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A Q–Q plot (or quantile quantile plot) is a scatterplot of the quantiles of two distributions. Q–Q plots are useful for comparing distributions.

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How do I choose a good model for this analysis? I'm going to describe the scenario below, and outline several options I have brainstormed. First, the scenario: I have data from 50 technicians that ...
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A collaborator analyzed some data with a one-way ANOVA. But when I looked at the data, I had this residuals QQ Plot. It doesn't look very normal. But my collaborator went ahead with the ANOVA. I've ...
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I'm having trouble interpreting the diagnostic plots obtained from a gam modeled with family="scat". The data seem to adjust reasonably well to the 45 degree line, but the red reference line ...
Mar's user avatar
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In the context of QQ-plots I encountered two different definitions of the ECDF: The first definition is$$F(x)=\frac{1}{n}\sum_{i=1}^n1_{[X_i,\infty[}(x)$$ and the second definition is $$F(x)=\frac{1}{...
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I am reading the article "The Unit-improved second-degree Lindley distribution: inference and regression modeling" by Emrah Altun and Gauss M. Cordeiro. And I want to replicate one of their ...
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I am currently working on a project that involves evaluating the distribution of several variables, and I am using Q-Q plots as part of the analysis. While I have generated the Q-Q plots for these ...
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I am new to statistics and am seeking guidance on analyzing the effects of earthworms on litter-derived carbon using R. I conducted an experiment to assess the impact of earthworm presence (with three ...
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I am new to applying the machine learning models. I have to find a correlation between 1 continuous dependent variable and 27 continuous independent variables. In the beginning, I was confused about ...
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Conceptually, I am having a hard time as to why we consider the quantile-quantile plot for linear regression diagonistics, and I cannot seem to get a clear answer after searching extensively. The ...
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I recently found myself answering a question on Stack Overflow about adjusting a dataset to a unknown distribution. Adding my two cents to the community, I have provided a script to draw a Q-Q Plot in ...
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Let's say I'm going to do an analysis where my response variable has a gamma distribution. I perform the analysis pointing to the distribution in my model (eg. using the lme4 package, m1<-glmer(Y~...
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I'm learning some basic EDA using the Boston housing price dataset and I want to filter out outliers in the feature columns. To do that I first wanted to understand what distribution each of my ...
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I'm struggling with the idea of residuals and error terms in GLMs. I've gathered that there are no explicit error terms in GLMs because the distributions modelled don't allow the decomposition between ...
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I've read dozens on post on the subject but I cannot figure this out. From what I've gathered, GLMS don't include an error term in their formulation unlike linear models (LM). I was wondering why (or ...
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I have a model in R looking at infectious disease spread on social networks, and I am running into a problem where my data are clearly not normally-distributed when I try to run a linear regression ...
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I am new to r programming and have ran into an odd situation while plotting a QQ plot for studentised residuals with ggplot2. See code and plot below: ...
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I have a gamm that looks to be heavy tailed according to the qqplot so I'd like to account for this. According to this page things like scaled t distributions for heavy tailed data are only available ...
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I am aware that different statistical packages provide Q–Q plots using code or via a black box. For example, minitab with R integration for Q–Q plot from here. I am trying to do this manually via ...
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I have plotted a normal Q-Q Plot and a histogram to check the normality of this set of discrete data. My interpretation is the data are not normally distributed since they do not fall on the linear ...
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I stumbled upon something interesting while attempting to do a log transformation for some data (with zeros) today. It seems that there must be a good reason for this that I'm just not seeing. I'm ...
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I'm trying to build an GLM regression (10k samples and 50 dimensions). I ran an analysis of the dependent variable since the regression has a normality assumption for the dependent variable. The QQ ...
cat's user avatar
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I'm trying to run GWAS pipeline using plink, but the results I got look really off. The QQ-plot of the p-values is far above the diagonal. I'm pretty sure I followed the correct QC process, and the ...
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I have run multiple tests to determine normality on my dataset, but I am unsure which one to adhere to, especially since my histograms, density plots, and QQ plots leave much to be desired in terms of ...
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The distribution is as follows: However the Shapiro-Wilk test yields a p-value of 0.0 and a W statistic of 0.9. There are over 7,000 values in the sample. Note, the quantile values have been ...
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I have an array of over 6,000 data points and am trying to show whether they follow a normal distribution. Statsmodels (the library I'm using to generate plots) gives the option of using a 45-degree ...
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I am doing a statistical test (program used is SPSS). On the basis of distribution and sample size, I have to chose the correct variable analysis. I also have to justify every decision. I have two ...
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My data is collecting deposition of particles from the atmosphere once a month for 11 months at two sites. I am testing to see if my two sites' data are normally distributed so I can determine what T-...
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I have plotted the qqplot of the residuals that my model generates with the python module statsmodel sm.qqplot(data, line ='r') and it looks like this The points are placed on a straight line but ...
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I am calculating a multiple regression with a sample of 128 and I was wondering, what distribution would best describe this residuals qq plot? It seems like a a Poisson-distribution to me, is it ...
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I want to use an ANOVA for my analysis (2x3 design). I can decide if I can safely use parametric tests. The two samples results: Shapiro-Wilk p<.001) and Q-Q plots don't seem to be normally ...
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I'm currently working with multivariate GARCH representations of time-series for financial data using the rmgarch R package. This package in turn uses the well-...
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I'm trying to figure out if my data follows a normal distribution and if it contains outliers. I have plotted the histogram and now I would like to plot the quantile-quantile (Q-Q) plot. My point is, ...
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Continuing from my previous question here. Furthermore, I intend to perform the chi-squared test and plot QQ-plots to test the hypothesis $H_0:\lambda=1$. I do not get to see the actual data though; I ...
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I wish to test whether a large number of observations $X_i$ follows an exponential distribution with parameter $\lambda=1$. I also wish to test this hypothesis exactly, and intend that if the ...
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There seem to be at least two different methods to calculate the theoretical quantiles in a Q-Q plot. In the following, the normal distribution is assumed to be the theoretical distribution. Split ...
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In this paper: https://www.tandfonline.com/doi/pdf/10.1080/02664763.2021.1940109, the authors have two actual datasets (e.g., 59 observations showing continuous annual flood data) and the authors want ...
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Let $X_1,...,X_n\sim X$ be $n$ i.i.d. random variables. I want to to test if they follow a normal distribution, in other words, check if their distribution belongs to the Gaussian family. These are ...
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Can we say that the assumption for linearity is met? I'm confused because the tails are heavy, and deviations have a bow-shaped pattern. Still, I think that the linearity has met because the majority ...
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Hello. Can anyone help me with interpreting these plots? I would like to know what assumptions of the linear model are not being met and what method should be used to fix the problems. I think there ...
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If the dependent variable is discrete ordinal, like 0-10 then an ordered logit or ordered probit is appropriate to use. They are both similar but their interpretation are different and their error is ...
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I am currently exploring ways to check the normality of a given variable in the dataset. Since most algorithms assume a variable's gaussian distribution, it is important to check it. A Q-Q Plot Came ...
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I runned two GLMs using the same dependent and independent variables, but modelling each analysis according a different type of distribution. Then, I compared its AIC values to find what distribution ...
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I use qqnorm to plot my data as the photo attached. Does this plot indicate the data is normal distributed?
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I am trying to figure out if there is a way that we can perform some statistical test to check the interaction between two independent continuous variables and a dependent variable in R. I have three ...
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I'm finding it hard to interpret this plot. Is it skewed, bimodal, or what is it? What do the points lying in the same line and rising suddenly mean? Is it exponential?
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I am looking at this article on theoretical q-q plots and am trying to understand it in its entirety. The part where I get lost is when the author writes: We first find the f-values for alto What do ...
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I am trying to evaluate the normality of the distribution of my model's residuals. I have been using statsmodels.api.qqplot and ...
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I am trying to create a regression model for prediction. I need to generate prediction/confidence intervals for my model. I am trying to decide whether to use a quantile regression or linear ...
Archie's user avatar
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I am trying to make two-sample Q-Q plots in Python. A Python function that is used for calculating quantiles has the option of fitting parameters for the calculation of quantiles. These parameters are ...
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I want to show the confidence envelope for a two sample Q-Q plot in R (or Python). The aim is to use the Q-Q plot to give an indication of whether my two samples are drawn from the same population The ...
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