Regression when the OLS residuals are not normally distributed
However I am not limited to OLS and in facts I would like to understand the benefits of other glm or non-linear methodologies. What is the most efficient way to model data violating the OLS …
Why is normality of residuals (homoskedasticity) "not needed" for …
Nov 3, 2024 · Recently there has been a barrage of posts on Twitter critiquing someone for stating that normality of residuals is a necessary assumption for OLS regression / the General …
How does OLS regression relate to generalised linear modelling
May 9, 2016 · How does OLS regression relate to generalised linear modelling Ask Question Asked 9 years, 6 months ago Modified 9 years, 1 month ago
Cause of a high condition number in a python statsmodels …
My conclusion: in the case of regression against a single variable, don't worry about the condition number UNLESS you care about the sensitivity of your y-intercept to the input data.
ANOVA vs multiple linear regression? Why is ANOVA so …
12 ANOVA and OLS regression are mathematically identical in cases where your predictors are categorical (in terms of the inferences you are drawing from the test statistic). To put it another …
Difference between an "Ordinary Least Square (OLS) model" and a …
Dec 15, 2017 · It is my understanding that an OLS model is (or at least it can be) based on a panel data, where the covariates are treated as fixed effects, right? So what is the difference …
What happens when I include a squared variable in my regression ...
Mar 19, 2013 · 25 I start with my OLS regression: $$ y = \beta _0 + \beta_1x_1+\beta_2 D + \varepsilon $$ where D is a dummy variable, the estimates become different from zero with a …
Standard error/deviation of the coefficients in OLS
Feb 13, 2015 · In OLS, the variance of the regression coefficients are computed as $$ \mathrm {Var} (\hat {\beta}) = \sigma^2 (\mathbf {X}^\mathrm {T}\mathbf {X})^ {-1}. $$ Now, if I need to …
What is the difference between a hierarchical linear regression and …
8 I am conducting a research whereby I have a few independent variables (all of them are dummies), moderators (one is a dummy, the other is continuous) and a continuous dependent …
How To Solve Logistic Regression Using Ordinary Least Squares?
Sep 21, 2016 · 9 The sigmoid function in the logistic regression model precludes utilizing the close algebraic parameter estimation as in ordinary least squares (OLS). Instead nonlinear …