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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lmer with/without covariates of no interests

I am new to using R and lmer model, so the following question may sound dumb to you, but I do have difficulty with figuring that that :( I have this ...
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Regression with multiple variables and time series (in SPSS)

I tried to find similar questions on this topic but couldn't find anything that helped me with my problem, so I will try to explain it on my own. I'm trying to do a regression analysis on revenue for ...
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5 answers
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Correct Interpretation of Survival Curves

I am interested in learning about how decisions (e.g. prioritization of treatment) can be made in the real world based on Survival Curves (e.g. Kaplan-Meier). To illustrate my example, I simulated a ...
3 votes
2 answers
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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, ...
5 votes
1 answer
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Confidence interval for GLM or the maximum of a function?

Imagine I have a set of (xi,yi) measures. I can show it on a scatter plot I want to choose the value of x that maximizes y, or I could fit a function and find the values of the parameters that ...
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1 answer
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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 ...
2 votes
2 answers
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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 ...
1 vote
1 answer
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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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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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1 answer
162 views

Instrumental variables analysis with exclusion restriction violation

I am working with data from a randomized experimental study in which the random assignment of units is used as an instrument. However, there are four endogenous variables (treatments) which are ...
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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 ...
44 votes
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How do I fit a constrained regression in R so that coefficients total = 1?

I see a similar constrained regression here: Constrained linear regression through a specified point but my requirement is slightly different. I need the coefficients to add up to 1. Specifically I ...
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How does ordinal regression compare to quantile regression?

I am familiar with ordinal regression and quantile regression at a high level, but would like a deeper understanding of the two beginning on how they differ. Can someone compare and contrast the two, ...
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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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Interpreting a residual plot

I have created this residual plot using a linear regression in R and was wondering how to interpret it. I believe that it violates the assumption of equal variance as there are clear patterns in the ...
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Statistical Models that "Exploit" Distributional Knowledge of the Predictor Variables

I am trying to see if there any Statistical Models that (better) "Exploit" Distributional Knowledge of the Predictor Variables. For example, I feel that is a common misconception (e.g Where ...
2 votes
2 answers
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Comparing outcomes of two treatment groups: t-test/Mann-Whitney U versus regression

I'm a student and had a question about statistical analysis. I'd like to compare post-treatment outcomes between two groups: group A who received a traditional drug (n = 200), and group B who received ...
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1 answer
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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 ...
2 votes
2 answers
468 views

Is spurious regression a problem for lasso and similar techniques?

I was toying with R to see how the number of variables might affect spurious regression. Suppose that we have an $I(1)$ vector $y$ and a matrix $X$ with $I(1)$ columns. If the two are not related then ...
2 votes
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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 $\pi_k \ge 0$...
1 vote
2 answers
861 views

What is the name for this iterative regression method?

What's the correct term for regression where you first regress on one input variable (feature), take the errors, regress on the next feature, etc.? In what specific cases is this useful? Are there any ...
4 votes
2 answers
938 views

Inverse Regression vs Reverse Regression

I'm aware there's a great number of questions which deal with the mathematical difference between the two, but I'm still confused as to best practice. Basically I'm looking at a situation where we ...
2 votes
1 answer
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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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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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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 ...
2 votes
1 answer
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Repeated measure nested within individuals?

(I'm a beginner so my question could be really dumb ...) So, I have a set of data that contains 30 schools' variable A and variable B. These two variables were measured repeatedly each year for 10 ...
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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 ...
2 votes
1 answer
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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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31 views

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 ...
2 votes
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 ...
1 vote
1 answer
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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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1 answer
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Better to use wald test or likelihood ratio test to make pairwise comparisons after omnibus test in this scenario

I am testing the association between a gene and a binary disease. The gene has many different "versions". These versions are called alleles.I am also including covariates for sex, age, etc. ...
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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 ...
2 votes
1 answer
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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 ...
1 vote
1 answer
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Goodness and prediction measures for conditional logistic regression models

As mentioned in this comment and answer How to get fitted values from clogit model, it is not clear that predicting from a conditional logistic regression model is meaningful. It seems to me that it's ...
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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,...
1 vote
1 answer
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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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1 answer
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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 = ...
2 votes
2 answers
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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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Assumption of linearity between variables and log odds in logistic regression

I know that in logistic regression we assume a linear relationship between the independent variables and the logits. Can you explain why is this a reasonable assumption?
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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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3 votes
0 answers
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Intuition of the log odds linearity assumption for logistic regression

I'm currently having trouble understanding the assumption of logistic regression that the input variables must be linearly related to the log odds. Specifically, what actually happens to the model ...
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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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1 answer
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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 ...
2 votes
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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)?
5 votes
1 answer
314 views

interpretation of model coefficient in logistic regression

In simple logistic regression, a common interpretation of the model coefficient $\beta$ is that a 'one-unit increase in the independent variable is associated with an increase of the log-odds ratio of ...
1 vote
1 answer
386 views

Extreme differences in Odds Ratios in Logistic Regression with or without a predictor

I was trying to get an intuition for the interpretation of the coefficients in a logistic regression that was intended to reproduce to some extent that presented in a youtube video (http://youtu.be/vq-...

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