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Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
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Decomposing R^2 into independent variables
Consider a linear regression model:
$$y = β_0 + β_1X_1 + β_2X_2 + ... + β_kX_k + ε$$
where $R^2 = 1 - (SSR/SST)$. …
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1
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183
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Decomposing R squared or VIF
In the context of multi-regression, I am wondering if there is a way to decompose $$VIF_i = 1/(1-R_i^2)$$ where $R_i^2$ is the r squared obtained from the regression of dependent variable = i and independent …
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Reducing the dependency between variables
I am trying to perform a multi linear regression model:
$$y_i = β_0 + β_1x_{i1} + β_2x_{i2} +... + β_px_{ip} + ε_i$$
where $$x_{i1}, x_{i2}, ..., x_{ip}$$ are highly correlated with each other (VIFs …
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2
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Influence of a data point on the regression result?
Let's say I perform multiple regression where y = income, x1 = educaiton, x2 = sex, and x3 = religion from 2003 to 2018, where the data is measured daily. … Is there any way to quantify an impact of a single day data (e.x. 2005-07-01) on the regression result? …
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How to choose variables for regression
I need to form multiple regression on the fund returns using the benchmarks returns as independent variables (i am allowed to form linear combination or manipulation of the indices or even non-linear combinations …