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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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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1answer
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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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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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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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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 fit the intercept

I'm practising using R and I'd like to do this task: So I fitted the model: ...
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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
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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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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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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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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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Test if intercepts in ancova model are significantly different in R

I ran a model explaining the weight of some plant as a function of time and trying to incorporate the treatment effect. weight ~time + treatment The model looks ...
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Why does using the pnorm function on the intercept estimate of a binomial regression recover the mean of the dependent variable?

I've fit a series of mixed effect models with the lme4 package in r on a set of binomial outcome variables (1: the event happened, 2: it did not). The fixed effect specification is intercept only, and ...
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2answers
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Linear regression when a non-zero intercept is theoretically implausible

How should I think of a linear model with a positive intercept when, theoretically, the intercept has to be zero? Think of the following example: we are modeling how many birds does a feral cat hunt ...
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How to obtain a distribution of the unobserved time-invariant fixed effects in a fixed-effects regression?

I am running a fixed-effects unbalanced panel regression using the plm package: ...
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1answer
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When using Lasso and calling coefficients (.coef_) which is the coefficient of the constant? [closed]

By calling .coef on the Lasso model built, there are only numbers corresponding to the coefficients. These coefficients are supposed to match, say, the columns of the pandas dataframe given as input. ...
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1answer
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Computation of the intercept in logistic regression model

I'm trying to understand the way the odds of the reference groups are computed. Let's consider an example from this paper. Data can be summarised in the table: The reference group is Older and New. ...
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Issue about confidence interval on OLS intercept

Let us assume this simple linear model: $Y|X=\beta_0+\beta_1X+\epsilon $ where $X \sim N(\mu,\sigma^2)$ and $\epsilon \sim N(0,\sigma_{\epsilon}^2)$ Suppose also that $X$ and $\epsilon$ have all ...
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1answer
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Can an random walk ARIMA model have a nonzero constant term?

From what I'm reading it seems like a nonstationary ARIMA model can have a nonzero constant term. I'm not understanding how this can happen. Suppose we have an AR(1) model where $\phi_1=1$. If p is ...
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356 views

Why does the constant term have a P value in statistical programs?

I notice when running regressions in programs such as Stata that the constant term has a p value. Why and what does this p value represent?
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nonsensical intercepts for regression models

Let’s say that I have performances in 9 sports as predictor variables and the total points of those sports as the DV. So I am making three regression models(non-nested) with three predictors each (...
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When to include intercept in logistic regression? [duplicate]

I see on this page following statement regarding a function for logistic regression: An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant. ...
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1answer
61 views

Is the intercept term in a linear regression model the intercept term?

Currently working through some notes on linear regression and they say the following: In the linear model: $$Y=\alpha+\beta x$$ the intercept term is the mean value of the response." However, I've ...
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1answer
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glm with two interactive factors

I've run a glm (gaussian family) with a*b as independent variables. At first, I run two separate models (like glm(y~a) and ...
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1answer
140 views

multiple regression coefficients - Standard error of intercept

I am implementing an R-type summary() function in python with the restriction to exclude use of scientific libraries. (assignment) I found this https://www.nd.edu/~rwilliam/stats1/x91.pdf material ...
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Comparing two populations with high dropout, best tests and interpretation of intercept significance

I am analyzing some single cell data and was trying to pick the best test. I first tried logits and found that all three genes of interest were significantly across populations. I also found that the ...
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Interaction term btw. binary and continuous variable - dropping the intercept?

I run a linear regression with many dummy variables (in total 10). Thus to avoid the dummy variable trap, I dropped the intercept and included all dummy variables. Now I'd like to have a look at the <...

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