Questions tagged [regression]

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

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Ridge Regression Alpha/Lambda: Basic Characteristics?

I fear this is an ill-posed question that has been asked a million times, but what are the basic characteristics of the penalty multiplier (usually called $\lambda$ or $\alpha$) in Ridge Regression (...
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How to compare return percentages of products in different lifecycles?

For my project, I am trying to predict return ( when a product in ecommerce sale is returned) rate of products. For the same of simplicity, assume I have 3 static features (dont change in time) and ...
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1 vote
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How to compute minimum sample size of a simple linear regression model with given statistics values

Suppose the statistics values are given as follows: $\sum_{i=1}^{n}x_i, \sum_{i=1}^{n}y_i, \sum_{i=1}^{n}x_iy_i, \sum_{i=1}^{n}x_i^2,\sum_{i=1}^{n}y_i^2$ Firstly, we can compute the regression ...
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Is there a certain method to communicate the results of earnings without logging the variable?

I am investigating whether earnings differences have widened between different social classes in several European countries by comparing two different periods. The picture below shows the findings of ...
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Multinomial logistic regression R vs Python

Does anybody have experience with the SKlearn multinomial regression (model = linear_model.LogisticRegression())? My data looks like this: ...
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Logistics model on variable with values 1, 2, 3?

I have a dataset containing traffic crash information. One variable in the set is the number of fatalities that resulted in the crash, which has the values 0, 1, 2, and 3. I am working in R and want ...
1 vote
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LME Quantifying differences between and within intervention groups

I am new to mixed-effects models and trying to ensure I understand them appropriately. I am analysing the results of a 2x2 cross-over intervention study. Essentially, I have 50 subjects who completed ...
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plm/fixed effects models: fixef function error - wrong effect argument

I am using the plm function to analyze a large dataset with 120,000 IDs over three years. My specification looks the following: ...
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What could cause regression linear models to predict exactly the mean of train set while random forests perform worse?

Data set: I'm working on a linear regression problem where my train set $X$ is of shape $(703 557, 53)$. Each row is a client's features, which could be its age, its gender, how many calls we received ...
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Testing equality of coefficients from two different samples

I have the regression statistics for the same regression run on two different samples, and am asked to explain whether it is possible to test for equality of the coefficents, $\beta_1$and $\beta_2$ ...
11 views

Interpreting Predictions from the Log-Linear Model (or Log-Log Model...)

I understand that when we fit an OLS regression to the log(y) (as either a log-linear or log-log model), the predicted value from that model [log(y).hat] cannot be simply exponentiated to solve for y....
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How to select predictor variables for linear mixed model?

I have a linear mixed model with ~30 clinical/treatment variables and repeated outcome variables for patients. E.g. The outcome variable is Breast symptom scores, which were collected at different ...
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What is the intercept in a regression model with demeaned dependent variable?

Suppose you have a regression model $\tilde{y}$ = $X\beta$ + $\varepsilon$, where $\tilde{y}$ = $y$ - $\bar{y}$ and $X$ contains a constant. If you estimate the model by OLS, does the estimated ...
615 views

Negative prediction values from linear regression in R

So I made a linear regression in R Studio to predict the price of a car based on the year of fabrication. The data set is called "audi" and my linear regression looks like this: ...
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Potential direct effect of moderator on independent variable [duplicate]

I am looking at the effect of a moderator variable (MV) on the association between my independent variable (IV) and my dependent variable (DV). I have now come across a theoretical paper which ...
57 views

IPTW in Cox Regression model using the WeightIt package - Question on ATT vs. ATE interpretation

I am currently trying to perform some IPTW adjustment in the context of Cox Regression models. I was interested in expanding my understanding of the differences between ATE vs. ATT estimation. I've ...
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Is there a fundamental mathematical reason that ordered factors are represented as orthogonal polynomials in linear regression?

At least for R, Chambers/Hastie write in their book "Statistical Models in S" in chapter 2.3.2 "Coding Factors by Contrasts": Ordered factors are coded so that individual ...
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1 vote
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Regression with 'stacked' data

I've occasionally seen people do something like the following. Let's say we have a survey battery with k questions, for instance an evaluation of red jelly beans, an evaluation of green jelly beans, ...
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How to test whether a factor differs from 50%, and which levels within the factor differ from 50%?

I have a categorical factor with 100 levels and 100 different proportions. I would like to test (a) whether these proportions differ from 50%, and (b) if any of the levels in particular differ more ...
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1 vote
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Distribution-free prediction intervals in linear regression

I've found some literature on the subject, but it is rather difficult to read. I am wondering if the following simplified method makes sense. My question is what part is correct in this methodology, ...
13 views

Adjusting for baseline when time is a covariate, what is the interpretation of $y(t=0)$?

When one aims to estimate the treatment effect over time, it is recommended to include the baseline value as a covariate. Assume continuous outcome $y$, continuous time $t$, categorical treatment \$x \...
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