Questions tagged [intercept]

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

Filter by
Sorted by
Tagged with
8
votes
4answers
951 views

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 ...
2
votes
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 ...
2
votes
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 ...
3
votes
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 ...
1
vote
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 ...
0
votes
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 ...
4
votes
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 ...
23
votes
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 ...
1
vote
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 ...
1
vote
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?
1
vote
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 ...
7
votes
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 ...
3
votes
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 ...
2
votes
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 ...
7
votes
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 ...
6
votes
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 ...
3
votes
2answers
6k views
0
votes
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) ...
4
votes
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 ...
3
votes
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 ...
1
vote
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 ...
1
vote
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: ...
0
votes
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 ...
3
votes
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 $x=0$, $y$ ...
1
vote
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 ...
2
votes
1answer
523 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 $\...
8
votes
1answer
10k views

Understanding the intercept value in a multiple linear regression with categorical values

I'm failing to understand the value of the intercept value in a multiple linear regression with categorical values. Taking the "warpbreaks" data set as an example, when I do: ...
0
votes
0answers
118 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: ...
2
votes
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 ...
3
votes
1answer
1k 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 ...
2
votes
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 ...
0
votes
1answer
2k 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. <...
0
votes
1answer
185 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 ...
2
votes
1answer
452 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 ...
2
votes
0answers
433 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 ...
1
vote
0answers
38 views

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 ...
1
vote
0answers
163 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 ?
1
vote
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 ...
1
vote
1answer
88 views

Get overall tendency in the dependent variable, beyond the effect of the independent variable [closed]

Hypothetical data-set: There's a dependent binomial variable 'happiness', with $0 = unhappy$ and $1 = happy$. Then there's an independent categorical variable 'color' with the levels $blue, red, ...
0
votes
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 ...
3
votes
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 ...
0
votes
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 ...
3
votes
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 ...
2
votes
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: ...
2
votes
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 ...
1
vote
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 ...
0
votes
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 ...
5
votes
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 ...