Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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Choose between residual sum of squares (RSS) and comfounded RSS?

In every course I have taken, I was taught to use the residual sum of squares as (part of) the loss function in regressions, either in simple OLS, lasso or other linear regression methods. Recently I ...
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How to check confounding and mediation in large dataset?

Given a large dataset, one cannot possibly check every model. In particular, it does not seem clear to me that one can check confounding or mediation in either cases. How does one check confounding/...
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"Survival" vs. "Hazard" : When to Use Which?

When dealing with Survival Analysis, we create models that estimate two properties: Survival: Survival Probabilities tend to be more straightforward to understand. Survival Probabilities estimate the ...
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Statistical Models for "Prioritization"?

Are there any general classes of statistical models that are able to perform "ranking and prioritization" tasks? For instance, suppose a hospital has: data on patients (e.g. age, height, ...
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Testing difference between coefficients of nonlinear regression models

Let us consider following data showing sigmoidal dose-dependence for two distinct compounds (blue and red): I wonder about the best approach of comparing the blue vs red "curves" with ...
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Stratification of the continuous y (target) variable in regression setting

Is it wise to stratify the continuous y (target) variable when you split your training and testing data from the total sample in regression setting? Here is the approach in python to do implement ...
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Question about a paper on Calculating All Possible Regressions

I am currently reading the paper "Computational Efficiency in All Possible Regressions" by Liu and it mentions the following. A quick explanation of what I understand: We have a set of $k$ ...
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is nrmse scale-dependent?

Im trying to evaluate my regression models using a normalised version of the RMSE, nrmse = rmse(y, y_pred)/rmse(y, y_mean) where y_mean is the array of the same len as y filled with the mean value of ...
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LinearRegression in Pytorch and sklearn, what is the differnece?

I am currently implementing Linear Regression in Pytorch and sklearn and I get two different Mean squared error (MSE) values for both. MSE is lower for Pytorch Linear Regression. Wanted to ask what ...
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Why the author is quite loose when controlling in control variables for exchange rate regression? [closed]

I read a paper from (Han 2020) and from his equation (4) Δ s ...
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Is "Uncensored Data" necessarily more "Informative" when compared to "Censored Data"?

I am told that one of the main benefits of Survival Analysis models are their ability to handle Censored Data. This is in contrast to standard regression models that are unable to do so. For example, ...
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Help with Excel's Regression Output

I'm a junior engineer at a small biotech company and have some (real) data from a fractional factorial DoE (3 factors, 2 levels, 4 test conditions with six replicates each). Currently, we use excel to ...
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Should you clean your data after or before selecting a sample?

Assuming a 500k dataset. For statistical modeling purposes (selecting up to 10% of 500k as a sample). Should I clean the 500k dataset first before selecting a sample or select the sample first and ...
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Why is total variation $\sum_{i=1}^{n}\left(Y_{i}-\bar{Y}\right)^{2}=\sum_{i=1}^{n} y_{i}^{2}$? [closed]

I've been interested in Econometrics and the book I use is Econometrics by Badi H. Baltagi, 5th edition. I tried to answer some of the problems. However, one problem from chapter 3 no. 2 got me ...
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Interpretation of binomial regression in R

I'm running a regression to see if theres a positive or negative relationship between cvdrst and smkcigst, but I'm not sure how to interpret this regression also why is the data for smkcigst NA, even ...
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How to use sparse PCA loadings in a regression?

I'm using Dr Frank Harrell's code in RMS 2nd edition. He goes into sparse PCA. Does anyone know how to code a regression model after getting the sparse component grid? ...
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Adding Controls to Staggered Difference in Difference Regressions on Stata

I am running a staggered diff-in-diff model, looking at legalization's effect on various variables. For context, only a percentage of all states have legalized, and the year they legalized differs ...
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VAR model with AR(p) and ARMA(p,q) data?

I want to estimate a VAR-model with 6 variables, all of them are stationary. But when I analyse the time series by examining ACF, PACF and auto.arima in R. ...
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Fitting Sparsed Constrained regression with non-negative coefficients adding to 1

I see a similar problem in How do I fit a constrained regression in R so that coefficients total = 1? Specifically, my model is $Y_i= \pi_1 X_1+\pi_2 X_2 +...+ \pi_K X_K +\epsilon_i$ with $\pi_k \ge 0$...
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CAPM Estimation

Please that might sound basic for all of you but I am not an expert and I need to estimate the following model using OLS regression: R= a + β1 RM + β2(z)RM + ε (the model is called conditional CAPM, ...
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regression analysis in brand funnel

What regression can I use for brand funnel? The participants who do not choose a brand in the consideration, can not pick the brand in the conversion stage due to filtering. However, this is possible ...
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Is it possible to estimate IPTW in two different subgroups to evaluate interaction? (Subgroup Balancing Propensity Score?)

This questions needs a toy example to be explained. I apologize if the question is not clear. Suppose we have an observational study in which we want to evalute the association between exposure to ...
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1 answer
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Applying log transformation to input data has almost no effect on pearson correlation, what does that tell me about my data?

I have a data set where some x input has a 0.59 Pearson correlation with variable y (sample size is about 300). After performing simple linear regression I get the following standardized residuals ...
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Violation of application requierements / assumptions

As far as I know all methods / models making assumptions about data distribution, data scale, etc. but whenever I read a paper (political sciences) the researcher(s) are violating them (some). E.g. ...
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The correlation coefficient with a priori intercept and slope [closed]

What does the formula for the product moment correlation coefficient look like if the intercept and the slope are given a priori?
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What does it mean if my confidence interval includes zero with a significant p value in linear regression analysis?

I performed linear regression analysis to assess the associations between continuous variables. I found a significant p-value but my confidence interval includes zero. What does it mean? Here are the ...
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Detailed comparison of two methods for obtaining the ridge regression solution

I have come across two different ways of obtaining the ridge regression solution, which are as follows: Method1:-(obtained from here) $RSS(\beta) = (Y-X\beta)^T\cdot(Y-X\beta)+\lambda\beta^T\Omega\...
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Scale location plot interpretation

I ran a regular OLS regression and wanted to check if the assumptions for OLS regression was meet. To do this I plotted a scale location plot, but I'm struggeling with the interpretation of the result....
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should I scale the dependent variable within each grouping variable in linear regression?

I have several variables measured in different units (blood pressure, scales, heart rate), but all of them are an indicator of stress. I would like to combine them in a single model to try to predict <...
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Inclusion of year and seasons as variable for regression with non-stationary response?

The common knowledge is that OLS only makes sense if both the response and explanatory variables are stationary (ignoring exceptions like cointegration), as otherwise, there could be effects of ...
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Can a transformed variable's SE be meaningfully interpreted?

Suppose, for simplicity, I have a simple linear regression model, and I have transformed the response variable by taking the square root. The pre-transformed standard error is equal to 17, and the ...
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What are some well-known unbiased estimator of regression coefficient besides OLS estimator?

Is there any other unbiased estimator of regression coefficient than OLS? For instance, one might consider using unbiased estimator with less computational cost (since OLS involves matrix inversion)?
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Gauss-Markov with $p>n$

Let $p$ be the number of parameters in a linear regression model, let $n$ be the number of observations, and let $p>n$. $$\mathbb E[Y\vert X] = \beta_0 +\beta_1X_1 +...+\beta_pX_p$$ Does the Gauss-...
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How do I do a logistic regression model in R for an outcome with multiple values?

I want to analyse the association between the outcome "Other CTR-CVD" and the independent variables would be "anthracyclines", "Her2", "VEGF", "TKI, "...
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when we lacked of some colums in validation set, how to use validation set in regression problem?

I am doing the project related with regression problem like failure rate prediction. For training data set, I have columns like below I have 10 columns training data. And for validation set, it is ...
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A misspecification error with linear models that can complete reverse the direction of an effect, has this been described, has this a name?

Linear models are ubiquitous in economic, social, health and nutritional sciences and the starting point for much research and many articles. However, there is a problem with linear models. When the ...
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How do you interpret Beta coefficients for Fixed Effects Panel Data Models?

Let's say we have House Prices across different cities (Bristol, Brighton, London, Glasgow) across time (Monthly data from 2016-2020) and we're trying to predict it using unemployment and crime. t = ...
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How to perform variable/feature selection before random forest in R?

I have a phyloseq object with 4000+ taxa and 300+ samples. What methods/packages can I use to perform feature selection to reduce the number of taxa/OTUs prior to performing random forest? I was ...
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Unbalanced Sample and interpreting log-transformation

I am analyzing a sample of two groups of 5,092 and 114,038 customers - and two very basic questions I assume. Question 1: Do you think this is a problem in general AND for the usage of OLS, fixed-...
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and what if non-linear activation functions give better results than the linear ones?

I had a regression problem with small data set, I solved it with neural networks (MLP, ELM,..) As convention, I used a linear function for output layer, the results were not so good. I tried to change ...
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Unbiased estimator of regression coefficient in high dimension

Is there any unbiased estimator for the regression coefficient $\beta \in \mathbb{R}^p$, p >> 1, where $$ y_k = x_k^T\beta + \epsilon \in \mathbb{R}? $$ Note that $x_k \in \mathbb{R}^p$ and $\...
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Can I convert a classification problem with an ordinal dependent variable in a regression problem?

It looks interesting to me to know about the variables related to the students performance, so I started to look into the following dataset: https://archive.ics.uci.edu/ml/datasets/Higher+Education+...
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How to predict multiple future values in a linear model in R?

So i currently have a data set consisting of the Year, Credit Hours, and Number of students. I have been trying to predict future credit hours by the number of students. ...
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My CNN is not learning but just memorizing [duplicate]

So there has been similar posts but none of them solves my problem, so I decided to created a new question. I'm working on a regression project where I intend to use CNN to predict material properties,...
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What's WRONG with my multiple regression model

I am working on a regression model, more precisely, multiple regression model for predicting one single value. I have a dataset of cars and some technical data. For example, I have the following ...
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Design loss function for model-based reinforcement learning

I'm doing some model-based reinforcement learning, and I'm stuck at how to better design the loss function for fitting the dynamic model of the environment. In continuous state and action space, the ...
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Showing a regression model is unidentifiable? [duplicate]

I am following a tutorial in which we are looking at a risk regression model (the Cox’s proportional hazards model. In particular, the hazard rate is modelled as $$ \lambda(t) = \lambda_0(t) \exp(\...
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How to do forecasting from linear regression when covariates are correlated

Consider I have in situ measurements (samples) of 4 variables: Temperature, salinity, pH, depth I know how temperature will change and want to calculate the expected change in my other variables ...
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Intuition for confidence intervals vs prediction intervals for linear regression

I am having a bit of trouble understanding the difference between a confidence and prediction interval in the context of linear regression, and in what scenario we would use either of them. I've posed ...
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Interpretation of Multinominal Logistic Regression coefficients

I am struggling to understand my Multinominal Logistic Regression. This is my first time ever tackling such a model. Note that I was following this recipe. I am trying to predict the redemption rate (...
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