**585 assumptions questions.**

I am doing bayesian inference on normal data, and wonder how robust are the results with respects to some assumptions and potential mistakes.
Specifically, for simplicity I want to assume that ...

I'm trying to run a 2x3 mixed design ANOVA. Participants were measured on three different domains at two different times. One out of the 6 sets of results violates Levene's test while the rest do not. ...

I read that these are the conditions for using the multiple regression model:
the residuals of the model are nearly normal,
the variability of the residuals is nearly constant
the residuals are ...

I'm looking at a dataset of survival times of animals in two different groups. While there is some early mortality in the first group, the Kaplan-Meier survival curves overlap, the log-rank test and ...

I'm creating my first path analysis model with lavaan (R package). The assumption of multivariate normal distribution, however, is violated. Also, in the regression M1 ~ X1 + X2 (mediator ~ exogenous ...

I have been asked to analyze data from field observations of a fish community.
Data are collected in two sampling-sites during 16 days (nonconsecutive), twice a day. Dependent variable is biomass.
...

There are several threads on this site discussing how to determine if the OLS residuals are asymptotically normally distributed. Another way to evaluate the normality of the residuals with R code is ...

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 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've been struggling to find out what the assumptions of multi-level modelling. The textbook I have starts saying you can ignore homoscedacity and independence now at the beginning of the chapter, and ...

I am having trouble to understand Stable seasonality F-test. I know it is basically the same test as one way ANOVA, but the assumptions for one way ANOVA canĀ“t be met for it. For ANOVA the data should ...

Some asked before "Why do random effect models require the effects to be uncorrelated with the input variables, while fixed effect models allow correlation?"
My question is: how do you check this ...

What are the most relaxed assumptions to get consistency of the linear regression estimates with $p$ variables?
The most basic assumptions that I know are in White (1984):
1) The model is correct
2)...

I have a question about a data analysis that I am running. I am analyzing the results of a survey in which (expectedly) there exists far fewer people in one group than in another. This survey is an ...

I have been trying to find the major assumptions a Canonical Correspondence Analysis makes when doing its analysis. I have had a hard time finding anything useful. I did, however, find the assumptions ...

I am doing my thesis at the moment and I needed to check the BLUE assumptions for my data set. I am doing my thesis on trying to explain IPO underpricing and my data set consists of 100 companies who ...

First a short description of my dataset:
I have 8 independent binary variables with the values 0 and 1 (say something about characteristics of the subject line of an e-mail)
I have 7 dependent ...

I want to perform a 2 sample t-test assuming unequal variances, however my variable is currency. Currency is discrete, however when checking the assumptions of the t-test, I see that the data should ...

I still can't get it. I think that if the assumptions are satisfied, the model is right. But what is the meaning of "right model"?

If I was building a linear mixed-effects model and I changed the variance structure (let's say to a power function) to represent an increasing variance over time points, would the assumption of ...

I'm working with a time series of macroeconomic data (independent variables) and bank loss rates (dependent variable) to show how bank losses vary based on the state of the economy.
To estimate the ...

Main Question
Can you include a dichotomous independent variable in a random slopes & intercepts model? Or does the dichotomous nature of the variable violate any assumptions of random slope GLM ...

Is it possible to perform a t-test to explore differences in the means of two datasets with different units?
One dataset contains ecosystem services measures (units: carbon mass, noise reduction,...) ...

Above is a screenshot from Introduction to Linear Regression by Douglas C. We usually assume that in linear regression model $y$ has a linear relationship with the parameter, but why here the author ...

In response to another question StasK writes:
In multilevel analysis, you have to make strong assumptions: (i) that
your random effects are normal (or, if you have random slopes as long
as ...

I am a student and very new to SPSS, though fully motivated to understand the concept. I have a data set and need to check its stationarity on SPSS. I downloaded the R plugin on SPSS to do it but can ...

My question is very similar to Serially Correlated Regressors.
None of the answers there answer the central point of the question. Is it at all a problem if independent variables, are serially ...

Say I'm using multiple logistic regression to help caterers in a large city predict the probability invited adults will come to a wedding. Say I have a proprietary dataset of likely relevant predictor ...

I just read Demand-Driven Forecasting: A Structured Approach to Forecasting(Wiley and SAS Business Series) and have a few doubts in Holt-Winters Model:
1) Unlike OLS Regression Modeling technique or ...

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 ...

Assesing (with R) the assumptions for normality and homogeneity of my variables for an ANOVA:
Should I run one normality and homogeneity test to each of the 3 subset of variables got after splitting ...

Are there any assumptions that must be covered when fitting an NNETAR model? non-correlation, normality, or something? I've already saw Rob Hyndman post where he says NNETAR doesnĀ“t need stationarity, ...

I recently used multiple linear regression to model monthly species abundance (y) and environmental variables (x) 2005-2016.
To ensure assumptions were satisfied for multiple linear regression I had ...

I'm stuck on some subtleties involved in simple linear regression formulation.
In a very general fashion I understood that the optimal point forecast for a random variable $Y$ (in terms of MSE ...

What is the difference between endogeneity and unobserved heterogeneity? I know that endogeneity comes for example from omitted variables? But as far as I understand, unobserved heterogeneity causes ...

Why is the "sphericity assumption" in RM-ANOVA, i.e. the assumption of constant variance of difference scores, called "sphericity"?
(This question was suggested in the comments to a related question.)...

I've heard (sorry cannot provide a link to a text, something I have been told) that a high positive kurtosis of residuals can be problematic for accurate hypothesis tests and confidence intervals (and ...

The dependent variable in my data can take on the values 0-7 and is, therefore, a discrete variable. I have data from a number of experiments with one, two, and three-way factorial designs.
The ...

The question is from a Master-level Probability Course.
It is well known that the underlying assumption for the binomial distribution is that there are n independent Bernoulli trials. More ...

I am doing a pre/post sentiment analysis based on media journalism covering a recent event. Newspaper articles from New York Times and Washington Post were acquired. The number of negative words were ...

Does the Levene's test have any assumptions? If so, what are they?
To my surprise, neither my textbooks or Wikipedia, mention any assumptions for this test. But I seriously doubt that the test doesn'...

I am confused about the homogeneity of variance assumption in a multilevel model.
I'm developing a linear mixed model with a random intercept, slopes, and residual using SAS PROC MIXED. The data are ...

Forecasting an AR(1)EGARCH(1,1) returns series and just wondering what kind of assumptions we make when we forecast. I have used a normal distribution (as opposed to maybe a student t-distribution ...

What are the usual assumptions for linear regression?
Do they include:
a linear relationship between the independent and dependent variable
independent errors
normal distribution of errors
...

This is a follow-up question I have after reviewing this post: Difference in means statistical test for non-normal, heteroscedastic data?
To be clear, I am asking from a pragmatic perspective (not to ...

I have data on market share. I would like to compare a firm's mean market share before and after an event.
The data is quarterly and I have only been provided with 16 observations, 8 before the ...

The additive assumption means the effect of changes in a predictor on a response is independent of the effect(s) of changes in other predictor(s).
However, with regression, say, with one continuous ...

This isn't a strictly stats question--I can read all the textbooks about ANOVA assumptions--I'm trying to figure out how actual working analysts handle data that doesn't quite meet the assumptions. I'...

I would like to build a model where:
1) My dependent variable is an ordinal variable with 7 or 8 levels
(sadly, I know for a fact, that the intervals between them are not equal, because it is a ...

As a consequence of the central limit theorem the sampling distribution of the sample means will always be normal whatever is the distribution of the variable we measure.
From our sample we can ...

- regression
- anova
- normality-assumption
- residuals
- heteroscedasticity
- r
- multiple-regression
- hypothesis-testing
- normal-distribution
- t-test
- logistic
- linear-model
- independence
- least-squares
- ancova
- generalized-linear-model
- time-series
- repeated-measures
- data-transformation
- cox-model
- spss
- mixed-model
- chi-squared
- survival
- nonparametric

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