**541 normality-assumption questions.**

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-...

Why does it say data should be normally distributed for statistical analysis when different test follow its own distribution (i.e. t, Z, F)?
What does normality have to do with this?

Background: It has been shown and widely referenced (applets even exist, etc.) that for even a highly-skewed numeric variable, a sample size of $n\ge{}$30 is often "large enough" for the Central Limit ...

I want to conduct a 2 x 3 x 2 mixed model ANOVA with Group (malus/bonus contract) as a between-subjects factor, time pressure (nine, 12 and 15 seconds) as a within-subjects factor and loss aversion (...

A former colleague once argued to me as follows:
We usually apply normality tests to the results of processes that,
under the null, generate random variables that are only
asymptotically or ...

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 a dataset (N = 158) and the following variables that I am going to put into a regression model:
Y (a continuous dependent variable)
X (a continuous predictor)
Z (a continuous predictor)
GENDER ...

I´m running multiple regression and after using first differences (as the only method which was succesful with correcting autocorrelation) my model has non-normal reasiduals. As you can see on . P-...

For example I want to check if a sample is from a normal distribution, or a different one. It this possible with wilcox.test?

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 have conducted a survey and am now trying to figure out how to analyse the results. I have a series of 6-point Likert items, which I have coded like this:
-3 Strongly disagree
-2 Disagree
-1 ...

The one-way anova assumptions are:
independent and identically distributed variables (or, less precisely, “independent observations”);
independent and identically distributed variables (or, less ...

I have a relatively large dataset (around 1500 data points) comprised by around 90 subjects where each subject is measured 14 times on a likert scale -1 to 5-.
When I analyze the data with the lmer ...

I am a little bit confused on what the assumptions of linear regression are.
So far I checked whether:
all of the explanatory variables correlated linearly with the response variable. (This was the ...

Let $X$ be lognormal with parameters $\mu$ and $\sigma$ (such that log($X$) is Gaussian with mean $\mu$ and $\sigma$).
What is the distribution of $1/X$? (I.e., its "simple" parametric distribution)?

I have some trouble understanding the normality assumptions of the linear model. I have found a wealth of information already, but some of it is contradictory and I couldn't find a definite answer to ...

Let $\mathbf{x}$ be a random vector drawn from $P$. Consider a sample $\{ \mathbf{x}_i \}_{i=1}^n \stackrel{i.i.d.}{\sim} P$. Define $\bar{\mathbf{x}}_n := \frac{1}{n} \sum_{i=1}^n \mathbf{x}_i$, and $...

According to my understanding, to test whether the population mean is equal to a specific value when the variance is unknown, a one-sample t test could be used. This is always valid when the ...

I have already collected my data for my dissertation and as I have many variables I have done a factor analysis to drill down the variables to few factors. Then I have computed the mean of all the ...

Let's see the comparison below
Asymptotic normality is given by CLT for both cases. In MLE case, a variance of $\hat{\theta}$ is in distribution as $\frac{1}{I(\theta)}$, but in OLS case $\sigma^2Q_{...

I work on my thesis and use SPSS to analyze the data.
Because some of my data is not normal distributed, I would like to log-transform the data to see, if this changes the distribution.
I differ ...

I have a sample size of 6.
In such a case, does it make sense to test for normality using the Kolmogorov-Smirnov test? I used SPSS.
I have a very small sample size because it takes time to get each.
...

I have 100 samples from a population with unknown population distribution. Both the population mean and standard deviation are unknown. I want to check if my data comes from a normal distribution. My ...

this is my first post on this forum, so please let me know if you need more details. Would be really glad for some advice!
I have data that I want to model using LME. I tried to find a good fit for ...

I have two question regarding ARIMA modeling.
1) When I work with ARIMA models should I test for the absence of structural breaks? If the answer is yes, what function of R could I use to detect a ...

I have a data set with following structure:a word | number of occurrence of a word in a document | a document id How can I perform a test for normal ...

I have found the following test results:
Source: Link
Could you tell me what does it mean and how to interpret the following quantities: $Pr<W$, $Pr>D$, $Pr>W-Sq$ and $Pr>A-Sq$?
For ...

I am attempting to test some sets of data for normality. I have 64 groups to test. Each group has n=8 samples. [[I am aware of the problems with low n in regard to normality testing]]
My end goal is ...

We assume that $X_i \sim N(μ_1, σ_1^2)$ and $Y_i \sim N(μ_2, σ_2^2)$ [i.e. that they are normal distributions with a finite (or known?) mean and variance] [...]
-- Tamhane & Dunlop, ...

I am a teacher working on a simple research with the aim of studying whether the set of lesson designs I created can improve student performance. I have two groups with 15 students each. I divided ...

I went through all relevant posts on Cross Validated, and there are clearly some that discuss the same question, but for the life of me I cannot reliably figure this out based on the information given ...

I am analyzing buy-and-hold abnormal returns of stocks (dependent variable) using OLS regression. These returns, however, tend to be positively skewed (and are so in my case).
The residuals obtained ...

So I am following this applied statistics class, and we were taught 5/6 tests for outlier detection and normality test, then told to apply these to some datasets. I am quite confused on how to go for ...

I already checked here, but I didn't get my answer.
I have a data of three treatments, A, B and C.
Every treatment has 28 replicates. To check the normality of the data, i'll use shapiro test on the ...

I'm pretty new to statistics and I need your help.
I have a small sample, as follows: H4U
0.269
0.357
0.2
0.221
0.275
0.277
0.253
0.127
0.246...

I'd like to compare the (continuous) responses of two unpaired groups. The end objective is to test whether one of the groups will be 'superior' to the other (either by a mean or a median comparison).
...

I am trying to fit a multiple linear regression (OLS) model with IPO underpricing as dependent variable. As part of my master thesis I would like to analyze the effect of venture capital ...

According to What is the nature of the normality assumption in models for longitudinal data?, longitudinal regression has characteristics which make the usual assumptions more awkward to apply. ...

My data has a within-subjects factor of time (i.e. pre and post training) and a between subjects factor of group (training vs control).
One of my repeated measures DVs looks at proportional accuracy (...

Premise: not very clever in statistics!
Data: I have quantitative data (two variables, A and B) on two small groups of subjects (both N=7); I'm going to perform a T-test to check differences about ...

In regression, the asymptotic normality requires, other than the central limit theorem, observations are independent, and panel data clearly violates this assumption as observations are at least ...

Why does logistic regression not require residuals to be normally distributed and homoscedastic the way linear regression does? Why does this not cause problems for estimating logistic regression ...

Greene (Econometric Analysis), states the defintiion of Asymptotic Efficiency.
My question is, why does this definition contain a reference to normal distribution?
We already know by the central ...

This is most likely a dumb question, but why has the Jarque-Bera test of normality two degrees of freedom?
My initial thought was that the sum of two squared standard normals (i.e., skewness and ...

Problem
I have a time series data set with about 50 observations. I'd like to compute an interval that may contain the next/future value in the time series (the 51st data point). I tried using a 90% ...

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

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