Questions tagged [intercept]

The intercept in regression-type models is the value of the Y variable when all X variables are 0.

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Logit model intercept use

I want to estimate a Logit model where the independent variables are binary and one of them is categorical, so the whole data set consists of dummy variables. First of all, I am puzzled whether I ...
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Using LIME without intercept term?

I'm playing with LIME to explain the prediction of a machine learning model. LIME trains a (locally weighted) linear surrogate model around a point of interest. The weights of that surrogate model are ...
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Ordinal regression model in R - multiple coefficients appearing for only one dependent variable. Can someone help me analyze?

I am running an ordinal logistic regression model in R (with an ordinal dependent variable). For my model I am also only including one primary independent variable (which is also ordinal). However, ...
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What does c refer to in AR(p) and MA(q) model mathematical definitions?

What exactly does c represent in these formulas defining AR(p) and MA(q) models? MA(q): (https://otexts.com/fpp2/MA.html) AR(p): https://otexts.com/fpp2/AR.html
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Interpret significant intercept

I performed Wald tests on my GEE model (in fact, using anovas on nested models) and got no significant variables. Since I could not run a model without any independent variable I left distance in the ...
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Syntax for a random effect in an R regression - not sure if nested, and how to express?

I am running an ordinal regression for an experiment I have conducted, where the DV is ratings on a 5-point scale, and the predictors are the type of item, the type of participant (native speaker/L2/...
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Random effect coefficient: actual coefficient or deviation from main fixed effect?

This is a very simple question, but I'm starting to doubt myself: I have a mixed model which I estimate in Matlab with fitglm ...
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Problems with multicollinearity tests of no intercept models

i have an $\mathrm{AR}(1)$ model in R, $y_t \sim y_{t-1} + X_{1,t}+ \cdots+ X_{n,t}$ with no intercept and i want calculate some individual multicolinearity tests for each variable in the model. The ...
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Why does removing the constant term prevent the dummy variable trap?

I understand that if you have a dummy variable with $m$ categories that you should include $m-1$ categories in order to avoid perfect collinearity between regressors. However I don't understand why ...
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1answer
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Adding drift to an ARIMA(0,1,1) (0,1,1) model in R

I'm trying to add drift to my ARIMA(0,1,1)(0,1,1) model in R, however R is giving me the error message, Warning message: In Arima(insample, order = c(0, 1, 1), seasonal = c(0, 1, 1), include.drift = ...
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Why is the intercept different from the mean of Y when X=0?

I was hoping to find here a solution to some aspects of linear regression I had trouble understanding. Let's take an example of regression with the following variables: $y:\:$ depression (continuous)...
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Components of the intercept within regression and colon interaction (:)

I'm trying to figure out which elements are part of the intercept within a regression model characterized by colon interaction (:). This is the model: Y ~ A*B + A:B:C The independent variables are: <...
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Independent variable is correlated with intercept, creating singularities

I am building a logistic model with about 20 variables. I have used the following code: `fullmod = glm(cancer ~ B_SEX+BLINE_AGE_AT_BASELINE+B_BMI+B_chro+B_fdrc+B_hrt+B_LMET+ B_MET+B_FV+B_EDU+B_INC+...
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When forcing intercept to zero, how R-squared is changed? [duplicate]

I have some questions. In a linear model, I want to force intercept to zero. The program (I used JMP) does not provide R-squared when intercept becomes zero. So, I calculated R-squared by myself by ...
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Understanding the intercept in an unadjusted logistic regression

I have performed an unadjusted logistic regression using weights (obtained via genetic matching) as below. I am using the survey package to make working with the ...
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Intercept in a dynamic panel model

I have been taught that including fixed/random effects in a dynamic panel model yields inconsistent estimates when using OLS and hence motivates the usage of other estimation methods. However, does ...
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1answer
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Is the intercept fit differently for each regressor in Multiple Linear Regression?

is the intercept B0 in y = B0 + B1X1 + .... fit differently for every feature x1. Is it different for every feature coefficient or the same for all feature coefficients and why so?
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Interpreting estimates in a Poisson regression

I know similar questions have already been answered but in this particular case I need some additional help. I am working with this data set: https://github.com/proback/BeyondMLR/blob/master/data/...
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A queston regarding the meaning of the intercept in regression

Suppose we have a dataset where the indepedent variable $x$ is the work experience in years of an employee and $y$ is his salary in dollars. Such a dataset could consist of the following elements $$(...
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'Size' of intercept at linear regression

I have a question about this table. Why does the constant (intercept) change so dramatically from Model 1 to Model 2?
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Variance of intercept in multiple regression

I'm calculating a variance of coefficients in a multiple regression with three predictors. I have built matrices necessary for calculation of variance of independent variables, for example, for b1 I ...
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Logistic Regression with dummy variables? [duplicate]

I am working on a problem where response variable is binary and my features are dummy variables. I observed when I include intercept to model all the dummy variables' p-values are equal to 1. When I ...
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How do I interpret the regression output using Artificial Neural Network in R? [closed]

I'm working on a covid-19 dataset, and I'm interested in using the Artificial Neural Network (ANN) to measure the effect of some independent variables namely: confirmed cases, new cases, and total ...
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Weighted average, then adding an intercept term

I am working with a problem where we are calculating the weighted average of DNA modifications. Modified sites are placed into four different groups and the average weight is calculated from there. ...
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28 views

Why the OLS parameters of a linear regression are correlated? [duplicate]

I found that in a simple regression model y=β0+β1x+ε, the parameters β0 and β1 are negatively correlated. Is there a explanation for this correlation? I found this property here, but I would like to ...
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Mixed Effects Logit Intercept

When I run a simple model using the ordinal package from R. model1 <- clmm(A~ B*C+ (1| Subject), data = df, threshold = "flexible") summary(model1) I ...
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Logistic Regression with unbalanced data, intercept effect

In several papers I have read that when doing a logistic regression with unbalanced data, the entire effect of the imbalance is carried by the intercept. However, I cannot understand why this occurs, ...
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Should I give up the intercept when calculating general trend of time series?

I have a time series of customers. Say, every customer has 30 observations of how may items he purchased over a period of 30 months. I would like to add a feature indicating what the general trend of ...
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101 views

How to interpret insignificant intercept in logistic regression with orthogonal polynomial encoding

I have conducted a logistic regression. Model <- logistf(A ~ C, family ="binomial"(link = "logit"), data=Data) The dependent variable is ...
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Likelihood Ratio Test for Intercept in Simple Linear Regression?

I’m trying to construct a test statistic for the likelihood ratio test for the following hypotheses about the intercept of a simple linear regression: $$y_i = \alpha + x_i\beta$$ $$H_0: \alpha=0$$ $$...
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Why does level means coding only work for one (dummy) variable?

Consider the following reproducible example: ...
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Ability of neural network for regression to learn to predict input 0 where functional form doesn't hold for the data at input 0

Hopefully the title to this question wasn't too confusing. I have a feed-forward neural network with $tanh$ activation functions on each of the (four) hidden layers. I'm performing a one-dimensional ...
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2answers
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Alternative to R$^2$ in linear regression without intercept

It has been extensively described in this website the reason why one cannot properly calculate the $R^2$ - neither the Adjusted $R^2$ - in regression models fitted without an intercept. What is a good ...
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plot probabilities from mixed effect logistic regression with random intercept and fixed continuous and categorical covariate

I have created a mixed-effect logistic regression model with a random intercept, a ...
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Count Time Series Following Generalized Linear Models - Questionable Intercept

I am attempting to appropriately examine the relationship between a count time series variable (outcome) and a continuous, mean-centered time series variable (predictor). ...
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How to interpret the intercept of a multiple regression analysis after accounting for continuous and categorical variables

I'm attempting to run a regression analysis on a continuous outcome variable y, using a primary predictor variable that represents a treatment (...
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1answer
42 views

How to interpret an insignificant intercept in a model with significant coefficients?

Imagine a regression with one dependent variable (y) and two explanatory variables (x1 and x2). Both coefficients of x1 and x2 are significant. But the intercept is around 0.1 is not significant. How ...
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1answer
692 views

The definition of a constant term in a fixed effects model

Is it considered necessary to include a constant term in the definition of a panel data model which. among other regressors, includes a variable representing fixed effects?
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How do we explain together the results of paired t-tests and simple linear regression?

Description of my experiment: I have 2000 samples. For each sample I am measuring a metric Mi using four different methods A,B,C and D. These methods of measurement are completely independent. Ideally,...
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Does mean centering help with intercept estimation?

Suppose a model is built to model the sales of 20 beer brands for thousands of stores across the U.S. The response variable is sales and the independent variables include marketing activity and ...
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Effect of treatment in a model with random effects

I am working with Eyes (volume), each person has two eyes and I am using random effects to account for this in my model. (linear mixed effects model) The problem with nlme is that the output needs ...
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How if the coefficient constant Insignificant when Estimate ARMA?

I'm doing my thesis about estimate Volatility where I use ARMA for Mean Equation. when I'm trying to estimate the ARMA, I get Insignificant for coefficient constant but the AR and MA both are ...
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Extremely high intercept from Elastic Net

I am using the ElasticNet library from sklearn. I am using one predictor which takes values in the range [827.559, 827.5625]. When I fit the model using this ...
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Why are the intercept values of my QR non-monotonic?

I was led to believe that when conducting quantile regression you would expect intercept values to increase as you go up quantiles. However, I have run a quantile regression and the intercepts are as ...
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2answers
60 views

Back calculate intercept from multivariable COVID-19 logistic regression model

I am interested in creating a web tool to predict the absolute risk of in-hospital death from a published risk model of COVID-19 patients. Is it possible to estimate the intercept from the ...
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1answer
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I don't understand RidgeCV's fit_intercept, and how to use it for my data

Alright, I have an assignment that makes me calculate weights for a function with different terms. At first, I thought I might just leave the weight for the term $1$ out, and instead use the intercept....
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Is the random slope for a binary, categorical variable in a mixed model also reported in reference to one of the categories?

I'm wondering if I should be interpreting an estimated random slope for a binary categorical variable in the same way that I should be interpreting it if it were a fixed effect. That is, is it ...
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1answer
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Meta-analysis using mixed-effects model with moderators where response variable is effect size - is it appropriate to remove the y-intercept?

Greetings fellow statisticians, We're working on a meta-analysis looking at the effect of mindfulness based interventions [MBI] on self-compassion. We've computed Hedges' ...
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Calculate/understand the total sample mean of a binary dependent variable from the fixed effect estimates of a model?

was hoping you could help me to understand the model that I have just fit; in particular, I'm interested in the calculus of how the fixed estimates fit back together to make estimates for total sample ...

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