Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques require the assumption that the data are randomly sampled in some way. Theoretical results about estimators usually require assumptions about the data generating ...

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1answer
18 views

R - Test for homogeneity of regression slopes results in singular model

I am trying to check the assumptions of a two-way ANCOVA. So in my model I have two factors (F1, F2) one dummy coded two level covariate (C) one dependent variable (D) In order to check the ...
0
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0answers
18 views

How to check discriminant analysis assumption in R using lda?

I want to use lda in MASS package in R. According to the theory behind that, first need to validate the assumption. Actually I've found some example from the net but they did not bother to validate ...
4
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1answer
52 views

2x2 ANOVA - assess violations of homoscedasticity & normality

I have a 2x2 factorial unbalanced between-subject design, n = 355. My DV is a subjective probability estimate (i.e., a number between 0 and 100). My ANOVA model: ...
4
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1answer
124 views

Heteroskedasticity in residuals vs. fitted plot

I am testing whether price per ounce of beer (continuous variable, range of values mostly between 0.1 and 0.5 dollars) and the presence of promotion, advertisement, and display (all binary) have ...
9
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1answer
254 views

Are normally distributed X and Y more likely to result in normally distributed residuals?

Here the misinterpretation of the assumption of normality in linear regression is discussed (that the 'normality' refers the the X and/or Y rather than the residuals), and the poster asks if it is ...
3
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2answers
99 views

Logistic regression assumptions for a model with many binary independent variables

I am working on developing a logistic regression model that uses qualitative variables only ($n=990$). My remit is to define the equation that can identify the most relevant characteristics of a ...
4
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0answers
45 views

Model assumptions of partial least squares (PLS)

I am trying to find information regarding the assumptions of PLS regression (single Y). I am especially interested in a comparison of the assumptions of PLS with regards to those of OLS regression. ...
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0answers
18 views

Discarding the violation of homogeneity of variance in ANOVA

If after a bartlett.test(split(x,list(f1,f2))) I get a very significant p-value, but my sample size is huge -- millions of measurements -- is it justified to ...
0
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1answer
35 views

When reporting a CFA, is it desirable to assess univariate normality in addition to multivariate normality?

I am using SPSS AMOS to do a factor analysis and it produces statistics related to both univariate and multivariate normality. p35 of the AMOS Users Guide states that "[T]he observed variables must ...
5
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1answer
156 views

Does testing for assumptions affect type I error?

I just performed simple simulation. Made two "populations" with different means and the same variance. Since I prepared them I know that they: are normal, differs in location and both have the same ...
4
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3answers
221 views

Assumptions of linear models and what to do if the residuals are not normally distributed

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 ...
2
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1answer
60 views

Do these residual plots violate the linearity and homogeneity assumptions for linear regression?

There seem to be too many points clustered around negative values for all the plots And while 3 & 4 seem to have random enough patterns, 1 & 2 seems to have negatively sloped trend. If ...
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1answer
82 views

Why does poisson regression need to assume observations are poisson distributed?

Zuur (2013) 'Beginners Guide to GLM and GLMM' states that if the Pearson residuals, when plotted against fitted values from a poisson regression, show the pattern below then the assumption of poisson ...
2
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1answer
78 views

Consequences of non normal-distribution multiple regression

I am currently writing my thesis using multiple regression. However my data does not meet the regression assumption of normal distribution. I want to describe that, due to the non normal ...
0
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1answer
90 views

What makes the results differ for fixed-effects models vis-à-vis random effects models?

What makes the results differ for fixed-effects models vis-à-vis random effects models? The Cochrane Collaboration's website indicated that two models can produce different results for a meta ...
10
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4answers
367 views

Practically speaking, how do people handle ANOVA when the data doesn't quite meet assumptions?

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. ...
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0answers
28 views

Forecast accuracy – can we use correlation and $t$-tests?

Does it make sense to compare actual vs. forecast using correlation analysis / see how close $R^2$ is to 1? Does it make sense to use a paired t-test to test actual vs. forecast to get accuracy of ...
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3answers
101 views

Is it compulsory for a linear regression analysis that a dependent as well as independent variable have equal variance?

The literature suggests that we need to have dataset that meets the condition of homoscedasticity. However, it seems that such a condition is not proper.
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2answers
70 views

Percent error for linear regression model

Suppose I fit a linear regression $y = \beta x + \rm error$. In this situation, $x > 0$, $\alpha > 0$, and therefore $y > 0$. Moreover, the $\rm error$ is normally distributed with mean $0$ ...
3
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1answer
27 views

Validating assumed distributions in parametric models

When using a model that assumes a specific distribution of data, I get confused about how seriously I need to check for the assumption. For example, if we use some statistical test (e.g., based on ...
2
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1answer
58 views

Is it ok to spit non-normal variables in tertiles and put them into multivariate regression models?

I am now reviewing a paper in which the authors decided to predict a DV through linear regression using, beyond other variables, dummy variables obtained from a tertile split of continuous variables, ...
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0answers
34 views

Can mean values be seen as a form of PCA with a LOT of assumptions?

This question just occurred to me out of the blue. PCA is a way to reduce dimensions. Another way that is often (perhaps too often) used is to take mean values of two or more variables. This is done a ...
2
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1answer
84 views

Mann-Whitney test

I am carrying out a study in which I have $3$ groups $n= 2$, $n= 5$, $n=17$. Each group has a different inner ear abnormality, following the fitting of an ear implant I have measured their speech ...
6
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2answers
366 views

Variance-Covariance matrix interpretation

Assume we have a linear model Model1 and vcov(Model1) gives the following matrix: ...
2
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3answers
98 views

What to do when I have expected count <5 warning for a chi squared test?

I applied a survey consisting of 12 questions to 120 people and each questions include 4 nominal categories; I want to make comparison of people's answers according to their socio-demographic ...
0
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1answer
44 views

T test assumptions

I'm performing a t-test on a time series with a sliding window (i.e. every N samples, perform a t test). I know that overall, the samples are roughly normally distributed, however adjacent samples ...
2
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2answers
88 views

Same dataset analysed with four different linear models

I've analysed the same dataset (diamonds from ggplot2) in R with four linear models. Each model has a different error structure. ...
3
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2answers
246 views

Checking residuals for normality in generalised linear models

This paper uses generalised linear models (both binomial and negative binomial error distributions) to analyse data. But then in the statistical analysis section of the methods, there is this ...
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0answers
38 views

Forecasting and auto-correlation [duplicate]

I'm reading this chapter forecasting principles and practise from a forecasting book. The author has explained a linear regression model. Now this linear regression model will definitely have some ...
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0answers
46 views

Cross correlation in Stata

I have a cross sectional dataset of investment figures (in $) and a set of dependent variables. The data relates to various years (2000-2013). More than one investment is included in the dataset for ...
2
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0answers
22 views

Ways the likelihood ratio test may fail

I have a question related to: Why is a likelihood-ratio test distributed chi-squared? On point 2 of @StasK's answer, he states: The theorem assumes that all the relevant derivatives are ...
5
votes
1answer
288 views

Is it ever okay to ignore heteroskedastic residuals and continue with analysis?

My data is misbehaving and I can't seem to get residuals with constant variance despite doing more transformations than Optimus Prime. Is it ever okay to just continue with analysis in and just make a ...
4
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1answer
265 views

What is the minimum viable cell size for 2x2 ANOVA?

I have a 2x2, between-subjects experimental design (2 independent variables (IVs) with 2 levels each) and one dependent variable (DV). My data are unbalanced and an interaction between the IVs seems ...
2
votes
1answer
54 views

residuals plots from four linear models

I've made 4 linear models. For each of these models, I've plotted the residuals against the fitted values. First plot: generalised linear model with quasibinomial link function Second ...
0
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0answers
78 views

Fit of negative binomial regression model

I have ran a negative binomial regression. I'm guessing the use of a negative binomial regression is not ideal given my design, but I'm hoping I can 'get away with it', as it seems to be working ...
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1answer
68 views

What are the important conditions in ANOVA fixed effects?

I am working with an ANOVA model. I want to run a fixed effects ANOVA in which I have a ratio dependent variable and three independent variables with two and three levels. Obviously, before analyzing ...
4
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0answers
66 views

How can one test the assumptions of a zero-inflated negative binomial model in R?

I have fitted a zero-inflated model with a random effect using a negative binomial distribution in R, using the function glmmadmb. This is due to a large number of zeros and over dispersion. For a ...
4
votes
1answer
154 views

Normality assumption in linear regression

As an assumption of linear regression, the normality of the distribution of the error is sometimes wrongly "extended" or interpreted as the need for normality of the y or x. Is it possible to ...
2
votes
1answer
110 views

transformation to normality of the dependent variable in multiple regression

Is it really important to normalize dependent variables in multiple regression or are there any exceptions? My model is providing better results with more significant hypothesis when the DVs are not ...
6
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2answers
265 views

Choosing between $z$-test and $t$-test

Background: I'm giving a presentation to colleagues at work on hypothesis testing, and understand most of it fine but there's one aspect that I'm tying myself up in knots trying to understand as well ...
9
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4answers
347 views

Regression residual distribution assumptions

Why is it necessary to place the distributional assumption on the errors, i.e. $y_i = X\beta + \epsilon_{i}$, with $\epsilon_{i} \sim \mathcal{N}(0,\sigma^{2})$. Why not write $y_i = X\beta + ...
1
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1answer
37 views

should both scores of 2 time points be of same scale in mixed ANOVA

I am running mixed ANOVA as repeated measurement analysis for cognitive score at 2 time points for 2 groups (blood pressure low vs. High). The the score at time 1 was normally distributed and at time ...
3
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2answers
233 views

Linearity between predictors and dependent variable in a linear model

I run the following linear model in R : lm(formula = NA. ~ PC + I(1/SPCI), data = DSET) The p-value for each predictor is significant, and it works fairly well ...
2
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4answers
245 views

Is “ independent and identically distributed” an assumption or a fact ?

This is in the context of two random variables. A frequent assumption (e.g. of the error term in ANOVA) is of independent and identically distributed random variables. There is a question on this site ...
1
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1answer
90 views

What could cause different conversion rates for the same content in separate split tests?

We have an events based tracking system for our website, with split testing built-in and we are using ABBA for the calculations. The problem comes up when we are doing consecutive split tests. For ...
5
votes
2answers
283 views

ANCOVA in observational studies: what are the assumptions?

Using ANCOVA when groups differ on the covariate is controversial, although Tabachnick and Fidell write that this is a plausible function of ANCOVA in quasi-experimental (or observational) studies. As ...
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0answers
41 views

How to check the assumption of equal error variance might be violated?

I want use this box plot to check it. Thank you
0
votes
1answer
199 views

Checking assumptions LMM: residual plot with diamond shape

I am running a linear mixed model and want to check its assumptions. The model I run is comparing if males and females behave differently over time (timeclass=1,2,3,4): ...
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0answers
40 views

Mean of residuals in quantile regression are significantly differ from 0

Is it necessary to have mean of residuals which is equal to 0 in Quantile regression?
4
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3answers
2k views

Checking assumptions lmer/lme mixed models in R

I ran a repeated design whereby I tested 30 males and 30 females across three different tasks. I want to understand how the behaviour of males and females is different and how that depends on the ...