Normal versus positively skewed data
A normal model is symmetric, whereas many positive measurements have a long right tail. A skewed histogram alone does not identify a unique probability distribution.
Probability & statistics / English
Log normal distribution | Math, Statistics for data science, machine learning
codebasics · YouTube · 6:44
YouTube · codebasicsOriginal video
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Introduce the log-normal model by comparing positive skewed measurements with their logarithms.
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0:00Normal versus positively skewed data1:30The log-normal definition6:00A transformation to assessLearning script
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A normal model is symmetric, whereas many positive measurements have a long right tail. A skewed histogram alone does not identify a unique probability distribution.
A positive variable X is log-normal when ln X follows a normal distribution. Logarithms turn multiplicative ratios into additive differences.
The income example compares the original scale with a logarithmic view. Taking logs may make a suitable positive feature easier to model, but does not guarantee normality or improved prediction; assess the transformed data.
Knowledge cards
The log-normal definition
A positive variable X is log-normal when ln X follows a normal distribution. Logarithms turn multiplicative ratios into additive differences.
A transformation to assess
The income example compares the original scale with a logarithmic view. Taking logs may make a suitable positive feature easier to model, but does not guarantee normality or improved prediction; assess the transformed data.
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- Normal distribution ApplicationAt 0:00
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A normal model is symmetric, whereas many positive measurements have a long right tail. A skewed histogram alone does not identify a unique probability distribution. A positive variable X is log-normal when ln X follows a normal distribution. Logarithms turn multiplicative ratios into additive differences. The income example compares the original scale with a logarithmic view. Taking logs may make a suitable positive feature easier to model, but does not guarantee normality or improved prediction; assess the transformed data.
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