query: Why does the log transformation bring data closer to normal distribution?
positive_passages.docid: 146029
positive_passages.text: Does a log transform always bring a distribution closer to normal?
negative_passages.docid: 23922
negative_passages.text: Anscombe transform and normal approximation
negative_passages.docid: 54796
negative_passages.text: Choosing between LM and GLM for a log-transformed response variable
negative_passages.docid: 133192
negative_passages.text: Importance of normal distribution
negative_passages.docid: 127290
negative_passages.text: Intuition behind logarithm change of base
negative_passages.docid: 109585
negative_passages.text: Why Normality assumption in linear regression
negative_passages.docid: 98439
negative_passages.text: In statistics, should I assume $\log$ to mean $\log_{10}$ or the natural logarithm $\ln$?
negative_passages.docid: 27019
negative_passages.text: Test fit of discrete data distribution to normal distribution
negative_passages.docid: 100102
negative_passages.text: Unexpected behaviour of LogLogPlot function
negative_passages.docid: 125080
negative_passages.text: Why is uniform prior on log(x) equal to 1/x prior on x?
negative_passages.docid: 103217
negative_passages.text: Different probability density transformations due to Jacobian factor
negative_passages.docid: 19626
negative_passages.text: Is normality testing 'essentially useless'?
negative_passages.docid: 78828
negative_passages.text: Intuitive explanation for density of transformed variable?
negative_passages.docid: 62481
negative_passages.text: Log or square-root transformation for ARIMA
negative_passages.docid: 91193
negative_passages.text: Natural log approximation
negative_passages.docid: 61468
negative_passages.text: Linear mixed effect model interpretation with log transformed dependent variable
negative_passages.docid: 58620
negative_passages.text: What Ratio of Independent Distributions gives a Normal Distribution?