Linked Questions
21 questions linked to/from Why do we need multivariate regression (as opposed to a bunch of univariate regressions)?
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Does multivariate regression produce the same results as multiple, single regressions? [duplicate]
I've read a variety of answers on this topic and as far as I can see, the consensus is that multivariate regression is different from multiple, individual linear regressions. I also understand that in ...
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What does multivariate regression get us that several univariate regressions do not? [duplicate]
("Multivariate" regression in this post means a multidimensional response variable.)
I have been playing with multivariate regression for the past two days, and I have noticed something that did not ...
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Why bother with multivariate regression? [duplicate]
There are a few books out there on how to do multivariate regression, but of course the literature isn't as massive and detailed as univariate regression models.
So why bother with multivariate ...
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Does multivariate multiple regression take into account correlated outcomes? [duplicate]
Thank you so much for your time. I am running an analysis where I explore the association of the same predictors across multiple outcomes (these outcomes are correlated). My understanding is that when ...
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11
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When is it ok to remove the intercept in a linear regression model?
I am running linear regression models and wondering what the conditions are for removing the intercept term.
In comparing results from two different regressions where one has the intercept and the ...
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97
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7
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Explain the difference between multiple regression and multivariate regression, with minimal use of symbols/math
Are multiple and multivariate regression really different? What is a variate anyways?
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154
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3
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Removal of statistically significant intercept term increases $R^2$ in linear model
In a simple linear model with a single explanatory variable,
$\alpha_i = \beta_0 + \beta_1 \delta_i + \epsilon_i$
I find that removing the intercept term improves the fit greatly (value of $R^2$ ...
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82
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2
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Multivariate multiple regression in R
I have 2 dependent variables (DVs) each of whose score may be influenced by the set of 7 independent variables (IVs). DVs are continuous, while the set of IVs consists of a mix of continuous and ...
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When forcing intercept of 0 in linear regression is acceptable/advisable [duplicate]
I have a regression model to estimate the completion time of a process, based on various factors. I have 200 trials of these processes, where the 9 factors being measured vary widely. When I perform a ...
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1
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Why does statsmodels.api.OLS over-report the r-squared value?
I am using statsmodels.api.OLS to fit a linear regression model with 4 input-features.
The shape of the data is:
...
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2
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Simple, multiple, univariate, bivariate, multivariate - terminology
I do realise (some of) this has already been addressed here (e.g., Why do we need multivariate regression (as opposed to a bunch of univariate regressions)?, Explain the difference between multiple ...
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How can MANOVA report a significant difference when none of the univariate ANOVAs reaches significance?
I would just like to ask if it is normal for the values from my multivariate tests to be significant but for the values from my univariate tests of between-subjects effects table to be insignificant.
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Why is it possible to have a non-significant MANOVA but multiple significant univariate ANOVAs?
The only source I have for this being possible is this wikiversity page on MANOVA, but it does not explain why it is possible.
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1
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Multivariate linear regression vs. several univariate regression models
In the univariate regression settings, we try to model
$$y = X\beta +noise$$
where $y \in \mathbb{R}^n$ a vector of $n$ observations and $X \in \mathbb{R}^{n \times m}$ the design matrix with $m$ ...
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2
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1
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Linear regression with vector outputs
Suppose I wanted to make a linear fit to a dataset with vector input and output, by minimizing the least square error. Then the square error equation would be
$$E = \frac{1}{2}\sum_i(W\vec{x}^{(i)} - \...
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2
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699
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Probing effects in a multivariate multiple regression
I'm trying to run a multivariate multiple regression in R, i.e. including multiple predictors and multiple outcome variables in the same linear regression model. Does anybody know how to pull out the ...
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4
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1
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Heteroscedasticity robust variance-covariance matrix for weighted multivariate regression
I need heteroscedasticity robust standard errors for a multivariate linear model (MLM) with weights. In R we usually use sandwich::vcovHC with type ...
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Difference between several univariate regressions and one multivariate regression in a machine learning context
I know there are several other questions asking for the advantages of multivariate regression over several univariate regressions (e.g. this). I understand that the dependent variables can be ...
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When to specify multivariate versus univariate priors on parameters?
Suppose a linear regression model:
$$y \sim Normal(\beta X, \sigma)$$
For our purposes, assume $y$ is a univariate outcome and $X$ is a design matrix containing an intercept and one additional ...
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multi-response forecasting
General
Dear community,
I really struggle with some imporant issues for my next project. In general, the investigation is about multi-response forecasting with financial data. The predicability of the ...
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A method to avoid running several regression models?
Sometimes I have a research question where I have maybe 10 different outcomes and I am interested in whether any of these 10 outcomes are associated with several other variables, such as weight, ...
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