Questions tagged [regression]

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

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PSPP Errors Running Binary Logistic Regression [closed]

I am running binary logistic regression on a dataset and keep getting NaN and +Infinit errors in my output. NaN in the model summary and +Infinite in the Wald, Sig, Exp(B) and so forth columns of the ...
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Suggest ways to represent additive fixed effects?

The dataset I'm working with has additive fixed effect where each term is significant. I want to plot the model fit with one of the factors temp here on the x-axis ...
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Interpreting interaction effects for categorical reference group in regression

I am running a regression model in R including the following variables: Intent = continuous DV Attitude = continuous IV Story = categorical IV in 4 levels: Consumer, Heritage, Vision and Product ...
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Johansen procedure shows cointegration r=1, but ect is not significant?

I have 6 variables, all of them I(1). I tested for cointegration and got a significant result for r=1, so I decided to estimate a VECM. The problem is now that the ECTs of the VECM are not significant....
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R squared of subgroups

I am trying to predict a value using a linear regression, and I get an R squared of 0.63. My data is composed of 5 different groups (each with different ...
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About the Numbers of Dummy Variables

I have a question on the choice of dummies in categorical regression. If there are 4 categories, can I just choose 2 dummy variables to represent them in the following way: Category-Z1,Z2 c1-1,0 c2-0,...
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If there is a high correlation between two variables, should we expect the high linear regression coefficient?

I have a data set with multiple features, let suppose $x_1,x_2,x_3,x_4$ and my dependent variable is $y$, when I compute the correlation matrix for $y,x_1,x_2,x_3,x_4$ then imagine the correlation ...
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Mean centering and average marginal effects

I was reading Wooldridge's "Introductory Econometrics," in which he said: "The centering of explanatory variables about their sample averages before creating quadratics or interactions ...
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Adding/removing covariates in lmer?

I am trying to add covariates in lmer, but I do have difficulty with figuring that that, can anyone help me out with this? I have this happy_plot = lmer(happy_score ~ life_quality +(1|subject), data) ...
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Binary regressor - what happens if people move from one category to the other?

Suppose I have the following regression model: Monthly alcohol consumed A as independent variable, regressed on a binary regressor S (S = 1 if smoker, S = 1 if non-smoker), and through OLS I get an ...
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Cox Proportional Hazards : Why not "Cox Proportional Survival"?

Recently, I thought of the following question: We are often taught about a "Cox Proportional Hazards Model" - this is able to model the hazard between different cohorts of patients, assuming ...
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QQ Plot help meaning [duplicate]

How can I interpret the following QQ Plot? Can you explain it for example for the point 20 and 12?
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Difference between the siginificance in the summary table and the ANOVA table in R [duplicate]

In R when I am evaluating a linear model I have created, I often use the summary table and the ANOVA table. The first image below is the output from my summary table, the image below that is the ...
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95% confidence interval for the goodness of fit scores in regression

I see the following computation available online in classification setting. ...
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1 answer
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What does it mean when there is a pattern in residuals related to the dependent variable?

I created a linear regression model and realized I first need to check some assumptions. Autocorrelation not existing -> valid, since it is a between-subject experiment. Low collinearity between ...
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How can I do this ordinal regression?

How can I perform a ordinal regression analysis in R with the data shown in table 1? I have already tried to order my data (image 1). But now I have no idea what to do next. 1 - 6 represent class 0 - ...
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Leads and Lags in a Staggered Difference-in-Differences Model Stata

I'm trying to use: 1) lags to check whether the effect increases or decreases over time, and 2) leads to check for parallel trends. I'm thinking that regressing each dummy (0 years after treatment, 1 ...
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What is the difference between regression and state-space models?

I would like to know the differences between a regression model with autocorrelated errors and state space models (time series). When should each be used? According to this lecture, regression (linear ...
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Suitable econometric specification for my data

I'm playing around with my dataset and as a start, I want to understand whether the absenteeism and vaccination rates for employees across a large number of factories with separate units is dependent ...
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Is it possible to derive the joint probability distribution of squared OLS residuals under the classical linear regression assumptions?

Consider the linear regression model, $$ \boldsymbol{y}=\boldsymbol{X\beta}+\boldsymbol{\epsilon}, $$ where $\boldsymbol{y}$ is an $n$-vector of responses, $\boldsymbol{X}$ is an $n\times p$ matrix of ...
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Gauss-Markov theorem explanation (linear regression)

I have attatched an excerpt from my linear modelling lecture notes, this is the statement of the Gauss-Markov theorem, trouble is it goes into no more detail after this (not even explaining what the ...
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Missing "None" class in outcome variable. Least bad way of handling missingness?

I'm dealing with a preexisting dataset with an outcome variable of suicide which entails the following classes, of which multiple can be selected, but they roughly escalate in severity. Check if ...
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Error-Propoagation: Combine confidence intervals of two subsequent regressions

I have two subsequent regressions and want to combine the results of these two regressions. But I also want to show the increased uncertainty by somehow combining the confidence intervals of these two ...
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Alternatives to linear model regression

Let's say I have a univariate linear regression model LMR in which ...
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Partial effect of numerical variable for a fixed level of categorical variable in regression

I have a regression model (in R) as follows: lm(price ~ time + color + brand) where, price be the second hand price of sth (numerical), time be number of years ...
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2 votes
2 answers
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Logistic regression simulation with respect to event occurrence (prevalence)

I am trying to simulate logistic regression data, but under the constraints of prevalence. $$\text{logit}(y_i) = \beta_0 + \beta_1 X_1 + \beta_2X_2$$ For example, I want to create a dataset that has ...
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Correct loss function and metric for regression of count data in neural network

I am using a convolutional neural network to predict the number of occurrences of a certain pattern in time series data. Since there might be potentially any count of such patterns in a time series, I ...
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MANOVA invalid type (list) for variable R studio [closed]

I have dataframe contain 600 columns and 50 row and i want to perrform MANOVA i tried the following code ...
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Simultanously fitting multiple regression models on one dataset

Suppose there are two groups of (x, y) pairs, and that x and y are linearly correlated but with different slopes and/or ...
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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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1 vote
1 answer
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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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6 votes
2 answers
398 views

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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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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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 ...
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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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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 $\pi_k \ge 0$...
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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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