**984 regression-coefficients questions.**

I am running a simple ordinary least squares regression to understand the effect of parental attitude ($PA$) on Math scores of children.
$Math Score_{i}= \beta_{0} + PA_{i}\beta_{1} + Controls + \...

I am conducting an analysis in which I have 3 different groups and a set of 80 continuous variables that I think can discriminate between the 3 groups. I want to:
see if indeed I can discriminate the ...

I've found for my econometrics exams that if I forget the scalar notation, I can often save myself by remembering the matrix notation and working backwards. However, the following confused me.
Given ...

I have 2 variables with different information value . When i check the scorecard points the one with low Information Value has higher score range than the one with high Information Value. How is it ...

I'm doing a critique of an Economics paper, and I want to have my critique about omitted variable bias. I have found a variable that was not included that is both correlated with my regressor of ...

I am trying to apply glmnet's lasso to a set of features in which there are multiple categorical variables with multiple levels. My intention is to let lasso reduce ...

I have created two mixed regression models, one with raw unstandardized variables and the same model with standardized variables. When I convert the coefficients from the standardized variables I get ...

Possible duplicate of
(Why) do overfitted models tend to have large coefficients?
How does regularization reduce overfitting?
In the Coursera's machine learning course by Andrew Ng, I came across ...

I'm trying to run a multiple regression on a dataset in R.
The structure of the data that I want to use for the regression is as followed (only showing the variables I want to use):
...

Is it statistically correct to compare two models using their residuals? For example, I have two dose-response models, then am comparing their residuals and concluding they are not statistically ...

I have this problem, I want to run a Logit model, where its independent variables are all categorical (as factors), but when I run this code:
...

If we have one independent variable, then the given regression coefficient $\beta$ = Pearson's $r$. If we have multiple independent variables, how can calculate Pearson's $r$ for each variable if only ...

So I'm trying to build my own Wald test and likelihood ratio test code within a machine learning pipeline. I can get the final fitted logistic regression coefficients from liblinear. I'm coding in ...

Goal
Calculate covariance between effect sizes in a meta-analysis of relative stability, or the b1 slope coefficient of a regression of
$$
x_{T_{k_y}}-x_{C_{k_y}}
$$
on
$$
x_{T_{k_y}}+x_{C_{k_y}} \...

I am a beginner in statistics so I would like some help with literature.
I have a linear model $Y= b_0 + b_1 X^2$
Intercept $b_0$ is not significant (sig: 0,09 > 0,05) but $b_1$ is very significant.
I ...

Hi all, I am an undergraduate student who is currently doing an assignment. I am now facing a few problems which are:-
1) Age is usually a positive return to wage, but in my regression output, that's ...

I'm modeling conversions with a logistic regression. Each conversion can have up to 3 events (page visit, fill questionnaire, and call). I have 3 domains let's call them A,B, and C.
Rather than using ...

I want to check my understanding of random effects with regards to the reference group in a regression. Let's say I want to predict earnings for a population on the basis of individual ...

If I know the coefficient of dispersion and the median of a data set, is it possible for me to then calculate the percent of data points that are above the median?
For example, what if the median is ...

I have recorded a DV and IV of 20 participants. The IV is a repeated measure, and my goal is to see how variation in the IV can explain variations in the DV. More specifically, I want a beta ...

I have a problem where the data I am getting is a convolution of the original data with some function and I am trying to solve the following equation for $A$ $$ Y = AX $$
where $Y \in \mathbb{R}^{n\...

I have a bunch of control demographic variables (collectively termed D) and a dependent variable of interest (y). I also have quite a few independent variables of interest (xi).
Goal: I want to ...

I am trying to compare the model parameters among three multivariate multiple regressions. All three models incorporate date from the same 97 individuals and share the same 4 independent variables (...

Little background
I'm working on the interpretation of regression analysis but I get really confused about the meaning of r, r squared and residual standard deviation.
I know the definitions:
...

I am currently doing a research on stocks returns and have a qualitative statistic question.
I am basically testing whether two groups of variables do a better job in explaining returns: firm ...

(Tag recommendations welcome)
In the book Introduction to Statistical Learning chapter 3 exercise 5 we are given the question:
I've struggled to understand this question even after reading the ...

I just did a regression based on the gravity model where I try to identify the most important factors that determine the trade flows. In total I have 18 variables and 363 observations. In fact I would ...

I have the weekly time series of returns for both VIX and S&P 500.
For the VIX I'm looking at 1 week return period (e.g. this is a 5 day return series rolling weekly)
For the S&P 500, ...

I'm looking at effects of tree mortality (using "Biomass loss") on forest growth patterns. I incorporate loss into a mixed effects model like so (using lmer in R):
...

My question is regarding OLS regression and their residuals.
If we have a model:
$$Y = B_0 + X_1B_1 + X_2B_2 + X_3B_3 + e$$
Where Y = Independent variable, X_i = Dependent variabels, B_i are the ...

If we have the following general regression:
$$ln(Y)=\beta_0+\beta_1 X_1$$
Then it can be interpreted as an increase of 1 unit in $X_1$ will increase $Y$ by $100 \times \beta_1\%$.
But what if the ...

I would like to interprete the coefficients of a elastic net regression (i'm using function glmnet()$beta in R).
The coefficients of the elastic net regularized ...

suppose I have a regression model
$$y_i = f(x_i,e_i;\beta) $$
where $y_i,x_i$ are observed data and $e_i$ are error. $\beta$ is parameter of interest. Assuming that $f$ is smooth, yet not invertable. ...

Above is a screenshot from Introduction to Linear Regression by Douglas C. We usually assume that in linear regression model $y$ has a linear relationship with the parameter, but why here the author ...

What is the proper way to calculate bootstrap regression coefficients CI after MI? I mean is using a pooled bootstrap SE over imputed data sets with a pooled estimate of bootstrap regression ...

I have multivariate (100 variables) time series, and I have obtained regression coefficients using statsmodels' vector autoregressive (VAR) model. Also, I have computed cross-correlation manually ...

An AR(p) process is defined as the regression of a variable against its p lags-
$Y_t=c+\sum_{i=1}^p\phi_iY_{t-i}+\epsilon_t$.
Persistence in an AR process can be defined as a measure of how much the ...

Goal: Meta-analysis of a regression parameter.
My effect size is the slope coefficient of a line, I also have the standard error of this slope. For a given treatment within a given study, the slope ...

I have two predictors in a binary logistic regression model: One binary and one continuous. My primary goal is to compare the coefficients of the two predictors within the same model.
I have come ...

As the titles states, I would like to compare two coefficients in my multiple regression model but I'm not quite sure how.
...

In regularized regression, we have the following loss function:
$\sum_i(\beta x_i - y_i)^2 + \lambda L(\beta)$
If we compute the magnitude of coefficients at varying $L(\beta)$, we build a ...

this is my first post!
I know the sampling distribution of the sample mean is $N(\mu,\frac{\sigma^2}{n})$.
I also know that the sampling distribution of $\hat \beta_1$ is $N(\beta_1, \frac{\sigma^2}{...

Assume we have a categorical variable (one-hot encoded) with three or more categories. {race1, race2, ..., race-n}To avoid the dummy variable trap, assume we ...

I am trying to replicate what the function dfbetas() does in R.dfbeta() is not an issue... Here is a set of vectors:
...

I am running a logistic regression with 7 independent variables. One of these variables is income. If I don't log-transform income and run the regression, it results in a positive coefficient, however,...

Say I'm using multiple logistic regression to help caterers in a large city predict the probability invited adults will come to a wedding. Say I have a proprietary dataset of likely relevant predictor ...

I've read a few Q&As about this, but am still not sure I understand, why the coefficients from glmnet and caret models based on the same sample and the same hyper-parameters are slightly different....

I have question with some results I'm working with.
Model: avg_score = Î²0 + Î²1(nonwhite) + Î²2(hhold_avginc) + u
For some background, I'm seeing if average income and race influence state ...

Suppose, I have performed multiple regression analysis on the following data set where X1 and X2 are independent variables and Y is the dependent variable.
And achieved the following multiple ...

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