Questions tagged [regression]

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

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Why doesn't adding additional explanatory variables in a logistic regression model decrease our primary explanatory variables variance?

Imagine a clinical trial setting where we have binary outcome Y and we are interested in the effects of treatment X. Lets say we also have additional explanatory covariates Z and W. Thus our ...
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2 answers
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MLE to address multicollinearity in linear regression

OLS estimation assumes that the explanatory variables are independent in the linear regression model. There isn't such assumption when using the MLE estimation. So, my question is, can we use MLE to ...
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OLS R-Squared from Sliced OLS Regression

I have the following question: suppose we have a data set with 3000 observations $(X,y)$ and $X$ can be matrix. So we want to use a bunch of features to predict $y$. Suppose we sliced the data into ...
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How to test whether there is a significant (general) within group trend with data from many groups

I am having trouble identifying the correct statistical method for the following problem: I have data on a characteristic (e.g. body length) from several individuals per species, distributed in an ...
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How to interpret the coefficient of a limited independent Variable (Index)?

I assume this is a very simple question, however I am not sure about it. I have a regression table in front of me that contains the coefficients of a linear regression. The dependent variable is ...
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How to optimize a clinical scoring algorithm?

I've made two studies on clinical data that correlates with a disease. The clinical data can be aggregated into a score, such that the higher the score the higher your % of having the disease. However,...
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1 vote
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Combining/updating parameters from multiple estimations

Take a simple example of performing two independent linear regressions on a set of x-y data, in the form of y = mx + b, each using half of the data. I will obtain two separate estimates for m1, b1, m2,...
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2 votes
1 answer
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How do I interpret a regression model when there are impossible additive effects?

Let's say I have a model of count data as a function of the month of the year along with an additive effect of season (factor with 2 levels Wet and Dry which correspond to Jan - June and July to Dec ...
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1 vote
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What is the difference between lm() function and caret::train() function when it comes to creating linear regression models? [duplicate]

When applying the lm function as follows (the assumptions were not considered. The purpose of this example is just to make my question clear) : ...
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1 answer
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Entropy Balancing and regression

I have a panel data set consisting of a treatment and a control group. The control group contains much more observations than the treatment group. In order to adjust some specific Variables between ...
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What is the inverse normal transformation (INT) and what are the reasons behind using it?

I noticed a statistical method called inverse normal transformation in the following research article FTO genotype is associated with phenotypic variability of body mass index. I attached the ...
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1 vote
1 answer
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Panel data regression with time varying treatments and fixed effects

Experts, I have some trouble concerning my regression model for a panel data analysis. The dataset includes observations of 200 firms over a period of 6 years (2000 - 2005) regarding merger activities ...
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1 vote
1 answer
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Question on estimating (OLS) the ATE of RCT with multiple (2) treatments

Updated: I do not have enough points to comment so... Thank you Ben, you did interpret my question correctly. There are three treatment categories: control, A, and B. Thank you for clarifying that ...
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How does one deal with linear regression with heteroscedasticity?

Suppose I have a dataset with outcome continuous. I applied various transformations on either covariate, outcome or both. I have also tried polynomial terms. I always get over heteroscedasticity when ...
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Marginal structural model - help with some concepts

I'm trying to gain some (deeper) understanding of MSM's - what exactly they are and when they might be appropriate to use. Are my thoughts on the following correct (please feel free to correct any ...
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2 votes
1 answer
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How do we make predictions for future data when you have lagged dependent features used in training?

I am executing a lightGBM model to forecast my units sold (qty) over a period of time. Objective is to run a model for each product group and be able to capture the trends, price elasticity, etc and ...
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1 answer
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Anova with stratified Cox model

I'd like to use anova to investigate which variables are important to my Cox model outcome. I tried using anova from ...
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2 votes
1 answer
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How to represent the interval or uncertainty on regression predictions in an 'experimental vs predicted' plot?

Using an example similar to the one from R predict, simulate some independent variable ($x$) data, map them to an observed ...
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Ordinal regression or Spearman rho

I am a complete beginner in statistics and I am confuse which method is more appropriate. I have two variables; The independent variable is continuous ( hours) while my dependent variable is ordinal ...
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1 answer
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How do we compare MAPEs?

if we have two models M1 and M2: MAPE_{M1} = 100% and MAPE_{M2} = 70%$ Does that mean that M2 is better than M1? can we say that M2 is (100/70 -1)*100 = 43% better?
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linearity assumption mixed linear models: comparing two models (lmer in R)

I have a question concerning the linearlity diagnosis for lmers: Both models look very alike: (it's not the same data, but both have the same characteristics), i.e: ...
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13 views

Recommender System: How to predict user ratings using Linear Regression and User/Item Embeddings?

I hope this is the right forum for this, but here goes: I am currently doing a capstone project for a course. Part of that is building a recommender system using various algorithms such as NMF, kNN, ...
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Analyzing frequency of events in R

I have data on fire incidences: structure(list(Season = c("Winter", "Winter", "Winter", "Winter", "Winter", "Winter", "Winter", &...
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How to estimate a semi-parametric regression in STATA which is a multiple index model

There is a module in stata to run a semi parametric regression single index model as in Ichimura (1993) (https://www.sciencedirect.com/science/article/pii/030440769390114K). The module is this: https:/...
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Robust standard errors in Cox survival analysis

The final step of running a Cox model I've been working on involves performing log-likelihood ratio tests to check the significance of each predictor to the model. I'd like to do this using ...
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Standard procedure to approach Linear Regression with data having different granularity

The goal of my analysis is to estimate a model that explains the Sales as a function of my marketing and media variales. I have available weekly data but with different granularity. My dataset is ...
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How to calculate a rate of change for longitudinal data with two time points when follow up time differs between participants?

I am looking at the rate of change of cognition in people with a neurodegenerative disease compared to controls. I have baseline cognitive test data and then follow up cognitive test data. However, ...
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1 vote
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Subtracting predictions from linear regression model & stochastic error [closed]

Understanding that a linear regression model includes stochastic error, can I eliminate error from my model's predictions by subtracting one prediction from the other? Let's say we have a model of the ...
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7 views

Is the Hosmer-Lemeshow test appropriate for logistic GLMMs?

I have read a bit on the Hosmer-Lemeshow test as a goodness of fit measure in logistic regression, though I have read that it is quite flawed in terms of power, effects in choice of g, and issues with ...
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In Lasso regression, why is the intercept equal to $\bar{Y}$ when we center the columns of $X$

I am currently learning Lasso regression, and I am confused about a lecture slide that I am looking at: https://www.stat.cmu.edu/~ryantibs/datamining/lectures/17-modr2.pdf (Page 9). I attached a ...
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effect sizes from stepwise regression

I'm new to stepwise regression and I've been asked to conduct one for my boss. In doing so, they also asked for the effect sizes from each predictor in the model. Disregarding any debates around ...
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23 views

Differing statements Breusch-Pagan test vs. studentized Breusch-Pagan test

I am running a linear regression ...
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0 answers
21 views

Overall effect for non-binary categorical variable, Cox

There are many great answers on Cross-Validated explaining that the p-values for cox.zph (or other regression models) in R refer ...
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1 vote
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Minimization of error of distances

I have a set of input points $(x_i, y_i)$ and a point is drawn at $(p, q)$. This gives me a distance vector $d_i$ of L2 distances. Later, each point $(x_i, y_i)$ moves to a new point $(x_i', y_i')$ ...
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4 votes
2 answers
412 views

Can we have NAs (missing data) in a linear-mixed model?

I've kinda asked this before, but I guess that I've made it too complicated and I still don't get it right...(I'm using R, so lmer) To put it simply: can we have NAs in the outcome and/or in the ...
0 votes
1 answer
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What's the difference between statsmodels' RLM and robustbase's glmrob?

The Python package statsmodels comes with robust models of linear regression (RLM, https://www.statsmodels.org/stable/rlm.html). ...
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Interpretation of psplines in Cox model output

I have used psplines() to account for nonlinearity and to help with cox.zph violations in my Cox model. I'm aware that the ...
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3 votes
1 answer
234 views

Alternative interpretation of multiple regression coefficients?

If we have a linear regression of the form $$ Y = \beta_0 + \beta_1X_1 + \beta_2X_2 $$ is it valid to interpret the coefficient $\beta_1$ as the associated change in $Y$ when $X_1$ increases by a unit ...
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1 vote
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R: What's the correct way to run an ANOVA with a continuous DV, one continuous IV, one categorical IV, and a blocking factor?

I'll TL:DR this in case if it's too much to sort through. Depending on method: either lmer(), lme(), or ...
6 votes
1 answer
379 views

conceptual understanding of quadratic regression

It's clear to me how to interpret the coefficients of a quadratic regression: ...
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1 vote
0 answers
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How does one deal with missing data of unbalanced longitudinal data? And what about multivariate case?

For balanced longitudinal data, the missing data can be handled by multiple imputation in wide format where I assume MAR. However, for unbalanced longitudinal data(i.e. number of measurements varying ...
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2 votes
1 answer
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Is a general linear model a "system" of linear regression models?

I was trying to understand the difference between a multiple linear regression model, a general linear model, and a generalized linear model. I have seen very similar questions have already been ...
1 vote
1 answer
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R: weird behaviour in linear regression [closed]

I am trying to practice with R through some problems, one of them says: ...
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Using consistency to prove unbiasedness of linear regression

I am not sure if this question has already been asked, but basically in an interview I was told that using consistency, we have that $$E(\frac{Cov(X,Y)}{Var(X)}) = \frac{E(Cov(X,Y))}{E(Var(X))}$$ ...
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1 answer
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How to control for a variable in a (Cox) regression, but not include it in a predictive model?

I have been analysing some survival datasets for lung transplant patients for the past several months. I have found some variables that are statistically significant - such as year of transplant and ...
1 vote
0 answers
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Regression on imbalanced and zero-inflated data. How to deal with less frequent values?

I am implementing a regression, however my regressor has not been able to predict the least frequent counts. I've tried adjusting the hyperparameters (as you can see below), but I haven't had much ...
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0 answers
28 views

predicting a real number in fixed range

Suppose we need to predict a real number in fixed range, for example, [0 .. 5], and Y can be 3.14, 2.4654 etc. What is the name of this kind of tasks (to be able to search further) and what are the ...
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ANCOVA with two dichotomous IV and three covariates - how to interpret interaction between IV

As part of my thesis I am investigating how emotions influence food preference predictions between romantic couples. I've run an ANCOVA with one continous DV ...
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how to determine linearity between dependent and log transformed independent in a logisitic model

0 I am trying to assess the impact of treatment strategy (categorical three level) on outcomes following an injury. The outcome is a binary outcome and I am building a logisitic regression model to ...
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4 votes
2 answers
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Help: Partitioned samples efficiency in OLS compared to one sample regression

As usual, we can estimate by OLS the model (in matrix form) $Y=\alpha+\beta*X+u$ with a sample of $n+m$ observations. The OLS estimator is $\hat{\beta}=(X^{T}X)^{-1}X^{T}Y$. Now, if we partition our ...
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