Questions tagged [regression]

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

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Why OLS perform better than LASSO?

I am comparing OLS and LASSO regression for survey data. I have n>p, but I think my data is high-dimensional data as the p is 3000 and n is 48000. I am using k cross-validation. The results are ...
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What are the advantages of using a Machine Learning (NN) method instead of regression model in survival analysis?

Suppose that I have a sample of survival times $t_1,...,t_n$, censoring indicators $d_i = I(t_i < C)$, and covariates $x_i\in{\mathbb R}^P$. Suppose that I have a flexible parametric regression ...
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What is correct way to deal with incomplete uncertainty array for curve_fit?

I have a pandas dataframe that looks more or less like this: ...
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Distribution of Residuals and Response - When to use which?

I would like to analysze the relation between my continuous response 'soil moisture content [%]' and 2 categorical and 1 continuous predictors. I fit a linear mixed model and checked the distribution ...
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Can introducing time fixed effects variable into a PanelOLS decrease overall and between R^2?

I am trying to find if there is a relationship between the number of people employed by the tech industry within a city and wages in that city. I ran two Linear Regressions on my data. The first one ...
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cluster by multiple linear regressions

If I have an X and Y feature, can I cluster by what linear regression two populations fall on? Consider: In the pink is one regression. In the yellow is another. I know that these two lobes are ...
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How do I analyze my data with regression analysis?

I am a college student currently doing a research paper so I don't have much experience with analyzing a data table. Our group decided to conduct a survey and I have collected a data table from an ...
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How to isolate out groups from a sample

Lets say you are doing a study on a group of people in which you record their weight over time and get an average chart of weight/time. You suspect that this average chart is not really a good ...
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why the sum of two variables is insignificant while each of them are?

When I regress stock returns of period $t$ on stock ownership of period $t-1$, the coefficient on this lagged stock ownership is insignificant. However, when I disaggregate the lagged stock ownership ...
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Power with a hierarchical multiple regression analysis [duplicate]

I was wondering how to calculate the power with the below data? I did a hierarchical multiple regression with two models. There are a total of 87 participants and the alpha level is 0.05.
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How to calculate your power in GPower? [duplicate]

I was wondering how you calculated the power via Gpower with these results? There are a total of 87 participants and the alpha level is 0.05.
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How to calculate power?

I was wondering how you calculated the power via Gpower with these results? There are a total of 87 participants and the alpha level is 0.05. It is a longitudinal design in which I performed a ...
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Which method of logarithmic transformation is correct to use in linear model?

I try to do a simple linear model with logarithmic transformation of y values. I found that depending on which method I use the results differ and I don't ...
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how to interpret classes dependence that are not the reference class in a linear model

If we run the three following codes: ...
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Interpreting DHARMa residuals for a glmer.nb regression using count data

1 I am modeling overdispersed count data (detection of species) in a GLMM to account for changes in the number of detections of the individual (response variable) to covid period, area (rural vs urban)...
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How to measure marginal effect of interdependent variables on a binary outcome?

My data represents observations on a possible sequences of events that may lead to a positive outcome (y). Each event (A, B, C, D) is dependent on the previous event; for D to occur C must occur, for ...
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Interpreting DHARMa residuals for a glmer.nb regression using count data [duplicate]

I am modeling overdispersed count data (detection of species) in a GLMM to account for changes in the number of detections of the individual (response variable) to covid period, area (rural vs urban) ...
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Ordinal Regression with Categorical Predictors in R (Proportional Odds Logistic Regression)

My independent variable is Party Identification which I think will be best to create dummy variables for (Conservative, Labour) and then Lib Dem would be the intercept if I'm not mistaken. The ...
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Difference in means vs OLS regression coefficients

Suppose I have a data set where each row represents a test subject. There's a dependent variable (y) and two binary columns (x1, ...
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Difference between y against x, x^2 and sqrt(y) against x in a linear model

Suppose that we have a response variable y which is known to have a quadratic relationship with a predictor variable x. What are the differences between fitting a linear model of y against x and x^2, ...
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Post hoc power analysis for multilevel regression analysis

I have a multilevel model with 2 levels (L1 = individuals, at least 710 per country; L2 = countries, 17 total) ...
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For all datasets with a binary outcome, will linear regression always yield betas with a smaller standard error compared to logistic regression?

Any cases where the betas' standard errors from logistic regression will be smaller than linear regression, after converting from log odds space to probability space?
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Regression left limited dependet variable

My scope is to analyze the impact of certain variables on the change in sales. As you can see, my dependent variable is a proportion of two variables and is limited to -1 (-100%). On the other hand, ...
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Why have predictions for neural network regression wider error margin for edge values?

I am doing a simple neural network regression and I notice my predictions always have high variation at the edges (values 0 and 1, in a normalized case). An image of the true value versus predicted is ...
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Incorporating prior evidence of predictor having no effect in bayesian linear regression model

Say we start with a linear regression model of the form $$y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \epsilon, \quad \epsilon \sim N(0, \sigma^2)$$ with the conjugate prior $$ \begin{align*} &\sigma^...
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build a linear regressor with labels in different scales

I just ran into this linear regression problem where the labels are in entirely different range for example for 25% of the samples, the labels are in [0.001,0.01], then for another 25 % of the samples,...
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Linear Regression as a descriptive model?

I understand that linear regression can be used as a descriptive model (also am well aware that the regression coefficients should be interpreted as correlations rather than causation). So I was ...
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Can I improve linear model coefficient estimates using group information without working it into model?

I am fitting a linear model in order to predict future observations. The training data consists of about 1000 observations. Each observation comes from one of 10 individuals, which means I have about ...
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Measure of goodness-of-fit in errors-in-variable regression

I have two observed time series $x_i$ and $y_i$ and I want to test if $x_i$ is a good predictor of of $y_i$. So I would usually run a simple linear regression Y ~ X and use $R^2$ as a measure of ...
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Which model should I use for Time series data?

I need to regress one dependent variable (dummy variable), against several other independent variables (dummy and non dummy variables). (FYI : I'm not using the past performance of dependent variable, ...
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What if an variable in a multiple linear regression is insignificant and all five other variables are significant!

I am currently working on a paper, during the regression analysis of the variables I have one dependent variable and six independents. After the regression, five of the variables turned out to be ...
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5 votes
2 answers
686 views

Correct formula for MSE

Throughout my student life so far, I have always considered the mean squared error to be calculated by $ MSE=\frac{1}{n}\sum(Y_i-\hat{Y}_i)^2$. However I was looking at one of my statistics mod today ...
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Bayesian Regression Credible Intervals/Standard Deviation extremely large

Apologies if this is the wrong place for questions that overlap with programming. I'm trying to implement my own Bayesian regression class, but I'm finding my credible intervals are coming in much ...
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EDA with Data Modeling

After I read R for DataScience and ggplot2: elegant graphics for data analysis, I am learning how use modeling techniques to improve my EDA. I applied this on two notebooks (https://www.kaggle.com/...
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1 answer
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Is a huge F-statistic OK?

I ran a multivariate regression model in R and got an overall F-statistic of 3,525.690 and three stars attached to it. This seems to be quite good from a statistical point of view. However, should I ...
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Is my survey data structured correctly to run OLS regression in R?

I have data from two surveys that were launched at the same time and am trying to find whether there is a statistically significant result on the number of respondents as a consequence of various ...
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Interpreting logistic regression odds ratios: proportional odds

I am not understanding the interpretation of the odds ratio from logistic regression coefficients. I understand that if $\beta_1$ is sex, with male the reference group, then $e^{\beta_1}$ gives the ...
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How to treat dummy variables and their interactions terms with an endogenous variable in a IV context?

I ran the following model with the instrumental variable Z1 because I think that X is endogenous: ...
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Variable scales in latent class or profile analysis

Do popular implementations of latent class or profile analyses automatically handle indicator variables of different scales? For example, if variables with similar distributions range 0-1, 0-1000, 0-...
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Out of Sample Regression errors

I am trying to compute $\text{R}^2$ and $ {delta RMSE} $ from an Out of Sample Linear Model in R. $ e _{ N }$ is the vector of rolling OOS errors from the historical mean model $ e_{A}$ is the vector ...
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Linear Regression vs. Gaussian Process Regression [duplicate]

I am trying to understand the intuitive differences between (Classical) Linear Regression and Gaussian Process Regression - intuitively, I have been told that Gaussian Process Regression is able to ...
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On the defense of "change from baseline" even in randomized trials - can anyone question points in this article?

Many times I read a strong criticism on the change-from-baseline (adjusted for baseline or not) both in randomized and non-randomized. Several people advised "don't even think of reporting the ...
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-2 votes
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linear regression with variables as linear combination [duplicate]

Consider a linear regression model with two variables x1 and x2. Suppose, I fit a new model with two new variables x1+x2 and x1-x2. Are these two models equivalent? What is the relationship between ...
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Posterior Predictive Distribution of Latent Function Values in Bayesian Linear Regression

In the book Gaussian Processes for Machine for Machine Learning, the authors review Bayesian linear regression. For the setup, we have $f = x^Tw$ and $y = f +\epsilon$ where $\epsilon \sim N(0, \sigma^...
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Regression - fitting with linear combination [duplicate]

Consider a linear regression model with two regressors x1 and x2. Suppose, I fit a new model with regressors x1+x2 and x1-x2. Are these two models equivalent? What is the relationship between the ...
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1 vote
1 answer
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Baseline variable in regression

I am currently looking at this paper: https://www.nature.com/articles/s41591-021-01487-3 The equation for (1) includes a variable for a baseline value. I am confused as to why they do this as I ...
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Regression model using two collinear time-related variables?

I am building a regression model to assess how a certain outcome, tracked from 2015-2018, changed in the year 2018 specifically relative to 2015-2017. The outcome underwent a natural year-by-year ...
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2 votes
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Multi-categorical variable in linear regression

I am trying to explain the variance in "wages" across different groups of the population. In my model, "wages" is a dependent variable. The key independent var is "groups"...
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Comparing RMSE/MSE of original data and log10-values of the same data

I want to see if my models work better on the original data or on log10 transformed data. But how can I do this? Normally I train the model, calculate the RMSE/MSE on the test data and compare these ...
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1 vote
1 answer
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What to do when the p-value is not significant?

What can I do when the p-value of my regression is not significant? I tried to transform it with log or sqrt, it improved a little, but not enough to go below the 5%. The residuals follow a Normal ...
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