Questions tagged [linear-model]
Refers to any model where a random variable is related to one or more random variables by a function that is linear in a finite number of parameters.
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Linear model with interaction - pairwise comparison
I have the following model in R lme(log_weight ~ log_weight0 + Group*Day, random = ~ 1 | ID, data = mydata).
The interaction term is significant. I would like to ...
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Do I need to set contrasts in my model matrix when using the car package for type 3 ANOVA tables?
I have been running several general and generalized linear models (not linear regressions) using glmmTMB and lme4. After I ...
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Confidence ellipsoids vs. Confidence intervals for linear regression model parameters
The individual confidence intervals for linear regression parameters are given by
$$
\hat{\beta}_i \pm t_{1-\alpha/2, n-(p+1)} \cdot s \sqrt{(X^T X)^{-1}_{ii}}
$$
where $X$ is the $n \times (p+1)$ ...
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Newton-Raphson on Cox PH
I am currently working on my research, namely comparing the conventional optimization method, namely Newton-Raphson, in estimating Coxph parameters with the SGD optimization method. What if I want to ...
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Can I use a variable from Principal Component Analysis (PCA) as outcome Y in a linear regression [closed]
(1) I have a set of 3 predictors which predict an outcome Y (BF10 = 10). Each predictor alone does not predict Y (BFincl < 1)
(2) I would like to assess if another predictor can predict those 3 ...
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How to determine relative contribution of explanatory variables in a linear regression
I estimate a linear regression model, for instance,
$$Y = \alpha + \beta_1 X_1 + \beta_2 X_2 + u$$
and I want to determine how much the variables contribute to $Y$ on average. Put in other words, I ...
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In a bivariate linear regression why is $Y = \alpha X + \beta + U$ where $\alpha$ and $\beta$ are real constants & $U$ is an r.v. an assumption?
Suppose that I want to conduct a bivariate regression between random variables $Y$ and $X$. In the textbooks that I'm reading from, primarily Introductory Econometrics and Estimation and Inference in ...
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ADALINE simple implementation with 2 features bug
I am reading Machine Learning with PyTorch and Ski-kit learn book by Sebastian Raschka
While plotting the decision boundary (a line in this case, since the number of features considered = 2) I can't ...
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Is the OECD BCI Dataset fit for use with Linear Regression?
I am wondering if the OECD Business Confidence Index can be utilised by a linear regression model for time-series data.
I have had a look at the ‘basis of prep, for the data and I am rather confused (...
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on a linear regression analysis, the determination coefficient is 0.99, but the residuals are not distributed normally. How do I interpret this?
So to preface I'd like to say that this is for homework and I am not very good at statistics. Please explain things to me like I am 5 years old.Also english is not my first language.
So the homework ...
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Interpreting coefficients in Log-linear model vs. Poisson regression model
I am trying to understand the difference in interpreting coefficients between log-linear regression and Poisson regression models. To clarify, when I use the term "log-linear regression", I ...
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Uncertainties when fitting an image
I know how to fit a straight line to a set of 2d points with uncertainties on both coordinates, in order to obtain estimators, goodness-of-fit, and uncertainties - see for instance Press & ...
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Perform a weighted linear regression on $x_i, y_i$ by doing a standard linear regression on $X_i, Y_i$?
Let's say we want to do a weighted linear regression between two series $(x_i)$ and $(y_i)$, with weights $(w_i)$, and get the coefficients from the line $y = mx + p$, and the $r^2$ coefficient.
Is ...
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Matching on actual earnings versus matching on the kind of unexpectedness in earnings
For simplicity, lets assume this is a question about linear modeling, although I am actually looking at some non-linear models and am willing to consider other models if they would be more ...
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Proving the equivalence of two distinct approaches to multiple regression for binary classification
I'm stuck with this peculiar problem that uses multiple linear regression in order to solve a binary classification problem (note: it's not considering the logistic version or any other GLM approach).
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