-hand graph which is on the Logarithmic scale with the left-hand graph which has original Income values on Y-axis. The right graph tends to be more linear. Logarithms help us to handle non-linear data.

When and why should you take the log of a distribution of numbers )?

When you care about relative changes, use the log-scale; when you care about absolute changes, use linear-scale. This is true for distributions, but also for any quantity or changes in quantities.

When should log transformation be used?

The log transformation is often used to reduce skewness of a measurement variable. If, after transformation, the distribution is symmetric, then the Welch t-test might be used to compare groups. If, also, the distribution becomes close to normal, then a reference interval might be determined.

Why do we use log distributions?

The log transformation is, arguably, the most popular among the different types of transformations used to transform skewed data to approximately conform to normality. If the original data follows a log-normal distribution or approximately so, then the log-transformed data follows a normal or near normal distribution.

When would you transform a dataset using the log transformation?

The log transformation can be used to make highly skewed distributions less skewed. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics.

Which variables should I log transform?

Only independent/predictor variable(s) is log-transformed. This tells us that a 1% increase in the independent variable increases (or decreases) the dependent variable by (coefficient/100) units. Example: the coefficient is 0.198. 0.198/100 = 0.00198.

Why would you do a log transformation?

The log transformation is, arguably, the most popular among the different types of transformations used to transform skewed data to approximately conform to normality. If the original data follows a log-normal distribution or approximately so, then the log-transformed data follows a normal or near normal distribution.

Does log change distribution?

If the original data does follow a log-normal distribution, the log-transformed data will follow or approximately follow the normal distribution. However, in general there is no guarantee that the log-transformation will reduce skewness and make the data a better approximation of the normal distribution.

When should you log transform data?

When our original continuous data do not follow the bell curve, we can log transform this data to make it as normal as possible so that the statistical analysis results from this data become more valid . In other words, the log transformation reduces or removes the skewness of our original data.

Why do we use log in time series?

Log transforms are popular with time series data as they are effective at removing exponential variance. Where transform is the transformed series, constant is a fixed value that lifts all observations above zero, and x is the time series.

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