All Questions
Tagged with heteroscedasticity multiple-regression
86 questions
5
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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 ...
2
votes
1
answer
168
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Assumption in multiple linear regression
The principles of multiple linear regression are widely described, however there are still some aspects I don't truly understand why. Specifically speaking I don't understand why heteroscedasticity ...
- 447
0
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1
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149
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How critical/serious is the heteroscedasticity in my data (Breusch-Pagan test significant at p=.03)?
edit below
I am doing this analysis for the first time. How concerned should I be about heteroscedasticity in my data? Here's the scatterplot of predicted values vs residuals:
The Breusch-Pagan test ...
- 41
1
vote
0
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32
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Trust the graphs or go with Breusch-Pagan and White's tests for Homoscedasticity on large datasets? [duplicate]
I have a large dataset (n > 500,000) which I'm building a linear model with lm(PV1READ ~ PV1MATH + PV1SCIE + ST004D01T). Tests for Normality, No ...
- 159
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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 ...
2
votes
2
answers
295
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Multiple linear regression homoscedasticity/linearity
My question is about the implications of the violation of homoscedasticity/linearity for multiple linear regression. I have tried to find the answer in multiple sources but could not figure it out.
I ...
- 21
2
votes
3
answers
352
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Why does heteroskedasticity not affect $R^2$ and why does it make estimated regression more statistically significant?
I am studying what the consequences of heteroskedasticity are. And i found that assuming that the model is linear in the parameters (i.e $Y=X\beta+\epsilon$), is identifiable, has no perfect ...
- 236
1
vote
2
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253
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Heteroscedasticity still present after Box-Cox transformation
I just started to learn regression and I'm trying to fit a linear regression model to some data with one continuous independent variable x1, one categorical variable x2, and the dependent variable y.
...
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0
votes
0
answers
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 ...
0
votes
0
answers
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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192
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Is it impossible to fit HC4 robust method for linear mixed models?
I've been struggling all day to fit a robust HC4 model to my lmer in R (all the examples I've seen were with lm. I simply cannot ...
- 843
0
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0
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177
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What to do when there is Heteroscedasticity in a multiple regression model?
So I’m doing a multiple regression analysis with Y and 6 features. I realize that Heteroscedasticity exists between Y and 2 of the independent variables. How can I deal with this problem? Y is already ...
0
votes
0
answers
471
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Covariance matrix of errors for homoskedasticity/heteroskedasticity
I've seen homoskedasticty and heteroskedasticity defined as the following
The error term of our regression model is homoskedastic if the variance of the conditional distribution of $u_{i}$ given $X_{...
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1
vote
1
answer
535
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How to tell if there is a homoscedasticity of the model based on this plot?
I am building regression model of cholesterol predicted by 4 dietary components. I want to check if the assumption of Homoscedasticity is satisfied. I plotted Residuals vs Laverage plot.
...
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3
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2
answers
436
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Does this plot show heteroscedasticity?
I am running a multiple regression with a continuous DV and a mix of dichotomous and continuous IVs (but mostly dichotomous). This is the ZRESID vs ZPRED scatterplot, and I think there is ...
0
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0
answers
70
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Tried rectifying Heteroscedasticity and I don't know why I did not succeed
I am doing a regression on the influence on interest rates on marketing spending. I tried many different approaches to get my regression working. I have panel data and time series. I got a data set of ...
- 13
0
votes
1
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1k
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Trying to use the white test in r
I am doing a regression on the influence on marketing spending.
I have already tested for heteroskedasticity with the Breusch-Pagan Test and found that the test came out positive.
Based on the ...
- 13
1
vote
0
answers
170
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Weighted least square regression - different ways of estimating weights
Newbie here.
I have a question regarding WLS regression.
Specifically, I've come across different ways of estimating weights in WLS regression, the most frequent ones being:
...
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1
vote
0
answers
78
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Test statistic for regression analysis with robust standard errors
I am looking for the test statistic for a regression analysis (using lm in R) with robust standard errors. As far as I understood the F-statistic from the original model is invalid but I was wondering ...
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1
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0
answers
741
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How can I transform response variable with negative values to fix heteroscedasticity
I'm trying to build a multivariable least-square linear regression model, and there is heteroscedasticity in my model. I saw many articles suggesting transformations such as log transformation or Box-...
5
votes
1
answer
706
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how to deal with heteroscedasticity in least squares regression with multiple independent variables
I am trying to build a least-squares regression model and when I analyzed the independent variables, I saw a case of heteroscedasticity in one of the independent variables.
I'm building this model in ...
- 515
0
votes
1
answer
217
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Can I use an OLS regression model with assumption violations for filling the missing data and prediction?
I am working on a multiple linear regression model as ordinary least squares (OLS)with several predictors and one response variable.
The data is for the counties for different years around 2200 ...
6
votes
1
answer
274
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Flawed multiple linear regression in academia? Heteroscedasticity's effect on p-value?
I believe I have found a paper in academia that has used a flawed multiple linear regression. I have downloaded the data set and replicated their regression results. I have done some diagnostics and ...
- 351
3
votes
1
answer
1k
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Which plot to check for heteroscedasticity in a multiple regression model
I have a linear model like:
Reg.Model = lm(Y~X1+X2+X3, data=DF)
If I want to check for the presence of heteroscedasticity using a plot, should I plot the residuals ...
- 261
0
votes
0
answers
52
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Residual plot Diagnosis
I am working on a multiple linear regression model to investigate the relationship between several independent variables such as profitability, leverage, board size, percentage of women on board, ...
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2
votes
2
answers
537
views
Do explanatory variables have to have a linear relationship with the response variables?
Do explanatory variables have to have a linear relationship with the response variable in multiple linear regression? What is the reason for this assumption?
Also, why are heteroscedastic ...
user274779
1
vote
1
answer
1k
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Homoscedasticity issues? And how to solve this?
Based on the plots attached. Do i have any issues with the homoscedasticity assumption? It looks like the dots on the scatterplot are spread but there is some sort of downward trend. Is this causing ...
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0
votes
1
answer
164
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Assumption of Homogeneity of variance [duplicate]
Why is assumption of Homogeneity of variance required. What are the problems if they are not satisfied
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1
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1
answer
53
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How Do I Create a Better Model?
Disclaimer: I am a senior undergraduate student of Political Science with little proficiency in Data Science; please help me understand better and forgive any ensuing statistical illiteracy!
TL;DR: I ...
- 11
0
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0
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47
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In a multilinear regression, does the usage of White standard errors always correct for heteroscedasticity?
I've calculated a multilinear regression. When testing the assumptions of linear regression, I've come to understand that my model violates the assumption of homoscedasticity (as shown with a Breusch-...
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3
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1
answer
702
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Checking the constant variance assumption for residuals vs fitted plots: What about for the same fitted values?
For a residuals vs fitted plot, we use the fitted values $\hat{Y} = \beta_0 + \beta_1 + \cdots + \beta_p x_p$ on the horizontal axis and the residuals on the vertical axis, and then compare the ...
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1
vote
0
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49
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how to check heteroskedasticity in pyhton?
After building model by using glm. How to check heteroskedasticity? are there any tests available to find or is it checked by using graphs??
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0
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1
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32
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How can the below graph be interpreted
How can you interpret the scale location graph in terms of Homoscedasticity?
0
votes
1
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240
views
Data transformation to deal with heteroscedacity
I am trying to build a linear regression model of the data which generally looks like this:
Certainly due to the exponential (I guess) nature of the data, I have tried to do a logarithmic ...
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1
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0
answers
1k
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Generalized Least Squares for linear regression with continous and categorical predictors
I have a linear regression model in R studio with a continous and a categorical predictor, where the assumption of homoscedasticity is violated:
...
- 111
2
votes
0
answers
228
views
How much heteroscedasticity need to be present in order to justify the use of robust standard errors?
Im trying to figure out if my data is heteroscedastic and if I need to use robust standard errors (Huber-White standard errors). The dataset contains 70 000 rows and 5 columns. Y is a numeric ...
- 77
2
votes
1
answer
143
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Do these graphs show that the regression assumptions are met?
Is there any concern regarding this plot, specifically that it meets the homoscedasticity assumption? May I continue with multiple linear regression? How can I fix this?
My research is on household ...
- 21
0
votes
0
answers
68
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Minimizing the expectation value of least-squares loss when data and model are randomly distributed with known normal distribution
How do you minimize the stochastic robust least-squares problem
$$
\min_x \mathbb{E}\left\{||A x - b||^2\right\}
$$
in which both the parameters $b$ and the model $A$ are normally distributed with ...
- 131
0
votes
1
answer
1k
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How to fix heteroscedasticity (funnel shape)?
I am running a mlr in python on a dataset with 2D feature vectors, X1 and X2 on a single response, Y. The data ends up being funnel-shaped, as below:
X1 v Y, with the colors being X2.
It was ...
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2
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0
answers
49
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Multiple linear regression and model build in light of regression diagnostics
I have a dataset of approx. 200 observations, consisting of Profit which is my dependent variable and is continuous, and the independent variables are Turnover (also continuous), and 3 additional ...
- 1,083
2
votes
2
answers
3k
views
Does this graph imply a violation of homoscedasticity?
I assume that this graph doesn't support the assumption of homoscedasticity. Am I right? Does it make sense to carry out another test to be sure?
y-axis: Regression Standardized Residual, x-axis: ...
user268744
1
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0
answers
96
views
How do I appropriately control for a limiting/maximum value in regression?
I have a dataset where one variable is limited by the value of another. It is a study of participants with a particular disease. By necessity, therefore, age of disease onset, A, can be no larger than ...
- 443
1
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1
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123
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Multiple Regression - Heteroskedasticity? - Is this a linear model?
I am analyzing a multiple regression model in SPSS. I am checking whether the requirements for a linear model are met. The last requirement is homoscedasticity.
In my survey, you can choose between 1-...
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7
votes
0
answers
2k
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What are the differences between HC estimators and their small sample properties?
I am currently using R to run regression with the following code:
...
- 468
2
votes
1
answer
329
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Heteroscedasticity consistent (HC) standard error analysis and interaction effects in an OLS
I have made a model with several variables, and 8 of them interact with a dummy to find interaction effects. These are added stepwise, resulting in three models. Now, through a Breusch-Pagan test I ...
- 21
1
vote
1
answer
116
views
Evidence for heteroscedasticity from unordered values
I'm fitting a linear regression model on a dataset about how many upvotes a certain post will get based on its views, its author's reputation ecc.
To satisfy the normality assumptions I performed a ...
- 113
0
votes
2
answers
2k
views
Multiple linear regression: homoscedasticity or heteroscedasticity
Regarding the multiple linear regression: I read that the magnitude of the residuals should not increase with the increase of the predicted value; the residual plot should not show a ‘funnel shape’, ...
1
vote
0
answers
355
views
Using coeftest results in predict.lm() in R [closed]
I am analyzing a dataset in which the variance of the error term in my regression is not constant for all observations.
For this, I re-built the model, estimating heteroskedasticity-robust (Huber-...
- 11
2
votes
1
answer
2k
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In the presence of heteroskedasticity, is quantile regression more appropiate than OLS?
..for understanding the relationship between a dependent and independent variables, given that quantile regression makes no assumptions about the distribution of the residual.
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1
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2
answers
2k
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Constant Variance Assumption in Linear Regression
It seems to me that the following plot of "Residuals Vs. Fitted Values" violates the assumption of constant variance, since for lower fitted values, there are fewer points whereas for higher fitted ...
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