Questions tagged [regression]

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

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Linear regression dependencies of the evidence

In linear regression in order to calculate the posterior distribution $ $$p(\mathbf{w}|\mathcal{D})$ we need to perform the Bayes rule using the likelihood, prior and evidence as follows: $$p(\mathbf{...
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Is it okay to specify interactions before main effects in a linear model (and ANOVA?)

I would like to specify some interaction terms before all main effects in a linear model, but am finding it difficult to do in R, so I am wondering if there is a statistical reason why I shouldn't? ...
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Heteroscedasticity and non-normal errors are only an issue when predicting from a linear model - why?

In Regression and Other Stories, the authors state that heteroscedasticity and non-normal errors are only problematic when predicting from a linear model (1; p. 154-155): Equal variance of errors. ...
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Detecting biasing columns and removing bias in machine learning

I came across a multivariate machine learning problem in which I need to detect the biasing column and remove the bias of that particular column in predicting final target variable. train data-> [...
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Do I need to transform this data for linear regression? If yes, what type of transformation is best?

The picture below is the dependent variable on the Y and one of the IVs on the X axis. The Dependent variable is range bound between .5 and 12 while the IVs range from 0-over a million depending on ...
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Adjusting curve in logistic regression

I have binary data (temperature vs sex). I fit a logit model and got the value of x at midpoint i.e the temperature where probability of being male of female is 50%. I have attached the curve below. ...
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1answer
44 views

Fitting a survival model to see if there is interaction between gender and age

hi I am new to survival analysis. I am trying to fit a cox regression model with age, sex, type of case(local vs imported cases), and regions(urban vs rural). I tries to fit two model ModelA ...
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1answer
23 views

Out of sample versus in-sample predictions

I am running a simple linear regression between labor force participation rate (LFPR) by country, and log GDP per capita. regress avg_LFPrate log_avg_gdp With the ...
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Testing L2 effects without any L2 variance?

I'm testing a hypothesized effect of an L2 variable on L1 outcomes (students nested in classrooms), but a few of my outcome variables have no L2 (classroom-level) variance (i.e. the within-classroom, ...
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22 views

Sample size in logistic regression - ouput with R

I think im missing smoething. How I calculate the sample size from this output - ?
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3- and higher way interaction effects

Assume I have a simple model with one dependent variable $Y$, two moderators, $W,Z$, one predictor $X$. To keep it simple, I want to model 3-way interaction effects. My question is if we can model 3-...
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Dummy in a VAR-Model with more than one Lag - how to specify?

I am estimating a VAR-model on German data, which has a strcutural break due to the reunification. I want to depict the structural break with a dummy that is 0 before the reunification and 1 ...
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Beta regression or fixed effect regression

I am using panel data for 27 different countries in 5 different time periods. My IV and DV both are in fractional form (0,1), i.e., in percentages. Please guide me, if beta regression is appropriate ...
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how is constant added log transformation useful

I want to transform the response variable that has both negative and positive values. When I looked at this, I saw that most people recommend constant addition to the variable and then take the ...
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Can I partial out covariates with true coefficients known in Logit? [closed]

Suppose the model is $$ \ln(\frac{P}{1-P}) = X\beta_0 + A + \epsilon_{ij} $$, and I know $\beta_0$ and thus $X\beta_0$. Think of A as a high-dimensional fixed effect term. Can I partial out the effect ...
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How to interpret covariate effects from gamma regression (JAGS)?

I'm new to gamma regression and I'm trying to run a model with basically this set up from a Sean Anderson tutorial: ...
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1answer
33 views

Use dummy in the regression or not

I have the following data: The data shows information about prices for different kinds on different days (days are the same across the kinds). The volume shows the number of spare parts bought by the ...
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Multi-output Neural Network only predicting one value

I have been using LSTM multi-output Neural Nets to perform two tasks, regression coupled with a classification. The data is in a time-series format where my dependent variable is trade quantity ...
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3answers
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mixed linear model for repeated-measures

I am trying to analyze if I can expect some differences between regions in the following dataset. I believe (I am not sure at all) that one way to do this is to use statsmodels: I have got 4 subjects, ...
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Logistic regression model

I have a question about binary logistic regression. I have been trying see which IV are the independent predictors of an outcome of a categorical variable ​in a sample of 680 cases, where 30% of cases ...
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Predicting events - seizures in epilepsy. A question about time series models matching with observations

I've been keep a diary of epilepsy seizures, and would like to attempt prediction modelling as an help for better management of anti consultant therapy. Could you help to suggest models that fit with ...
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What kind of machine learning models fit data of this kind?

I have been working with a manufacturing process. It would be very efficient to build a machine learning model for the kind of data that I have. So, my dataset has typically three inputs. VAl_1 VAl_2 ...
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Why adding polynomial terms make linear function non-linear?

I would love to know why polynomial terms could make non-linear functions. From my understanding, it is just about using the current independent variables, so the relationship between $x$ and $y$ ...
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Equivalence RKHS Minimization

Take a kernel $K$. You want to use this kernel in a regression context. I read that the kernel rigde regression solution with kernel $K$ is equivalent to redefine the kernel $K$ as $K_{eq}(x,x') = K(x,...
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interpret interaction effect in linear mixed model with dummy-coded categorical predictors with lmer

I've looked through quite a few websites and threads here, but I find the interpretation of interaction effects in linear mixed models with categorical factors quite tricky and would be glad if ...
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Sampling variance of regression coefficient of change versus baseline

Suppose that $y_1$ and $y_2$ are samples (size $n$) from a bivariate normal distribution with correlation $\rho$ (unknown). I perform a linear regression (OLS) of $y_2 - r y_1$ versus $y_1$, where $r$...
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Stratified Intercept in Individual Patient Data Metanalysis in R

I am trying to run an Individual Patient Metanalysis (IPD) in R. However, using a Random Intercept Model seems not to be appropriate since I only use 4 Trials. In this article a stratified study ...
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Can Simple Multiple Regression be applied when you have a Training Set where the number of features is greater than the number of examples? [duplicate]

Suppose we have a Training Set $X$ of size $n\times d$, where $n$ represents the number of examples and $d$ represents the number of features. Assume that $d>n$, so the number of features is ...
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Multiple regression variable centering when model building, to model input variables or both?

I am working with GLS linear models. I have a transformed dependent variable (log) and 2-3 independent variables. Now I am aware of the basic ideas surround scaling and/or centering variables. I am ...
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How does Basmann’s 2SLS differ from Theil's 2SLS?

I am reading Diagnostics for 2SLS Regression. In the paragraph "Review of 2SLS Estimation", the 2SLS regression is reviewed. It says that the 2SLS regression was developed by Theil and ...
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Logistic regression to shortlist marketing agencies

I have received a very ill formulated question that I have a hard time wrapping my head around - so I hope you can help! INTRO: Top-beer is a local brewery in US, where you work as a digital marketing ...
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Normalizing the columns of a matrix through matrix multiplications

Generally the normal equation is derived using a calculus-based approach of minimizing the least squares error. I'm trying to learn the linear algebra approach. My understanding so far is as follows: ...
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Should I perform Tobit/Ordinal Regression instead of OLS?

I'm using regression to understand what demographic variables explain playtime and performance in an educational video game. Demographic variables are age is numerical while gender, race, income, ...
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Confidence interval for Regression Trees

Many people think the regression tree is only an algorithm and it doesn't make sense approach confidence interval to it so I'd like to know if there's anyone figured out how to do it. A regression ...
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How to visualize multivariate linear regression? [duplicate]

When I have data which has one dependent and one independent variable, I can clearly explain it in cartesian coordinate system here is a regression line when my $y$ approximately equals to $2x + 3$ ...
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1answer
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Confidence/Prediction Interval

Is it possible to establish a CI on forecasts/budget projections that are more subjective in nature, ie: no model was used for forecasting purposes? Is it reasonable to use linear regression on both ...
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Is multiple regression the correct approach? [closed]

I'm having trouble deciding the correct approach to analyze a dataset. Say I have a big dataset of all the answers of a survey and I want to do profiles of the respondents that answered "NO" ...
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How to measure relationship between one independent variable and multiple dependent variables when DVs are both continuous and categorical? [closed]

How can I measure the relationship between one independent variable and multiple dependent variables, but the dependent variables are a mixture of both continuous and categorical variables? The ...
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1answer
41 views

How do I eliminate Linear dependence in a diff-in-diff model in R?

I am using panel data to try and observe the effects of Vietnamese immigration in the California Bay Counties in 80s. I am using R. I am regressing average weekly wages (adjusted for inflation) on the ...
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1answer
228 views

how to deal with heteroscedasticity in least squares regression with multiple independent variables

I am trying to build a least-squares regression model and when I analyzed the independent variables, I saw a case of heteroscedasticity in one of the independent variables. I'm building this model in ...
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Partial Residual Plots and Interactions

In linear regression, we can use partial residual plots to decide on potentially nonlinear transformations of a quantitative covariate $X_j$. They plot the residuals $r_{Y|X_{-j}}$ on the y-axis and $...
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intepreting AIC and drop1 of models

Consider the following case: we have continuous response A, and indicator B,C,D. ...
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Is there any simple description about cluster?

I saw the single cluster and multi-way cluster quite a bit in some finance papers (cluster for standard error). I am asking for some simple explanation about what is cluster and why we need to do ...
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2answers
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Definition of partial R-squared?

I'm trying to understand the definition of Partial R-squared values in the context of a regression model. Does anyone have a layman's definition or an intuitive example that might help me better ...
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1answer
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Output in two-part Fractional Response Model (FRM) package incorrect?

I was experimenting with the Fractional Response Model (FRM) package, and decided to replicate the results using the base GLM package to better understand the theory. I am able to replicate the ...
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1answer
22 views

How to Improve an Elastic Net Model?

I have a dataset with $n=1500$ observations and $p=2700$ variables. I fitted an Elastic Net model with $\alpha=0.4$ and $\lambda=0.1$ I chose the $\lambda$ with cross validation, and the $\alpha$ ...
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1answer
40 views

Sample size confounded with factors (ANOVA)

What do you suggest doing when sample size is confounded with factors in an ANOVA? "For example, in a two-way ANOVA, let’s say that your two independent variables (factors) are Age (young vs. old)...
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Linear Regression - predicted values constantly below true values

just a short question, which may be easy to solve for most of you. I am just starting with linear regression models in python. Therefore I made a simple multiple linear regression with training and ...
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Data acquisition for subsequent global/bayesian optimisation

For a research project, we are interested in applying global/bayesian optimisation for engineering an enzyme for optimal/maximum efficiency. The assumption is that there is a functional relationship $...