# 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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### Does the intercept in a logistic regression capture the unobserved effects?

Theoretically, does the intercept term in a logistic regression model capture all unobserved effects? In other words, in a logistic regression model with a perfect fit (i.e. all relevant variables ...
0answers
133 views

### Deming regression implementation: force intercept to 0

I implemented Deming Regression in a known programming language, using the algorithm from here: https://en.wikipedia.org/wiki/Deming_regression However, the algo does not specify what to do in case we ...
2answers
534 views

### How to find and correct y-axis offset in time series?

I have usage data of many machines like this: In this machine, the usage grows linear with the time. However, in early 2016, someone or something messed with the total usage in hours and the next ...
1answer
742 views

### Does R glmnet regularize on intercept?

Both this post and original paper, suggest R glmnet does not regularize the intercept. But why I am observing the intercept ...
0answers
745 views

### Negative and not significant intercept in multilevel model

the objective of my work is to estimate whether policies can increase men's time investment in housework. In order to estimate whether the regulation of normal weekly working hours affects men's time ...
1answer
273 views

### Do we include the intercept term when predicting in simple linear regression?

In "Introduction to statistical learning wit R", there's a simple linear regression fit on a 'TV' feature where the target class is the 'Sales'. Beta 0 is 7.03 and Beta 1 is 0.0475 Now we want to ...
1answer
120 views

### Detect a change in linear regression model's error

At time T0: A linear model is deployed to predict outcome Y as a function of X with equation Y = m1*x + c1 At time T1: The underlying process drifts to a new mean, resulting in consistently positive ...
3answers
16k views

### Importance of the bias node in neural networks

I'm curious to know how important the bias node is for the effectiveness of modern neural networks. I can easily understand that it can be important in a shallow network with only a few input ...
0answers
35 views

### 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 ...
0answers
224 views

### 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?
1answer
339 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 ...
2answers
19k views

### What exactly is the standard error of the intercept in multiple regression analysis?

I understand that in multiple regression analysis, for each independent variable, you would graph dependent variable vs independent variable and you would make a line of best fit and calculate the ...
0answers
326 views

### 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 ...
0answers
531 views

### Logical reasons for choosing regression through the origin [duplicate]

Is it reasonable to choose a regression model with a value of 0 for the intercept when this makes logical sense? For example, I am trying to model a physical ...
3answers
3k views

### What are the uses and pitfalls of regression through the origin? [duplicate]

Spuriously high R-squared is one of the pitfalls of regression through the origin (i.e. zero-intercept models). If the predictors do not contain zeroes, then is it an extrapolation? What are the uses ...
1answer
3k views

### Confused about 0 intercept in logistic regression in R

I'm exploring the effects of removing the intercept in a logistic regression model. Assume a model: $$logit(Y = 1) = \beta_1 x + \beta_2z + 0$$ with $x$ and $z$ being categorical variables with 2 ...
2answers
6k 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: ...
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) ...
0answers
543 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 ...
1answer
1k views

### Conjoint Analysis - Incorporating individual-specific intercept

We are new here, and have recently gotten a question that we have very much been struggling to answer. It is concerning a question regarding a conjoint analysis in which we have to incorporate an ...
1answer
1k 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 ...
1answer
2k 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: ...
0answers
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 ...
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 $x=0$, $y$ ...
0answers
611 views

### 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 ...
1answer
523 views

1answer
436 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 ...
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 ...
0answers
188 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 ...
3answers
3k views

### How does the inclusion of an intercept change the variability of the residual?

I want to use the variability of the residual as a measure M and then test whether M is higher or lower after some event. However, I estimate separate regression before and after the event to obtain ...
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: ...
0answers
194 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 ...
1answer
575 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 ...
1answer
130 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 ...
1answer
5k 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 ...
1answer
6k 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: ...