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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31 views

Estimating intercept term in simulation study

I am working on a simulation study in which I have generated data to mimic a scenario with treated and untreated members of a population along with a number of baseline covariates. I am using those ...
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120 views

How to Interprete Constant Intercept in Log Models?

Even though this seems like an especially simple question, I am not 100% sure about what the constant in a log-log model, log-level, or level-log model means. For example, in log(weight) = 8.29 - 0....
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Unusually high intercept for one of the data sets

I’m running a regression to test the stock market in Sweden. In essence, I have made a categorization of two types of buys and two types of Sells. Then I have divided the sample containing all trades ...
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1answer
360 views

Intercept is in the error term (dropping?)

The model is $$y_{it} = \delta_0d2_t + \delta_1 crm_{it} + (\alpha_i+u_{it})$$ Here the intercept is placed in the error term. Therefore if $\alpha_i$ is correlated with an independent variable would ...
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Should an ARMA model always include a constant term?

Should an ARMA model always include a constant term? And if not, when is it appropriate to drop the constant?
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1answer
305 views

Modeling intercept in ridge regression in a leave-one-out cross-validation test

In a leave-one-out cross validation test, I am calling a ridge regression model for the training samples, and predict the response for the test samples. And my metric for the accuracy is whether the ...
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1answer
5k views

The difference between with or without intercept model in logistic regression

I like to understand the difference between with or without intercept model in logistic regression Is there any difference between them except that with the intercept the coefficients regard the log(...
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2answers
5k views

Convert 'intercept/drift' term to 'constant' term in arima/auto.arima functions, in case of higher orders - R

suppose for a time series, the auto.arima output is: ...
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1answer
853 views

High t-stat for intercept?

I have built a multivariate distributed lag model (two regressors) which produces p-values that are significant just below the one percent level. However, my constant has a t-stat of like 26. Should I ...
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1answer
531 views

Is Bias term required for RBF with Gaussian kernel?

For standard logistic regression, we add a bias term (1) in the features. Is it required when RBF is used with Gaussian Kernel?
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1k views

a regression through the origin

Why do a pair of variables with no significant correlation and no significant regression intercept and slope, have a highly significant regression with high adjusted $R^2$ when the regression is ...
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1answer
148 views

Lmer() R function does not return random but fixed intercepts

I'm using the lmer() function to run a random (intercept) mixed effect model as follow: memC1 <- lmer(dev ~ tempW + tempB + sex + ls + (1|id), data = finalInfo) ...
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523 views

Simulate event rate in logistic regression - Finding the intercept

I want to simulate a logistic regression (using a set of continous and binary confounders with known odds ratios) with a specified probability outcome (e.g. event rate = 0.2). This is actually a ...
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1answer
1k views

What to do with an insignificant intercept in a GARCH model? [duplicate]

I have fitted model to my data and estimated parameters both using R and Matlab. Here are my results: ...
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30 views

Bias imbalance in learning algorithm

I have the following model: $$ q^\star = argmin\{\sum_{i=1}^n 𝓁(y_i q^T x_i)+ λ\|q\|^2_2\} $$ where each $x_i$ is a sample of a $p$ dimensional feature vector. As it is common to do, I have encoded ...
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1answer
1k views

Fitting simple linear regression with no intercept

Say we fit a simple linear regression without the intercept term. I know this is inadvisable for the most part, unless there are some situations where it is reasonable to assume that when $x=0$, $y$ ...
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Example for Time Series with Drift and one with Trend

after going through a few posts here on CrossValidated, StackOverflow and Google, I am still puzzled how I can detect whether a time series has a "drift" and how it differs from trend. Here are four ...
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1answer
2k views

VAR models in R: Constant, Trend, Constant+Trend, None

I currently apply a VAR models with the vars package in R. There are the options to select "both", "none", ...
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1answer
474 views

ANOVA for intercept term in Simple Linear Regression

In an ANOVA framework, is it possible to test the hypothesis that $\beta_0 = 0$ given a "full" model of $\hat y_i = \beta_0 + \beta_1 x_i$? Or does the "full" model not contain the "small" model $\...
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Regression Through Origin [duplicate]

I was reading about (simple) linear regression through origin and I have the following questions: What are the standard assumptions of such a regression model? I am asking this of the true model not ...
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114 views

Force a rma.mv fitted model to have the intercept value equal to 1.0 in R

I'm new in this blog and my knowledge in R is very weak. However, I was trying to set the intercept at 1 in the following rma.mv ('metafor' package) function: ...
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475 views

Why is PCA “a regressional model without intercept”?

As stated in How does centering the data get rid of the intercept in regression and PCA?, PCA is a regressional model without intercept. Thus, principal components inevitably come through the ...
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1answer
3k views

Ways of comparing linear regression interepts and slopes?

I'm a little bit confused about this, so any help would be appreciated! Let's say I have a repeated-measures design in which participants take part in a task where they have to rate the ...
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1answer
946 views

How to interpret lme() outcome with a mix of cat/non cat variables

I apologize in advance for my question that can seem redundant, but I am still struggling to interpret the outcome of my lme model, as the other posts mainly deal with several categorical variables ...
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1answer
1k views

Interpreting odds ratios for logistic regression with intercept removed

So I ran a binomial glm to look at the effect of minimum temperature (continuous data) and moon phase (categorical data with 3 categories) on lion incidents. I removed the intercept to look at all 3 ...
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1answer
1k views

Interpretation regression intercept when only numerical predictors are standardized

I have a mixed-effects model in which the dependent variable is inverted (1/y) and the numerical predictors are standardized, but the binary variables are not. <...
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1answer
180 views

Interpretation of glm and HSD.test output

I've posted this on stack overflow a few days ago, but since nobody replied, I assume that it was the wrong forum for this kind of issue. And probably it's way to basic... but as I seriously need some ...
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1answer
449 views

Is there anything wrong with treating neural network bias as a node?

I've seen a lot of examples of neural networks where when they introduce the bias, they treat it as a node that always outputs 1, and then the nodes each have an individual weight for it, instead of ...
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When nature passes through (0;0): what are the consequences for a linear mixed model?

I'm analyzing the data for my master thesis; it's about photosynthetic efficiency for 10 different genotypes of a certain plant species (genotype=Accession in my dataset). For each Accession, I've ...
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156 views

GLM - compare the estimated model with the model with intercept-only

I have this glm output. How do the test to compare the estimated model with the model with intercept-only ? How can I comment on the result ?
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2answers
1k views

Analytical solution of a simple regression with fixed intercept

I would like to know how to find out the analytical solution of a simple linear regression with fixed intercept = 0: $$ s = e^{-ht}$$ $$ y = -ln(s) = h\cdot t$$ Here ist the background: I have ...
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2answers
6k views

Removing intercept from GLM for multiple factorial predictors only works for first factor in model

I am running a binomial logistic regression with a logit link function in R. My response is factorial [0/1] and I have two multilevel factorial predictors - let's call them $a$ and $b$ where $a$ has 4 ...
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1answer
238 views

How is mean calculated in ARIMA models?

I am currently working with ARIMA models and I am a little confused about the way they are formulated. I found Rob J. Hyndman's blog post "Constants and ARIMA models in R" explaining it. But still, I'...
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1answer
407 views

Can I leave intercept out in OLS? [duplicate]

I have a model similar to the following: y = a + b + c + d + e; a,b, and ...
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1answer
958 views

Deriving the intercept term in a linearly separable and soft-margin SVM

I have read Andrew Ng lecture notes on Support Vector Machines as well as the notes from MIT OpenCourseWare and I have a few doubts concerning the derivation of the intercept value: First, there is a ...
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413 views

Should the intercept be included when you check the condition index?

Many sources state that a condition index >30 constitutes a multicollinearity problem. When I've tried to implement this check in practice, I've realized that the condition index (and VIFs) change ...
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1answer
7k views

How to treat categorical predictors in LASSO

I am running a LASSO that has some categorical variable predictors and some continuous ones. I have a question about the categorical variables. The first step I understand is to break each of them ...
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1answer
2k views

Standardized LASSO in R still has intercept

I understand the need to standardize variables when performing LASSO in R (I'm specifically using cv.glmnet, and setting ...
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186 views

Potential multicollinearity problem between a dummy and the constant term

In an OLS regression, I need to use a variable that is equal to 0 for all observations (1636) except 3 of them. I am afraid that this may generate a multicollinearity problem with the constant of the ...
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2answers
7k views

Role of the bias term in regression [closed]

I was trying to understand the role of the bias term in linear regression which is given by, y=w^T. phi(x)+b From what I understand it allows for any fixed ...
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1answer
2k views

Should I keep a non-significant intercept in a GARCH model?

I've estimated an ARMA-GARCH model. All the parameters except the intercept ($\omega$) in the GARCH part are significant. So I tried to estimate the model again imposing that $\omega = 0$. I compared ...
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1answer
2k views

How to force exponential regression to go through an intercept? [closed]

Excel has a handy function that lets you set the y-intercept of an exponential regression model: How can the same effect be achieved using R? I'm using the following code: ...
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0answers
180 views

Is the intercept estimation affected by multicollinearity?

Suppose I am running a regression $$x_t = \alpha + b_1y_{1t} + \dots + b_m y_{mt} + \varepsilon_t$$ where the $y_{i}$ are potentially linearly correlated (Some have an IVF bigger than 4; generally ...
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1answer
548 views

High chi-square of the intercept in logistic regression

I run logistic regression models where the event rate is generally very low. In my models I get a large intercept term. What bothers me is the exceptionally high chi-square of the intercept term. How ...
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1answer
128 views

Two interpretations of significant negative intercept: which is correct?

I applied a linear mixed model on binomial data. In short, I have 2 binomial independent variables: Prime (DO or PO) and Language (English L1 and English L2), and my dependent variable is DO use (DO ...
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1answer
4k views

How to know where to put bias terms in neural nets? [duplicate]

I've read different places that talk about bias terms in neural nets like this Importance of the bias node in neural networks But I still have trouble understanding what it is used for and how it ...
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1answer
5k views

How is the standard error of a slope calculated when the intercept term is omitted?

Let's say we have data that looks like this: ...
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3answers
49k views

Why are bias nodes used in neural networks?

Why are bias nodes used in neural networks? How many you should use? In which layers you should use them: all hidden layers and the output layer?