All Questions
Tagged with heteroscedasticity normality-assumption
55 questions
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Assumptions of Linear Regression (homoscedasticity and normality of residuals)
I am confused about some assumptions of linear regression: homoscedasticity and residuals are normally distributed. These two require residuals, but to get the residuals, we need to fit the model ...
0
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0
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32
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True or False: If the distribution of Y|X is normal, then the regression of Y on X must be both linear and homoscedastic [duplicate]
I'm trying to interpret an early and pretty dense (to me) paper on the theory of linear regression:
Bartlett, M. S. (1934). On the theory of statistical regression. Proceedings of the Royal Society of ...
- 642
2
votes
1
answer
61
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Anova model assumptions. How to go by?
I have a data frame that contains a continuous response variable measured on different species, at different elevation and month of sampling as explanatory variables. I want to analyze how the ...
- 73
0
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0
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36
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I am unable to relate the normal distribution dependency for regression . I need a mathematical intuition cum understanding for regression assumptions [duplicate]
What is the mathematical significance for the assumptions of linear regression to hold true for arriving at a single/multiple regression formula? Can anyone use the assumed normally distributed ...
1
vote
1
answer
585
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Dealing with violation of linear regression assumptions
I have a data set where some extreme, but not nonetheless important observations are present which prompts violation of the linear regression assumptions of normality and constant variance. The ...
- 327
1
vote
1
answer
64
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Testing assumptions for repeated measures ANOVA
I was wondering how to assess residual normality of a repeated measures ANOVA. In some threads, users refer to Venables and Ripley: Residuals in multistratum analyses: Projections and recommend to ...
- 433
0
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0
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133
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Can't fix non-normality and heteroskedasticity
I am attempting, via linear regression, to model a dataset.I've tried various transformations on the response/ and predictors, as well as WLS but the assumptions are not met. I'm looking for the ...
1
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0
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248
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Lmer model fails normality and homogeneity of the residuals; model-predicted lines are not great. Some predictors might not be linear. What can I do?
CONTEXT:
I gathered data from 1000 participants in five different countries, with each line in my dataset corresponding to a unique participant. My study examines how much individuals support the idea ...
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0
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0
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211
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Bootstrapping with Hetroskedasticity?
In most of the times in linear regression, the two problems of non-normality and hetroskedasticity are present both in the model.
However, the two problems could be (but not neccessarily) inter-...
- 171
2
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0
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36
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How do I go about rectifying violated assumptions when more than one is violated at the same time? [duplicate]
I am currently trying to run a model analyzing the duration of the egg stage of each sex of two species of insect across five different temperatures. All independent variables are categorical. My ...
- 461
1
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0
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138
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Model residuals are normally distributed but they are heteroscedastic and autocorrelated
I have a candidate set of five linear models, as I want to choose the model with the lowest AIC and calculate predictions from it. For each model, I have tested for normality of model residuals (...
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0
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0
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91
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Calculating fixed effects in a mixed model with non-normality and heteroscedasticity with a 3-level time variable?
Due to non-normality and heteroscedasdicity, I use robustlmm and not lme4 for my mixed effects model. The variables look like this:
ID: subject variable (random factor)
var1: categorical between ...
- 21
0
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1
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89
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Heteroskedacity and non-normality - What to do?
I conducted an experiment in which I am trying to model the relationship between my response weed_coverage [%] and the predictors soil moisture [%] + treatment + distance. Weed_coverage and ...
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1
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0
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38
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Can we objectively determine whether assumptions have been violated in R?
I'm testing statistical assumptions in R and so far I've been using plot(model, which = c(1:6)), which produces six graphs for linearity, normality of errors, ...
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4
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1
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241
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How does data that violates linear regression assumptions (of the residuals) look?
Linear regression has two assumptions about the residuals :
The residuals should have constant variance (for every level of the predictor).
The residuals should follow a normal distribution.
Is it ...
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1
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0
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35
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Problems with VAR: Autocorrelation when imposing restrictions, ARCH effects and non-normality at all times
I am estimating a VAR model for log-returns of: copper prices, USD/local currency exchange rate, and the local stock market index. Using VARselect I estimated a VAR(...
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6
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1
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Heteroscedasticity and non-normal errors are only an issue when predicting from a linear model - why?
In Regression and Other Stories, the authors state that heteroscedasticity and non-normal errors are only problematic when predicting from a linear model (1; p. 154-155):
Equal variance of errors. ...
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0
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1
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585
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Do I need to check for Autocorrelation, Heteroskedasticity and Normality when building a model with data that are not time series? [duplicate]
I want to build a simple regression model with non-time series such as Client ID Number. when testing the validity of this model, do I need to check for autocorrelation, Heteroskedasticity and ...
0
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0
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270
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When distributions are non normal and heteroscedastic, is it preferred to use ordinal logistic regression or permutation tests?
I am currently conducting statistical tests on my two independent samples (both with more than 1500 entries each). The sample sizes are no equal. My response variables are interval as well as quasi ...
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1
vote
1
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902
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Is assumption of residual normality and Homoscedasticity in nonlinear regression
I am still learning a lot about nonlinear regression and I have some questions about residual normality and Homoscedasticity:
1) From what I could find here (Consequences of violating assumptions of ...
- 161
0
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1
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417
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Dealing with violation of OLS assumptions
I am currently writing my Master's thesis in economics. I am analyzing the second home rate in Swiss municipalities in R.
The second home rate for municipality $j$ is defined as the share of the ...
1
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2
answers
2k
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Help interpreting residual vs fitted plot and normality (ANOVA on R)
I'm carrying out a statistical analysis on R using ANOVA and am not sure if the data meets the assumptions of normality of residuals or homogeneity of variance.
My data :
And my plots:
Any help is ...
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3
votes
1
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520
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Is one-way ANOVA robust to violations of homoscedasticity?
I read here that if group sizes are equal, ANOVA is robust against the violation of the assumptions of normality and homoscedasticity.
I am wondering if this is the case, and if so why?
- 133
3
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1
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957
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Analysis of variance with not normally distributed residuals : how important is normality?
I am using gls and anova to analyse my data. I use gls to aply weights. I have one factor (tree genotype) and I analyse its influence on soil content.
Here is an exemple of my data with one variable (...
2
votes
1
answer
6k
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Dealing with heteroscedasticity and non-normality in a mixed model
I am trying to fit a mixed model (person as random effect) on data which has heteroscedasticity and non-normality. I log-transformed the Y-variable but it did not ...
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5
votes
1
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119
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Use of supportive inferential statistics (e.g. Levene's and normality tests) in underpowered samples
We know that underpowered statistics greatly increase the probability of a type II error (by definition), meaning a greater chance of failing to reject the null hypothesis despite the existence of a '...
- 95
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0
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46
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Which one of these is correct for linear regression?
Only one of these is supposed to be the correct one for simple linear regression. Which pair of plots would you say has constant variance and normal distribution? I feel like none of them have both ...
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2
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0
answers
352
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Engle Granger Error Correction Model - Normality, heteroskedasticity and Autocorrelation tests
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 = \...
0
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0
answers
90
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Transformation for non-normally distributed homoscedastic data?
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 ...
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3
votes
2
answers
2k
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Theil-Sen estimator assumptions
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 ...
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0
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0
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856
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Logistic regression normality and homoscedasticity [duplicate]
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 ...
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0
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1
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37
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Can I use residuals for transformation?
This may be a simple question, but my data (once transformed) passes normality, however does not pass homoscedasticity. Can I look at the residuals to see if they pass this? or am I over looking this?
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2
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74
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Available options when data violates multiple test assumptions?
I am confused as to how I should analyse my data since I think it violates almost all possible assumptions.
I applied several hormoneconcentrations to plants and counted the amount of roots. ...
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2
votes
1
answer
407
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Testing the assumptions of a linear regression model
I am building a model for predicting airline prices. I have 19 relevant predictors and and 114000 observations. I am getting an $R^2$ predicted of 95.87 (Minitab displays $R^2$ predicted after ...
- 147
5
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1
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890
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Under what conditions is Welch's unequal variance t-test more conservative than Student's equal variance t-test?
I am comparing the anatomy of two Neanderthal populations for which sample sizes, the number of available bones, is low (usually between 3 and 8 for a given trait). I am using Welch's unequal variance ...
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1
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0
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265
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Problem with one-way ANOVA assumptions
Our research is entitled "Relation of Family Perfectionism on the Career Indecisions for Grade 10, 11 and 12 Students"
Independent variable: grade level
Dependent variable: family perfectionism and ...
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2
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0
answers
235
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Are regression estimates still reliable despite heteroscedasticity and non-normality
I am performing a simple linear regression with the lm() function to make statements about the association between the two variables. But I am not sure if my ...
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0
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1
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773
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In linear regression, why do the assumptions such as normality and homoscedasticity only hold for the residuals?
Why does the distribution of the residuals should be normal and variance of the residuals should be same across the values of the independent variable? Why do we assume these for the residuals only? ...
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2
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1
answer
55
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Are my assumptions right for this question?
I answered these homework questions but I was told that at least one of my answers is wrong. However, I can't tell which one I answered wrong. What incorrect assumptions have I made?
QUESTION:
MY ...
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8
votes
1
answer
2k
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Heteroskedasticity and Distribution of the Dependent Variable in Linear Models
I am running a multivariate ols model where my dependent variable is Food Consumption Score, an index created by the weighted sum of the consumption occurrences of some given food categories.
...
- 406
3
votes
0
answers
1k
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Linear regression with trimmed data
I would like to know how experts deal with real data. Even if statistical text books uses real data I'm always surprised how good the real data are and at the end of the exercises the residuals are ...
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3
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1
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2k
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Compare several means with different sample sizes (greater than 2)
I have seven different groups with different sample sizes and variances and I want to compare the means of their data. I'm not very informed in statistics, so could anyone help me out here? I've only ...
- 141
1
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2
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701
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ANOVA and Kruskal-Wallis Test in One Study
I am currently analyzing the results of my study which deals with several dependent variables. I have tested the data for normality and homogeneity and all but one passed the assumptions for ANOVA. ...
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1
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1
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123
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What is an appropriate test for a normally distributed, heteroscedastic, multi-factor data set?
I have a data set of active layer depths from an Arctic field site. There are two factors in the data set, Month measured (July or August), and Location (shrub patch or open tundra). I had intended on ...
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5
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1
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2k
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Standardized residuals vs fitted values: OLS assumptions satisfied?
Based on only the above plot, what comments would you make about whether the OLS assumptions are satisfied?
In particular homoskedasticity, normality.
I just want to know if I'm right. It seems to me ...
- 1,003
4
votes
1
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226
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Is an ANOVA applicable for these data?
I have a data set from 7 groups, with 20 fish in each group. Measurement of a parameter is made on 25 cells from each fish (so each observation in the data-set is completely independent, right?). One ...
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1
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0
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2k
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Non-normally distributed residuals for multivariate linear regression.Still a valid model?
I try to know if the independent variables are affecting the outcome of the dependent variable, but while the Shapiro-Wilk test shows residuals non-normally distributed, the autocorrelation of errors ...
- 181
5
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3
answers
15k
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When to use Brown-Forsythe Test?
I have been researching the differences between Welch ANOVA and Brown-Forsythe Test. I know that Welch ANOVA is used for more than two groups comparing whether there is statistically meaningful ...
- 901
1
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0
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942
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Unpaired t-test: large samples, unequal variances, unequal sample sizes [duplicate]
I a have two independent samples one with more than 700 observations and the second with more than 500 observations. I firstly use Cramér-von Mises test to test normality just to check how non-normal ...
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2
votes
1
answer
5k
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Consequences of violating assumptions of nonlinear regression when comparing models and/or datasets
I have a question about the consequences of using non-linear regression when the data violate the assumptions of (1) homoscedasticity and (2) normal distribution. Specifically, I am wondering about ...
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