# 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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### Linear regression with unusual error terms

Suppose that $Y = a + b K + X$ with $0 < X$ and $0 < K$ where $X, Y$ and $K$ are random variables. What are then the expectations of the intercept and slope in the case of a linear regression of ...
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### 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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### Chisq test for significance of intercept in R

I have a logistic mixed-effects model with both fixed and random effects. Imagine something like: ...
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### How to interptet Jensen Alpha statistical significance?

When you regress portfolio excess returns against relative benchmark excess return you get a model in which the beta (slope) could be interpreted as the one you get from the CAPM, that is systemic ...
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### Multiple Regression, R output how to interpret the intercept

In the example linear regression below, how do I interpret the (Intercept) with this R output? A) Does the (Intercept) line represent pop1? B) Does the Estimate column indicate the slope or the ...
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### t test for intercept?

the null hypothesis for slope is usually H0: slope is zero,but what's thenull hypothesis for intercept? is it H0:intercept is zero as well? From the picture we can see that the p value for intercept ...
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### Ways of comparing linear regression intercepts 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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### Logistic Regression, ''Intercept'' instead of a Category [duplicate]

I am currently trying to analyse the effect of an ilness (0= no infection, 1= infection) on 9 different Genotypes in plants. My Dataframe consist of 2 colums Genotyp and Infection. I have 459 rows for ...
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### Understanding fixef() output of fixed effects coefficients

I have a panel data set with several ID's and each has a certain number of year observations. When I fit a fixed effects ...
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### When you are running an analysis with only an intercept, does it make sense to include random subject and item intercepts?

Lets image a study where people get two shapes and are told to pick one. They each get 40 trials, each with a different shape/colour. There are 40 different pairs of shapes. There are 100 participants....
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### 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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### Is computing the average of a ratio the correct approach, and how to do it with nested data?

In general, is computing the average of a ratio appropriate? And secondly, is the nested model below appropriate for doing this? Here is a data set created from the Iris data that resembles my ...
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### How to decide if I should fit SARIMAx with or without intercept

I am trying to find the best SARIMAX model for my data, I am using auto_arima to find the order(ARMA and Seasonal) of the model. How do I decide whether or not I ...
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### Compare intercept of logistics (mixed-effects) model to value other than 0

Is it possible to compare the intercept of a logistic mixed-effects model to a value other than 0? Specifically, I have two choice alternatives and try to predict choices. My intercept would then tell ...
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### If I consider the fixed factor as a random slope, the p-value changes from p<0,05 to p>0,05

I'm having a hard time trying to understand the differences between these two models and why the first one shows correlation (p-value < 0,05) but the other one doesn´t (p-value > 0,05). I would ...
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### How to calculate the intercept (b) of a gaussian kernel support vector regression

I know the dual problem of an SVR problem is given as: where \alpha_i and \alpha^*_i are dual variables and they are also decision variables in the above problem. \epsilon specifies the epsilon-tube ...
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### Consistency of OLS when no intercept

Suppose I have a model $y_i = \beta_0 + \beta_1 x_i + e_i$ but instead I estimate $y_i = \beta_1 x_i + u_i$ using OLS. That is, I ignore the intercept. Working out the algebra, based on this post, we ...
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### What is the difference (if any) when talking about bias from a ML point of view and from a DL point of view

I'm reading around Machine Learning and Deep Learning and can't seem to understand the difference or similarity between bias in both. If we look at the simplest linear regression formula y = b0 + b1x1 ...
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### Is there a way to use the covariance matrix to find coefficients for multiple regression WITHOUT intercept?

Given: $$y=\alpha + \beta x$$ The problem on how to get regression coefficients $\alpha, \beta_0, \beta_1,...,\beta_n$ from the covariance matrix is solved here: Is there a way to use the covariance ...
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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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### How to correctly use I-Splines for monotone non-decreasing/ increasing regression?

I have the following data to which I want to fit a monotone non-decreasing spline. ...
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### Different values for intercept estimate in linear regression

I'm reading about linear regression from two sources. In here: https://online.stat.psu.edu/stat415/lesson/7/7.3 the estimate for the intercept is just $\bar y$. However over here: https://www....
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### Why is the intercept different in an AR(1) model compared to a lagged endogenous variable model? [closed]

In other words, why is that when estimating in EViews y = c ar(1) yields a different coefficient for c when compared to ...
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### Collinearity between the intercept and (logged) continuous variable: centering to fix it?

I have found a lot of information when there is collinearity between intercept and dummy variables. However, my collinearity diagnostics is showing a condition index > 30 (36) and it indicates ...
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### Simple linear regression without intercept - Expected Value and Variance Estimator (Slope) [duplicate]

I'm solving an exercise while studying for exam, I have been asked to find the estimator of simple linear model without intercept estimator, its expected value and variance. I got for the Estimator B1 ...
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### Mixed model fails to converge - do I delete the random intercept or the random slope, and what does the variance of the random effects say?

I'm in the process of building my mixed models, and unfortunately I encountered a problem when creating the random effects structure. I have two random effects: ResponseId (i.e., participant number) ...
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### intercept in manually weighed regression

Why does manually weighting a regression require the intercept term to be dropped? Consider a model $$y=b_0 + b_1x + \epsilon,$$ a simple linear regression. In classically weighted regression ...
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### Re-calibrating Intercept on logistic regression models for unbalance data

I have data-set that I’m modelling using logistic regression as land.cover~H1+H2+H3+H4+H6+H8+H14. My response and categorical variables are binary. However the number of 0 and 1 in my response ...
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### Why does removing the constant term prevent the dummy variable trap? [duplicate]

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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### Reason for not shrinking the bias (intercept) term in regression

For a linear model $y=\beta_0+x\beta+\varepsilon$, the shrinkage term is always $P(\beta)$. What is the reason that we do not shrink the bias (intercept) term $\beta_0$? Should we shrink the bias ...
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### Interpreting constant in regression with an interaction term

In a regression model with a single categorical exposure and no interaction term of the form: $y = \beta_0+\beta_1x_1$ the $\beta_0$ can be interpreted as the result in the reference group. So for a ...
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### Confidence interval x-intercept

The x-intercept of a linear regression is -y.intercept/slope. If I wanted to obtain a confidence interval (CI) for a x-intercept of a linear regression, coud it be wrong to find the CI of y-intercept ...
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### In R there is a problem of intercept and without intercept ,Pearson's Correlation does not follow! Why? (see the bolded resuls)

Xvec <- rnorm(200) Yvec <- 2.6*Xvec + rnorm(200) lmodxy <- lm(Xvec ~ Yvec) lmodyx <- lm(Yvec ~ Xvec) summary(lmodxy) Output ...
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