Questions tagged [skewness]
Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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Which model for highly skewed data
The response variable in the dataset is highly skewed with a "ceiling effect". The errors of a fitted regression model, will thus also be skewed. I tried to fit a regression but as expected ...
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Can you do a log transformation for excess kurtosis, or is that mainly used for skewness?
I am planning on doing a regression analysis on STATA on the financial performance of private equity funds. On my descriptive statistics, I saw higher levels of kurtosis and skewness. I decreased ...
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Should I transform my positively skewed predictor in hierarchical regression?
I'm doing a hierarchical regression trying to understand how intelligence (first predictor) and personality traits (second predictor) influence general knowledge (dependent variable). The problem is ...
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Is "skewing the data" and "skewing the results" just selection bias?
I recall various conversations with biologists, ecologists, and foresters that I neglected to ask for clarification on at the time. It doesn't occur in any of my statistics references.
Sometimes in ...
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How to test overall skewness / normality in a large data set of ordinal data in R?
I want to test overall skewness / normality in a large data set of ordinal data from survey questions and was wondering how this can be done?
(I couldn’t use Shapiro wilk as I received an error saying ...
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Centering a skewed predictor variable in a multilevel model that is involved in an 2-way interaction
Is there a correct method to center (mean or median centering) a skewed predictor variable in a multilevel model? The predictor is a skewed, count variable and will feature in a two-way interaction ...
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In a skewed sample with a large n, does Central Limit Theorem dictate that a t-test can be used, even if the mean cannot be interpreted? [duplicate]
I understand that, in the case of a highly skewed population and sample, the sampling distribution of the mean can still be normally distributed if the sample size is large, according to Central Limit ...
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Fixed Effects Regression, weird Residual Plots
I am running regressions analysing drought impacts on electricity generation based on fueltype of the generator. I transform the outcome variable (daily_generation) using an inverse hyperbolic sine ...
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Increasing probability of middle-large values decreases skewness more than large values?
I found this counterintuitive result and want to check with you folks if I did not make any mistakes.
Imagine a simple uniform distribution, of all values between 1 to 100. I would expect that ...
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Differences in Pearson skewness coefficients [duplicate]
I downloaded this and that file. Then, I created histograms showing the daily cost and annual expenses per country. We observe that the histogram of cost21 is more symmetrical than the histogram of ...
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Negative Coefficients for Interaction Term in Dichotomous Variables
I've come across an issue while analyzing some data and haven't been able to find a similar question on the site.
I'm working with data concerning the impact of certain genes and smoking on the ...
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How do I transform an extremely skewed distribution to use it for linear regression?
I'm currently working on a data set where the goal is to predict the number of rented bikes in Seoul, given information about the weather at the time.
The data set can be downloaded here: https://...
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Influence of Large Data Size on Logistic Regression Fit and McFadden's $R^2$
I am currently working on a logistic regression analysis and have encountered a situation where I have approximately 16 million data points. I am interested in understanding the influence of such a ...
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Can positive values with sd > mean have skewness = 0?
I'm trying to create an example of a distribution with all positive values, standard deviation > mean, and skewness =0 (third moment). I cannot. Is that possible? Can you prove it mathematically? ...
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Comparing probability distributions from the same ML algorithm
Background: We are using a research tool that uses computer vision to quantify facial expressions (ie whether someone is smiling) from a webcam. The raw output for this tool is time series data ...
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Log-likelihood skew-t
I am trying to write down the log-likelihood for the multivariate skewed student-t distribution, but I don't really get how to define it exactly. Could someone please tell me the definition as in how ...
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Can I do skewness on multiple standard deviation?
I have a 1000 sample of an electrical test at each 4 different time, so I do simple descriptive statistic to obtain standard deviation at each 4 different time. Can I then use skewness on standard ...
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Meaning of Skewness and Kurtosis values of Residual Errors in Time Series Forecasting Problem using LSTM
I have developed different kinds of RNNs (such as LSTM,GRU etc.)to predict future values of thermocouple measurements. The residual errors look like they do not follow normal distribution, so I wanted ...
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Cut off value of +/- 1.5 for Skewness and Kurtosis (Tabachnick & Fidell)
I've read multiple posts/papers citing Tabachnick and Fidell's cut off of +/- 1.5 as the acceptable range for skewness and kurtosis to determine normality; however, I cannot find it in their book. Can ...
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Hazard ratio and skewness
Suppose the distribution $F$ is absolutely continuous. Is there a way to compare (i) the monotonocity of the hazard ratio (i.e., strictly decreasing/increasing); (ii) the skewness of the distribution (...
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Power analysis to detect non-zero skew/kurtosis
Tests exist to determine whether a distribution is normal. For example the Shapiro-Wilk’s method. I'm wondering how to determine whether I'm powered to detect that my distribution is non-normal (e.g., ...
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What is the proper way to define skew distribution?
From What's the skewed-t distribution? there seems to be multiple way of defining skew distributions. However I am not sure if these methods are equivalent
The original questions show methods from
C. ...
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Non-parametric bootstrap for 95%CI calculation in stratified sample in R
I am estimating the population mean of the 2023 value of cars from a stratified sample. The value of the cars is right skewed on visual inspection, and some basic diagnostics indicate normality ...
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t-test on non normal data: type I/II error vs validity
First, I don't believe this is a duplicate post even though this topic has been brought up a million times. If it is, please point me to the relevant post and I will remove this one.
I am basically ...
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Can the ROC curve of 2 different data sets be same
I have been learning about ROC curves and had a doubt that can the ROC of 2 completely different data sets with different skew ( P/(P + N) where P and ...
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Using statistics to find significant relations between fishmigration movements and acidity in water
I am studying relations between migration movements of different types of fish and acidity (pH) of water. I am stuck on which statistical test i should use to find those significant correlations and/...
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Simulating Outcomes for Skewed Data
I am looking to simulate the results for MLB hitters in terms of their FanDuel and DraftKings fantasy scores. I'm wondering if this is doable given only the following information per player:
...
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Linear fit when distribution of errors is highly skewed
I have some datasets where the distribution of errors is expected to be highly skewed. I'd like to do a linear fit that takes this into account.
Here is some synthetic data that shows this:
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Skewness and log transformation and meta analysis
I am using 3 papers for a mini meta analysis using data (mean and SD) of both the experimental and control groups.
Using the means I have made 2 histograms displaying distribution. Only one of them ...
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How would I run an A/B test if the observations are very skewed? [duplicate]
I am looking to run an A/B Test testing the effectiveness of a promotion on revenue but I am concerned about the pre-period skewness of my customer spend. I am splitting my customers into 2 groups of ...
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I have a skewed dependent variable which is mostly (but not entirely) in integers. Is a negative binomial model suitable?
I have read that for highly skewed dependent variables (e.g., with excessive minimum values), a negative binomial model with robust standard errors may be best, even if the measure is not all integers....
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A question about the skewness of a compound Poisson distribution
I'm trying to intuitively answer the last part of item b of question 21 of chapter 9 (Mixtures and Compound Distributions) of the book Probability: the science of uncertainty with applications to ...
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Why use the bootstrap for a skewed distribution when you can use a transform?
Let's say you are working with a statistic (say, the mean of the population) of a skewed distribution with a long, long tail such that confidence intervals must be very skewed to achieve reasonable ...
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Why use the bootstrap for a skewed statistic when you can use a transform?
Let's say you are working with a statistic (say, the mean of the population) of a skewed distribution with a long, long tail such that confidence intervals must be very skewed to achieve reasonable ...
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Sum of medians or median of sums
I am estimating the total consumption of a community of species.
I have a dataset of total consumption for a given species based on population density and average energetic needs. This is related to ...
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Correcting for sample size in skewed data
Empirically I have noticed that when data is skewed, and when for example interested in the 99th percentile of latencies, that it is difficult to compare experiments with different sample sizes
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Transform a sample to have target values for the first 3 moments (Equating moments)
I want to transform the first three moments of a sample X of size N to get a new sample Y with moments ($\mu_Y, \sigma_Y, \nu_Y$). The first two moments can be mapped to the target values using the ...
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What are consequences of forming clusters on variables that are not normally distributed?
I am trying to use DBSCAN to obtain clusters of chess player rating changes (Elo rank) over one year of games.
I have a bunch of input variables, some of which are not normally distributed even if I ...
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Logistic regression via robust glm (glmrob) not appropriate if have only one observation in one of two categories of an independent variable?
I received abnormal results when using glmrob (R function from robustbase) when assessing the association of a binomial independent variable X0 with a binomial dependent variable Y using logistic ...
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Should variables be dropped according to its skewness values?
I am creating a classification model to predict the credit score of a person based on lots of factors. I got the dataset from kaggle. When I started doing the EDA part, I noticed that the skewness ...
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How to generated skewed distribution with specific means and variances (in R)?
For teaching purposes I'm trying to generate some probability distributions that have varying amounts of skew but precisely controllable mean and variance. I'd like to plot these distributions and ...
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What is the skewness of the difference between two binomial random variables?
I have two binomial random variables:
$$
X_1 \sim \text{Binom}(n_1,p_1), \\[6pt]
X_2 \sim \text{Binom}(n_2,p_2).
$$
I know that the individual skewnesses are:
$$
\mathbb{Skew}(X_1) = \frac{1 - 2p_1}{\...
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Do I need to use BCa intervals for this hypothesis test? If so, why?
I'm looking at claims costs of policies in an insurance dataset of about 30,000 observations, and I want to know if a small subgroup of the dataset have abnormal costs relative to the average of the ...
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Transformation function on random variable to match desired CV and Skewness
Let's say I have a continuous random variable (X) that takes any value greater than 0 and with certain Mean, CV, and Skewness values. The data can follow any distribution. I want to apply a ...
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Is a Z value of -4.703 statistically signficant? [closed]
I was wondering, i have a Z value of -4.7 is this a significant positive skew based on a 1.96 cut off?
And for Z value of Kurtosis of -0.16 is this proving its non significant as its less than the 1....
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Normality tests for latent variable in probit regression
I am performing a probit regression where the latent variable y* is conceptually important. I already have the model defined with regressors: categorical variables, quadratic terms, continuous ...
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Sampling from extremely skewed data in python for beginner
I have an extremely skewed data with these info:
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Kurtosis is greater or equal to square of skewness plus one [duplicate]
Given is a random variable $X$ with finite fourth moment. Let $\gamma_3$ and $\gamma_4$ denote its skewness and kurtosis respectively.
I want to prove that $$\gamma_4\geq 1+ \gamma_3^2$$
I have seen a ...
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How to deal with high skewness and kurtosis
I have two dependent variables (soccer dataset) that I'm interested in. They have the following skewness and kurtosis:
Variable A: % of minutes played --> Skewness: 0.145 | Kurtosis: -1.03
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Intuition for median and mean with skewness [duplicate]
In my textbook, it states the following about skewness.
The relative position of the median to the mean of a distribution can be identified by the skewness or vice versa. If the skewness is positive, ...
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