2014年3月8日 · Asset price changes (returns) typically have negative skew - many small price increases with a few large price drops. The skew seems to hold for almost all types of assets: stocks prices, commodity prices, etc. The negative skew can be observed in monthly price changes but is much more evident when you start looking at daily or hourly price ...

2022年2月12日 · I'm aware, that negative skew means long left tail and the mean < median < mode. Also aware, that negative skew is where most values are plotted on the right side of the graph. But the following statement confuses me the most. It is characterized by many small gains and a few extreme losses

2016年11月29日 · Negative Skewness is defined as: The left tail is longer; the mass of the distribution is concentrated on the right of the figure. I'd be inclined to say that's more a description that an actual definition; we'd have to make those concepts more precise to have it really count as the definition of skewness.

2016年12月24日 · $\begingroup$ If you modify your question slightly so that it asks about approaches for generating data with negative skewness (rather than appearing to ask for R code) it is likely to be on topic. However, you'd have to explain more about what characteristics you need (you haven't even indicated whether you need continuous or discrete outcomes ...

$\begingroup$ I understand that it is shape and type of deviation from linearity what matters here, but still it looks odd that both axes are labeled " ... quantiles " and one axis goes as 0.2 0.4 0.6 and the other goes as -2 -1 0 1 2.

2014年5月12日 · Using SAS, I checked for normality, and results showed data to be non-normal (Shapiro–Wilk < 0.05). I then performed residual analysis, which again showed non-normal data. Skewness was -0.42. The reason for the negative skewness was probably because there was a set upper limit (60 min) for the variable measured.

2019年2月1日 · In the case of negative values, you can use the PowerTransformer(method='yeo-johnson') method from sklearn. It is capable of handling positive and negative values, also values of zero. The skewness (measure of normality) of the data should decrease substantially.

2024年4月1日 · Very interesting about the lognormals. I should clarify I don't have a formal notion of niceness in mind. Ideally the condition would include cases with both positive and negative skew, but really anything more parsimonious than just a laundry list of specific distributions would be interesting. $\endgroup$ –

2017年8月4日 · Certainly, since skewness is unaffected by location shift. Since it seems unsusprising to have negative skewness with negative data, try this exercise ---Make yourself a set of data with negative skewness, any way you like-- and by any reasonable measure of skewness you prefer. Small data sets are fine; you'll need at least 3 observations.

But all I that know of follow the same convention that right-skewed and left-skewed typically yield positive and negative results respectively, as for example (mean $-$ median)/SD. The only certain thing, however, is that symmetrical distributions have zero skewness.