**569 normality-assumption questions.**

If a probability distribution has, say, 112 bins with around 29000 samples, with the maximum probability of a bin being less than 0.05, is the Jarque-Bera test an effective measure of conformance to a ...

I ran an between-subjects repeated measures (2*2*2) ANOVA in SPSS using GLM.
One of my dependent variables didn't meet the Test of Normality (Shapiro-Wilk p = 0.047) according to the table. The Q-Q ...

I am confused about the normality assumption in repeated measures ANOVA. Specifically, I am wondering what kind of normality exactly should be satisfied. In reading the literature and the answers on ...

I try the shapiro.test for my transformed dataset (logarithm). I obtain p value 0,0001207. I try to draw the graph of distribution and obtain this graph (I attached).
For you, do I have a normal ...

When is testing for normality necessary for machine learning with Big Data? Please give examples or counter examples.

A paper mentioned of using KS on 3 groups of six each, without details. My question: should KS for normality be tested once on all 18 samples, or on each of the groups of six. That is: should one ...

I get a little bit confused by the conclusions I can take or not with these small samples.
I have been measuring the degradation of a pollutant for 9 days. I measured the remaining concentration and ...

I have read somewhere in the literature that the Shapiro–Wilk test is considered to be the best normality test because for a given significance level, $\alpha$, the probability of rejecting the null ...

Background
I have two conditions: A and B, with around 400 measurement points for each condition.
The most common result for both conditions is around '700', and neither one has a result below '600'....

I would like to conduct a paired sample t test and thus, I'm checking for the assumptions of normality. Upon conducting normality testing, each group scores were found to be normally distributed ...

I just would like to understand some information about the joint normality and the margins. I read that the normal joint distribution almost always implies that the univariate margins are all normal.
...

Suppose that we are given a data. Then, one needs to select the best fits models that approximate the distribution of the data. The most widely used mixture model is a Gaussian mixture model. In this ...

I am doing exercise on Kaggle House Prising and I cannot understand something.
I watch and read articles for Normality test and more specifically JB test but I cannot understand why according to my ...

Is it mandatory to check for Multicollinearity and Normality in the independent variables for all types of Machine Learning Algorithms ?

i have several log-transformed continuous variables in my model and want to estimate their impact on likelihood of sale. can i include (natural) log transformed variables in a probit model? if so how ...

I am running a kstest on MATLAB. When I take the data directly, i.e. kstest(data), the result says that my data is non normal. ...

As a neuropsychology graduate student with some experience in statistics (I'm usually the guy other psychologists come to with statistics problems after trying it themselves but before seeing a ...

I have an unbalanced design with two factors, gender and site. I'm trying to test for differences in a size variable. Levene's test reveals that the data has equal variances. However, log transformed ...

So I was transforming data for machine learning purposes and checking whether I should use the data or log-transform it. In addition to creating histograms I decided to test for normality using ...

I have done so many research and have read so many posts and manuscripts to find an answer to my question but I'm getting more and more confused. So I found it best to ask my question directly.
As we ...

![Descriptive Statistics
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I have recorded the electrical response at a specific brain region by applying pulses to the region of interest. The electrical stimulation was applied in ...

So, there is the question: is there any statistical methods exist to apply normality test (hypotesis check) for grouped data?
For example, take a look at data frame:
...

I am performing a generalized linear model, where I have to specify a family different from the normal one.
What is the expected distribution of residuals?
For example, should the residuals be ...

I'm in a field that is overly concerned with transforming non-normal variables in an attempt to make them normal. However, it's also generally recognized that the standard transformations (e.g. log, ...

I have a paired sample data with n=21. The two values V1 and V2 are from two different time points for the same group. I want to see if there is any improvement/reduction in values at time 2(V2). Of ...

I was reading this article, where the author says that
Maximum Likelihood (ML) estimates are asymptotically normal if
the log-likelihood is asymptotically quadratic.
I have heard or read other ...

I'm building an Error Correction Model using the Engle-Granger approach with the following interest rates data:
Observations: 230
Periodicity: Monthly
I have the following model:
$$\Delta R_t = \...

I think I understood that normality of residuals may not be a problem if the sample is large enough (cf, here).
My question is: Would my sample be large enough to be analysed using a probit and an ...

I have a dataset that contains a range of values. I have created a frequency distribution of the values, and have included the plot below. To my untrained eye, it appears that the frequency ...

I want to compare four different groups on one dependent variable. Normally I'd do a one-way independent ANOVA, except that this time the normality assumption isn't met at all (see the below ...

I am considering this test for univariate normality which I believe, but not sure, may be just like the Q-Q test.
The procedure is like this:
We compute sample mean, standard error (S.E)
Then ...

Best-subset regression analysis:
I want to test effects of differents ecological variables on my response variable. I am working with function glmulti() of glmulti ...

My question might sound strange but this is the situation I'm dealing with!
I have a dataset, consisting of 4 data series, each a measurement of a parameter of a biological sample. We have 31 samples. ...

How to perform the analysis of normality of residuals and homogeneity of variances in a split plot ANOVA ? In this model we have 2 residuals, the assumptions are tested in the same way like a simple ...

I have some pre post data for an extremely small sample (n=5). Outcome is continuous and no control. What statistics would be more appropriate to report? Accordingly, is a paired t-test or a wilcoxon ...

In my study twenty subjects experienced all four levels of both factors (4x4 design). The residuals for my DVs were not normally distributed as would be required for the two-way repeated measures ...

I am new to statistics and looking at some plots of residuals normality.
Could someone help me out?
Is there normality in my residuals?

I need to run hundreds of linear regression models, with the same set of independent variables, but with varying dependent variables. I have checked normality for a few dozens. Some are normally ...

I have 1 binary dependent variable, 2 independent categorical variables and 1 ratio predictor which I centered. I have 4 H0s, testing first each IV separately on the DV (H1, H2), then both IVs on the ...

It looks approximately normal to me. But it also kind of seems to be a little bit skewed cause of the points popping out between -3 and -1. This QQ plot is for an anova, but does the central limit ...

In the field I work in, there is a large amount of impetus to use Shapiro-Wilk testing as the default normality test (possibly due to NIST and some pubmed papers). I understand that one weakness of SW ...

I have often read that thanks to the CLT, the residuals of a model are asymptotically normal. This argument always seemed odd to me since CLT states that
The sum of a number of independent and ...

Why is the F-test for difference in variance so sensitive to the assumption of normal distribution, even for large $N$?
I have tried to search the web and visited the library, but none of it gave any ...

I'm working on an algorithm that relies on the fact that observations $Y$s are normally distributed, and I would like to test the robustness of the algorithm to this assumption empirically.
To do ...

The ends of these graphs confuse me. I know most of the values fall on or near the line. But I am unsure of whether the data is indeed approximately normal. These are the two graphs.
Plot 1:
Plot ...

I investigated dataset using histogram and normaltest. I used scipy.stats.normaltest, got this result:
...

If there is a high correlation between two random variables $X$ and $Y$ (dependent), does that mean that they are normally distributed? Confusion i have is that both variables can be exponential in ...

I found by accident the nonparametric Theil-Sen Estimator as a replacement for standard OLS linear Regression. How well does it perform with autocorrelated data, non-normal residuals and ...

I have continuous data (latency to perform a behaviour) that is heteroscedastic and also the data and the residuals are not normally dsitributed.
I've tried
square root
$\frac{1}{log}$ and
log10 ...

I am unsure about how to analyze my data as they are quite non-normally distributed, which causes model residuals to be also very non-normally distributed. I have seen there are various threads on non-...

- normal-distribution
- regression
- anova
- hypothesis-testing
- t-test
- residuals
- assumptions
- data-transformation
- r
- distributions
- heteroscedasticity
- qq-plot
- spss
- self-study
- multiple-regression
- nonparametric
- skewness
- kolmogorov-smirnov
- statistical-significance
- goodness-of-fit
- central-limit-theorem
- repeated-measures
- time-series
- wilcoxon-mann-whitney
- small-sample

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