Questions tagged [skewness]

Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.

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Is normal distribution better than skewed distribution for machine learning input features? [closed]

Distribution of a particular feature in my ML dataset is skewed as shown below. Log of this feature looks like a normal distribution. Can the latter distribution offer better predictability in a ...
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Dependent value largely right skewed. What are my options?

I am modeling a dependent variable which is heavily right skewed by a large number of independant variables. This variable is integer. But let's assume this is our model. $ Y = a_0 X_0 + a_1 X_1 + b_0$...
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How do I solve skewness in a percentage difference calculation?

I have two variables I want to evaluate -- Planned Hours per project and Tracked Hours per project. Sometimes Planned Hours exceeds Tracked Hours, but often it's the reverse. I calculated the ...
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Can you help me with distribution/Skewness

Can you tell me if the data normal distributed are? It looks a bit strange. I would say it is skewed 40,80 and so on are sizes of the Apartments in m^2
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Are there possibilities to determine 95% confidence interval for right skewed data?

The dataset that I'm using for my thesis is right skewed. It is about lead times (days) I tried log10 transforming it in SPSS but it still does not meet the requirement p>0,05 (Shapiro-wilk). So ...
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How to build a regression equation for a gamma GzLM and how to interpret it?

I am trying to analyze if referral programs (1/0) have an impact on the average monthly spending of a user. I am confident that the gamma GzLM is the best model for my distribution: According to ...
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How to handle a highly right skewed dataset without 'hurting' it too much?

I have a freemium mobile app usage dataset. I want to perform a regression with the average spending per month (continuous) as a dependent, and if they came via referral program (binary) as the ...
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Please help: Balancing “skewed” responses to compare 2 groups using 4-pt. Likert

Ok, “Statistical community”, I need help. But, WARNING: Information below is wordy & probably confusing… I have ZERO statistical experience but science background & a bit of commen sense. ...
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Finding an optimal distribution to fit to a highly skewed data vector (DV with missing values) in R

I hope that this question has not already been asked. I am analyzing data in R (and am a novice). I have a highly skewed data vector in a dataframe with missing values that I hope to set as the ...
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Should I change the dataset to one with less zeros on the independent variable?

I'm running a spatial econometrics model and one of my explanatory variables is sewage coverage, which is 0 for 42% of the cities in the country of analysis. I've been told to reduce the focus of the ...
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Detecting Mean differences on streaming data

Im taking online measurements on highly skewed real valued data of two possibly unrelated distributions. The precision is low, which means small values tend to come in duplicates, while large values ...
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How do I remove skewness from a distribution?

I am working with the famous Credit Card Fraud Detection dataset which includes 28 PCA transformed columns. I'm dealing with the most skewed feature of all which after running the following snippet of ...
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Correct parameterization of a Unified Skew Normal (SUN) distribution for the maximum of correlated normal random variables

I have a vector of random variables $X=(X_1, X_2, ..., X_n)$ that follows a multivariate normal distribution with a vector of means $\mu$ and covariance matrix $\Sigma$. I am interested to find the ...
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How does skewness in the independent features affect the performance of classification algorithms?

Do we need to take action when we encounter a skewed feature (not the target)? We can log transform for regression etc., but does it matter in also classification models?
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Transforming skewed distribution of dependent variable in linear regression?

As I understand it, the skewness of the response variable in a linear regression does not need to be normal (only the residuals need to be normally distributed). However, I was generally wondering if ...
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Are there distributions for skewness and kurtosis? Similarly to mean (normal) and variance (chi-squared)

My question is really straightforward. The distribution of the sample means approaches a normal distribution (CLT). The distribution of the sample variance approaches a chi-square distribution (...
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Meaning of asymmetric third moment skewness

Given the formula of the third moment that define the skewness: $$skewness=E\Bigg[\bigg(\frac{x_i−\bar{X}}{σ}\bigg)^3 \Bigg] = \frac{\mu_3}{\sigma^3} $$ I understand that this formula calculates the ...
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Different methods to interpret skewness

I am trying to analyze if my data is skewed or not, since I am planning to compute the median and the interquartile range over the mean and the standard deviation if my data shows a substantial skew. ...
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Small Sample - Confidence Interval Mean

I have a sample size of 14 and I plan to measure confidence intervals for the mean of different characteristics. I can say this about my data: ...
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Skewed - Unknown Distribution - Median Confidence Interval [duplicate]

I am trying to compute the confidence interval of the median for my data. Sample size: 14 Skewed Unknown Distribution To my understanding, I can use non-parametric ...
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Skewed continous data & Linear Regression

I'm new to statistics. I created a data set of around 10000 observations and wanted to examine the relation between a variable A with continuous values between 0 and 10 to another B which can assume ...
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Sample skewness consistency

Is sample skewness a consistent estimator of (moment coefficient) skewness? I have ran some experiments and it seems this is not the case, but I cannot find any definite answer online. Here is an ...
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Geometry of Fisher-Pearson standardized moment

I'm studying the skewness of data distribution then the formula for measuring skewness is below: $$\tilde\mu_3 = \frac{\sum_i^N(x_i-\bar X)^3}{(N-1)(\sigma^3)}$$ I would like to understand if it is ...
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Negative skew - Stock returns - Clarification

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 ...
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How to account for weights of a skewed dataset in a machine learning problem?

I, a novice, have a dataset which I would like to use for multiclass classification. I know that the data is skewed, but luckily, my dataset contains an observation weights column. The observation ...
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Under what conditions are there pairwise monotonic relationships between mean, variance, and (positive) skewness of a lower-bounded distribution?

I am dealing with empirical data, integer- and continuous-valued, with a lower bound (at zero) that are often positively skewed, and seem to be following either the Poisson, $\chi^2$, binomial, or ...
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Mean substitution - skewness and kurtosis

I am doing the review questions from Thompson: foundations of behavioral statistics, chapter 4, question 2. I cannot seem to conceptualize the correct answer. Would appreciate any help as I need to ...
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Best way to code ratio of two skewed, heavy-tailed continuous variables with zero values present in denominator?

I'm modeling the ratio of total medical cost in one 12 month period and the subsequent 12 month period (total_medical_cost_ratio = post_total_medical_cost/pre_total_medical_cost). The data is skewed, ...
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Risks for the Coefficient of Variation when using very skewed (log-transformed) data

I have many datasets which I have normalized using log-transformation, however for some datasets the log-transformation did not improve the skewness that good (still close to 1). For these datasets, I ...
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How can I quantify the skewness, kurtosis, entropy when all of values of a list is 0?

Background Let's think, there is a list of values which presents activity of a person for several hours. That person did not have any movement in those hours. Therefore, all the values are 0. Then, ...
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Question about sample normalization for distribution moments [duplicate]

I am having trouble understanding how the sample formulas for distribution moments are derived, for example, the third standardized central moment is: $$ \frac{1}{n}\frac{\sum{(x - \mu)^3}}{\sigma^3} $...
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How do we determine if the data has to be transformed to reduce skewness? Visually or metrics?

I am wondering what would be the normal way for a data scientist to validate if the data is skewed or not. Is it by plotting the histogram or by finding skewness/kurtosis value (ex:- using pandas ...
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QQ-Plot for data on skewed t distribution in R

I have been trying to plot my financial data against a skewed t distribution in a QQ plot in R for some time now, but I have only managed to do it against a symmetric t distribution so far. I use the ...
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Hypothesis Testing with Skewed Large Data, Beginner

I am interested in learning more about hypothesis testing with skewed distributions and making sure my statistical power is strong. I have looked into non-parametric tests (the common ones) however it ...
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What does the skewness, when converted to the units of the data, represent?

I have calculated the skewness of my data. I was wondering what the skewness, when converted to the units of the data, represents in non-mathematical (biological?) terms? For instance, I know that the ...
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derivation of limitation of karl pearsons coefficient of skewness [duplicate]

can someone help me with the derivation of limitation of karl pearson's coefficient of skewness i.e ...
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Interpret the skewness in R

I have a skewness(x) equal to [1] 0.51308. Does this mean the graph is positively skewed or it is said to be symmetric? My ...
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Does skewness decrease standard deviation ceteris paribus?

For a given probability distribution, probability mass must sum to 1, thus by increasing a parameter corresponding to skewness do you shift probability away from the second central moment (variance) ...
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Wilcoxon or T-test, paired, or unpaired? large data set, almost 1/2 are zeroes for habitat suitability values

Background I am just learning how to work with Spatial Distribution Models, these are basically regressions on environmental values and occurrence coordinates for a species. The result is a grid map ...
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Should I do fix skewness of the data before feature selection or after feature selection and choosing the best model using AIC/BIC?

So, I am trying to develop a linear regression model and I want to know whether it is a good decision to fix the skewness of the data before selecting the best features for the model or should I use ...
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Do the robust standard errors in GEE also protect you from a skewed errors

I have a numeric outcome with repeated measures on individuals and several predictors. The basic analysis is linear regression, but with consideration of random effects or GEE to adjust for the ...
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Different methods for evaluating skewness of circular data

I am trying to find skewness of circular data. As far as I understood, there are different methods for this. For example these methods: Pewsey, Metrika, 2004 (link) Statistical analysis of circular ...
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Would this distribtuion be considered half-normal? [closed]

I'm a bit confused because of the high skewness value.: Edit: By half-normal, I was going by this Wikipedia page: https://en.wikipedia.org/wiki/Half-normal_distribution Maybe I'm mistaken?
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Skewness of Von mises distribution using two different methods

I want to use skewness of circular data using circ_skewness code in MATLAB. I know there are two methods for this: Pewsey, Metrika, 2004 Statistical analysis of circular data, Fisher, p. 34 However,...
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Normality test of data with large skewness and kurtosis

I have a data set and want to know whether it has a normal distribution. I use both Kuiper and K-S normality tests and the null hypothesis is rejected in both tests. This means that the data can have ...
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Is kurtosis and skewness subadditive?

I would like to ask a question regarding the subadditivity of high-order statistics such as Kurtosis and Skewness. Here Kurtosis refers to actual Kurtosis instead of excess Kurtosis. Are they sub-...
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Forecasting a skewed series

I am trying to forecast the following series. The grey line is the forecast and the black line is the actual. I want a similar pattern in the forecast as we se ein the actuals .. for this particular ...
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Calculate skewness in base R

I'm working on my literacy in math notation and statistics in general, using the R language as a means of translating formulas as I read through textbooks and teach myself. I'm trying to write Pearson'...
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How to handle highly skewed percentage data

I am trying to make a prediction model (explanation would be nice but mainly predicting as accurately as possible is more important, for now). My independent variables are all percentages (quantity of ...
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Heteroscedasticity due to skewness of predictor(s)

I read that in a linear model skewness of one of the independent variables can be the reason for heteroscedasticity in the model but I can't think of (or create) an example where this would be the ...
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