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Questions tagged [regression-coefficients]

The parameters of a regression model. Most commonly, the values by which the independent variables will be multiplied to get the predicted value of the dependent variable.

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Is it appropriate to use 3 linear regressions to assess the impact of one common independent variable?

I need to assess the impact of cultures on urban economies. The hypothesis is that more entrepreneurship oriented culture will better facilitate the local economy, controlling for other factors (that ...
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Glmnet() returns different coefficients [on hold]

I want to get the regression weights from glmnet(). However, I get different coefficients with different methods. ...
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Interpretation of higher coefficient for group with smaller mean

I am running a fixed effects poisson model with robust standard errors in STATA (xtpqml). The model I run it on has my count data as dependent variable and then as my independent variable I have a ...
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In linear regression, any intuitive explanation why duplicating samples will reduce coefficients' std-dev? [duplicate]

I read the explanation by Ocram here about how to calculate the stddev of coefficients in linear regression. I also run experiment with my sample data. I have test1 which contains 1000 samples; I ...
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Interpreting Logistic Regression Categorical Coefficients

So I have this question: If we fit a logistic regression with categorical predictor X with categories A, B and C, and have the estimated coefficients β0=−2.5 and βB=0.5 and βA=−0.2. (a) Interprete ...
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relation between regression models [closed]

For the regression model $y_i =\beta_0 + \beta_1 x_i + \epsilon_i$, several times the change of variable $z_i =x_i -\bar{x}$ is done and the regression model $y_i=\phi_0 + \phi_1 z_i$ facilitates it. ...
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1answer
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how to interpret/report estimated spatial lag coefficients

I estimated a spatial lag model with pysal. I want to know how to correctly interpret and report the resulting parameter estimates (given that spatial spillover exists). R's ...
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Does the high coefficient of determination in this graph predict a huge spike in global warming? [closed]

I have had three semesters of college statistics as part of my BSBA degree. From what I recall from regression analysis the graph seems to show a very high coefficient of determination between CO2 ...
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Why is the intercept in multiple regression changing when including/excluding regressors?

I have a seemingly naive question regarding the interpretation of the intercept in multiple regression. What I found several times is something like this: The constant/intercept is defined as the ...
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Converting coefficient of slope to autoregressive factor

I realize this is very fundamental. I apologize. Is there any way to convert the coefficients from a linear model into the decay factor if i want to express it as an autoregressive model? For a ...
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Interpretation of a quadratic term on a log transformed target variable

I've done some searching and found several posts related to this, e.g.: In linear regression, when is it appropriate to use the log of an independent variable instead of the actual values? Suppose I ...
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Interaction term and main effect multicollinearity [duplicate]

If I have the predictors $X$, $Y$, and $XY$ to fit a linear regression model. Won't I be increasing the standard error of the regression coefficients? This is because $XY$ is collinear with $X$ and $...
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In Simple Linear Regression $\hat \beta_1$ and $\bar Y$ are independent [duplicate]

I want to show that, in simple linear regression $\hat\beta_1 $ and $\bar Y$ are independent. My attempt: I have calculated the $\mathcal Cov(\hat \beta_1,\bar Y)$ and it turns out to be $0$.I also ...
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1answer
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Get odds ratios with confidence intervals from a lasso regression model

I try to understand lasso regression. So far, I do understand that it can be used to shrink regression coefficients in case of few events. The coefficients of some covariates are even shrunk to zero. ...
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1answer
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Interpreting statistical significance

Suppose I repeatedly run the same regression model for a set of individuals and I am interested in determining whether a given independent variable has a statistically significant impact on the ...
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Quantile regression and linear regression coefficient comparison

I am trying to understand the concept of quantile regression by modelling the monthly expenditure on insurance on several variables. The R package ...
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1answer
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Provide a precise and concise statement on what a simple linear model is

I have recently commenced a 2nd-year course on linear models and have been a little overwhelmed by either the abuse of notation or the lack of clarity behind what a linear model is. I've read multiple ...
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How to perform a multiple non linear regression without knowing the functions for each variable and the constraints for their coeffcients?

I have a data for number of cars and its causal variables are identified as GDP, population, urban fraction and fuel price but they have non-linear positive correlation but I don't know what that is. ...
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compare coefficients from different regression

I estimate the following models using the Hausman-Taylor estimator: $$y_{i,t} = a_{0} + B_1 controls_{i,t} + \beta_1x_{i,t=2000} + B_2 Year_t + B_3 x_{i,t=2000}*Year_t + e_{i,t}, (1) $$ $$y_{i,...
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Log level model - do I exponentiate all coefficients for interpretation?

I'm working with a regression model where I have a log transformed target variable due to the distribution of the log transformation being more normal. I have some numeric variables and also some ...
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1answer
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Does High Information Value (IV) for a variable implies high coefficient in logistic regression?

I'm performing a Logistic regression for a binary classification task. As a preprocessing technique I use a transformation with WOE and Information value(IV), but I found something counterintuitive ...
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Linear Regression with Changes

Consider two variables with levels over two time periods $\{y^i_t,x^i_t\},\{y^i_{t+1},x^i_{t+1}\}$. For example, it could be profit and cost data of various firms over two quarters. Suppose I take ...
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Test significance of weighted average of multiple regression coefficients from different models

I have 3000 independent time series samples (customers) where I fit a dynamic regression model with ARIMA errors to each sample and estimate regression coefficient of interest (intervention), $B_1{_i}$...
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Spatial Lag or spatial Error Model? Deciding by using the Lagrange multiplier diagnostics

Honestly, my knowledge of geostatistics is limited. My assumptions are as follows: If I want to choose between a Spatial Lag Model (SLM) and a Spatial Error Model (SEM), I can use the Lagrange ...
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1answer
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Capturing effects / Controlling for variables [duplicate]

I understand the idea behind regressions and know how to interpret them, however, when I hear the term "capturing the effect of.." or "controlling for.." so far I've just accepted it without ...
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Selected variables varies depending on whether or not standardization is in lasso regression (glmnet)

The paper often suggests both standardized and unstandardized coefficients in the lasso model (glmnet in R). However, when I run glmnet, the selected variable is different depending on standardized =...
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SVM as linear equations

I'm using SVM for a regression problem (sklearn.svm.SVR). After I train my model I use these 2 attributes svr.coef_ and ...
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How to compare LME regression coefficients across models with same variables but different sample sizes/trial numbers?

I have a quite specific situation that does not seem to be covered by other, similar posts: I ran a study where a task (Task A) was periodically interrupted by a probe that asked participants what ...
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Using a subset of parameters in joint confidence region of a linear model

For a standard linear model of the form $y = X\beta + \epsilon$, where $\beta$ is a vector of parameters. we can calculate an individual confidence interval for each parameter (of 1-$\alpha$ quartile)....
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How to interpret the resulting negative regression coefficient? [duplicate]

How to interpret the resulting negative Poisson's regression coefficient? I am investigating the effects of environmental factors on mortality. A negative regression coefficient means feedback (proves ...
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1answer
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How can I compute the standard error and confidence intervals for the base level on a variable?

I'm running a GLM with a tweedie, log-link function. That said, I have a categorical variable that transformed to dummy variables leaving off one of those variables when I modeled. Now that I'm ...
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How to measure the “Impact/influence” of a feature y on Logistic regression model based on the coefficients?

I have a dataset X which contains probabilities returned for classification 4 different classification models, say M1, M2, M3, M4 those probabilities are use to feed a fourth model M4 and that model ...
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Refining a difference-in-difference analysis

I'm doing a difference-in-differences analysis with one pre-treatment time (0), and two post-treatment time points (1,2). My basic regression model is: = $β_0+β_1T_1 +β_2T_2 +β_3S+ β_4(S∗T_1)+β_5(S∗...
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Ordinal Logistic Regression in R - Understanding coefficients

I am creating an OLR model using R with the polr function in the MASS package. All of my predictors are also ordinal data, all of the data is the integers from 1 to 5 coming from a customer survey. I ...
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1answer
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Incorporate continuous group level variable in a hierarchical model?

I aim to assess the effects of difficulty (continuous variable) and trial type (0/1) on whether a subject has been correct in a logistic regression model. However, I have also measured subjects ...
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1answer
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R-squared and sample size

I was wondering if R-squared is affected by the sample size? Is adjusted R-squared also affected? The reason behind this though is, that i have run a multiple linear regression on two samples. The R^...
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Dynamic regression, models with coefficients = 0 chosen as top models

I am running auto.arima on part of a time series (training data) using all possible combinations for several external regressors. I then choose the top 5 models according to fit to testing data using ...
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Interpretation of functional regression models for scalar response

I have an application scenario in which I want to determine a single outcome from the course of a series of measurements. I decided to give functional regression a try, so I read and ran the example ...
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Incorporating rank-ordered logit results from different samples

I would like to create rank-ordered logit models to predict the outcome (winner in this case) of variants of a multi-player game. For the most part, the predictors for each variant differ. However, in ...
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Shapley value vs ridge regression

My goal is to get the feature importance for multiple regression. I have a data set with some multicollinearity. I found two methods to solve this problem. The first one is the Shapley value. ...
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1answer
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Testing the difference between two independent regression coefficients

I would like to test the difference between two independent regression coefficients. David C. Howell's book 'Statistical Methods for Psychology' (Chapter 9.11) suggests that there is a t-test for ...
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Results of a survival analysis change when converting the data to counting process format

Consider the following simple example: ...
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How to calculate coverage rates for 95% confidence intervals for estimands (like regression coefficients)?

I'm working on a Synthetic Data Generation model, and I'm confused about a metric mentioned in a seminal paper (details of paper added below) Context: Synthetic Data Generation involves sampling from ...
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OLS reinterpretation (?)

I have read on a book written by a professor something similar to the following and want to check this statement on the forum, since it is the first time I have heard it. I have read that the OLS ...
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1answer
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Determining Intercept for Regularized Logistic Regression

Going off of the standard set up, we have $N$ observations and $P$ predictors stored in the data matrix $\mathbf{X} = \{ x_{i,j} \}$ for $i = 1, \ldots, N$ and $j = 1, \ldots, P$. The response is ...
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How do I compute a Cohen's d from b weight and standard error?

From Table 3 and the paragraph below, you have a good deal of the information I have available. N's for taser and non-taser conditions are 339 each. My initial inclination is to take the raw B weight ...
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How to get this coefficient in multiple linear regression?

I'm reading a paper in epidemiology. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2744485/ While I'm reading this article, there is a formula about multiple linear regression.(in this paper, (2) is ...
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2answers
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addressing the effect of the independent variable on the dependent variable for 2 different types of individuals

I am estimating the effect of a continous treatment X (that goes from 0 to 1) on a dependent variable y (data is taken through an experiment). I have around 250 Individuals in my dataset that can be ...
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Understanding subgroups and covariates in linear regression

I'm trying to better understand how adding covariates, especially possible confounders, to a linear regression affect the regression results. I also want to better understand the relationship between ...
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4answers
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Big Sample size, Small coefficients, significant results. What should I do?

I did some quantitative research and I used Rank-Order logistic regression in Stata. The the independent variables have almost 0 p-value which shows they have significant effect on dependent variable. ...