Questions tagged [regression-coefficients]
The parameters of a regression model. Most commonly, the values by which the independent variables will be multiplied to get the predicted value of the dependent variable.
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Multiplying regression coefficients by a constant
Lots on this on CV already but most tend to be about multiplying predictors by constants before performing the regression.
I feel like I should know this already but I'm wondering if it's kosher to ...
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49 views
Correct way to standardize interaction term with one binary and one continuous variable?
Say that I want to run a regression of Y on 1) a binary main effect (B); 2) a continuous main effect (C); and 3) an interaction of 1 and 2 (BC): gen BC=B*C.
If I ...
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Rewriting coefficients of a regression
We are interested in the variable $\alpha_1$, in the equation
$
y_{iv1}=\alpha_0 + \alpha_1group_v+x_ia_x+ \gamma y_{iv0} + \epsilon_{iv1}
$
Where $y_{iv1}$ is a dummy variable, and $x_ia_x$ denotes ...
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How make untrasformation of the dependent variable?
I ran a regression with electricity price volatility as dependent variable and power production types as independent. I found the following article on how to calculate price volatility based on price ...
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2answers
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Ordinal Logical Regression in R using polr interpretation
I'm using the polr package to do a ordinal logistic regression on my data. We did a survey for university students asking them a bunch of questions. To analyse the results i want to do an ordinal ...
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26 views
How does the probability weight, called a pweight in Stata, work?
I am using inverse weights in a panel data analysis (fixed effects) in Stata, to see if my regression coefficients are the same after I reweight the analysis to better represent respondents most ...
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When I read papers, I've noticed the descriptive statistics don't match the results. Why is this? [closed]
I am an economics PhD student. I've noticed that when I read papers that are regressing the relationship between some predictor, like income, and some outcome, like obesity, that in the Table of ...
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How to interpret coefficients from weibull regression for variables of proportion?
I want to fit a weibull regression model. The variables are however given as proportions (i.e. they are in interval <0,1> ... is this compositional data if they do not sum up to 1?). An example ...
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When Normalize is true are the coefficients arising from LASSO normalized or in the original state?
From this question: Are LASSO coefficients raw or standardized?
I understand that when standardizing the data, the coefficients are returned to the original scale. Is this correct? Can I just plug in ...
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1answer
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Is Dropping Interaction Terms Reasonable if You Just want Partial Regression Weights
Without interaction terms in a model, coefficients of lower order terms represents partial regression weights (i.e. how much influence does one parameter have assuming all other parameters are held ...
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Identifying lead/lags using multivariate regression analysis
I have three time-series variables (x,y,z) measured in 3 replicates. x and z are the independent variables. y is the dependent variable. t is the time variable. All the three variables follow diel ...
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Bias of Ridge Estimates in Regression
For a given ridge parameter, ridge estimates minimize the sum of squared predictions subject to an inequality constraint.
Are the ridge estimates biased regardless of whether the aforementioned ...
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How to choose weights in neural network to fit implicaton?
I'm wondering what weights should be chosen in neural network to fit an implication i.e.
$x_1 \Rightarrow x_2$
First important observation is that this logical statement takes value $1$ when $(x_1 =1, ...
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0answers
27 views
effect size for meta-analysis when study data violate normality assumptions?
I'm looking for advice regarding what effect size to use for meta-analysis. Our question is whether the number of individuals in a group increases per-capita productivity of that group. Studies are ...
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Can I use t-statistic as feature importance in linear regression?
Here it is mentioned that $\frac{\hat \beta_j}{SE(\hat \beta_j)}$ can be used as feature importance. Is this correct and can I then compare my features?
Once I have a linear regression model, I would ...
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Nonsensical negative coefficent with multiple regression, without multicollinearity
I am a bit confused with a result that came up with a multiple regression. I have these data
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9972
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9732
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3994
7999
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3554
7138
321
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7642
330
5336
7765
334
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How to apply diagnostic model to a new patient
With some help I performed LASSO regression on boostrapped and multiple imputed datasets to build a diagnostic model that can distinguish disease A from disease B using a large number of predictor ...
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Comparing feature importance using coefficients in multi regression (,binary and continuous)
have a set of 25 features. I wish to choose the best features for my model. Originally i was looking at the correlation of features with respect to response, and only taking those which are highly ...
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How to interpret coefficients relative to one another (regression) continuous and binary
I have a mix of continuous and binary data. promotion here is binary so 1= yes 0=no. i standardize (mean 0 and s.d. 1 ) the continuous variables so they are same unit.
I get the following output:
is ...
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Where does this formula “New odd=(old Odd)*(Exponentiated coefficient)*Change in independent variable)” come from? Logistic Regression
I'm working in a project using the logistic Regression and was reading some books when I saw this formula in the book titled "Multivariate Data Analisys" by Joseph Hair and others. I don't ...
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How to interpret standardized coefficients in linear regression?
I have data with mixed units e.g $, days, temperature. I run a simple OLS and I get the coefficients. I am trying to predict no of sales.
I am interpreting the coefficient of 600 for temperature as a ...
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How do I interpret and analyze the estimates based on specified knots in restricted cubic spline?
First time asking here in CV. I'm trying to perform an adjusted linear regression with a 3-knot restricted cubic spline on R. The 3-knots are explicitly specified based on literature/discussions. A ...
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Determining if coefficent is significant
I am attempting to better understand regression tables but I am having some difficulty when trying to understand coefficients and $p$-values. On the table given below from R, I am trying to understand ...
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Ordinal regression model in R - multiple coefficients appearing for only one dependent variable. Can someone help me analyze?
I am running an ordinal logistic regression model in R (with an ordinal dependent variable). For my model I am also only including one primary independent variable (which is also ordinal). However, ...
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Standardized regression coefficient vs Pearson's correlation coefficient?
When we approximate a general model (not necessarily linear) by a linear regression model (with intercept), I wonder why we should favor the Standardized Regression Coefficient against the Pearson ...
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Regression coefficient greater than 1: is it possible?
I was reading an article about the polar motion of the Earth: https://www.researchgate.net/publication/241368199_The_Earth's_variable_Chandler_wobble. A regression is performed between observed and ...
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1answer
19 views
How do I figure out the specific coefficient of a dummy variable?
I have a linear regression model that aims to predict the quality of melon icecream (dependent variable) using the amount of sugar, melon powder, and vegetable oil.
The equation is as follows,
$$\text{...
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28 views
Confidence intervals with sums of transformed regression coefficients?
I have an index that is formulated as follows, for party $j$, group $s$, where $w$ indicates weight of party or group as share of population :
$$
\lambda =\sqrt{\sum^J\sum^S w_j w_s(\alpha_j+\beta_{js}...
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1answer
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Conway-Maxwell-Poisson (CMP) - Coefficient interpretation (Log/IRR)
I'm using the Conway-Maxwell-Poisson (CMP) distribution to model the amount of nouns in a clause (data is under-dispersed). I've run the model using glmmTMB (family= "compois") but I'm ...
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1answer
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Relationship of a negative coeffecient and positive odds ratio in an ordered logistic regression
I am wondering about the relationship and interpretation of the regression coefficient and odds ratio (OR) of an ordered logistic regression.
I know the OR will always be positive given the ...
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Modeling and comparing repeated measures regressions
We have the following data: Subjects rated emotionality of three events that differ in terms of expressions (i.e., repeated measures DV with three levels). Then, for each of these emotionality rates ...
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high standard error for probit model [duplicate]
I have created two probit models with a sample size of 10000 in both. However, when I print the summary of these models, the standard error seems extremely high, 6.29e. What could be the reason for ...
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Logistic regression model coefficients seem to disagree with data
I'm running a logistic regression in R. The data for the model comes from survey responses. The response variable is 'change in wellbeing' and the predictor variables are derived from several other ...
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1answer
26 views
How to convert linear regression dummy variable coefficient into a percentage change?
Given a model predicting a continuous variable with a dummy feature, how can the coefficient for the dummy variable be converted into a % change?
Example:
...
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Definition of a link function (brms or multilevel model -associated)
I have one question related to a link function defined in BRM.
According to the reference (Bürkner, Paul-Christian. "brms: An R package for Bayesian multilevel models using Stan." Journal of ...
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GEE Interpretation beta coefficient
I am running a Generalized Estimating Equation in STATA with link(log) corr(independent) family(gamma) and vce(robust).
The results of the command...
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Interpreting regression coefficients from cumulative probability model?
Cumulative probability model (CPM) has been proposed as an alternative for linear regression and quantile regression since it allows flexibly different kinds of response variables (Liu et al. 2017).
...
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Nested regression model
I had a dataset that consists of 20000 chess matches. In this dataset there are columns and variables- variables like the players’ ratings- that might have impact on a binary result, white player ...
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1answer
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Interpreting coefficient for regression with log dependent variable
Say I estimate
$$
\ln y_i = \alpha + \beta x_i
$$
I understand that my estimate , say it comes out, $\hat{\beta} = 0.01$ is approximately the percentage variation in $y_i$ since
$$
\ln y'_i - \ln y_i =...
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t-Test for testing non-zero Multiple linear regression coefficient
I am supposed to use a t-Test to test wether a regression-coefficient is zero or not, atleast it is what i understood from it(see down below for the literal translation).
The scenario is, that i have ...
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1answer
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Is it a good way to present updated model coefficients by refitting the model using both derivation and validation data?
I am conducting a research on the development and external validation of a prediction model. I have developed a regression-based prediction model using derivation set data and externally validated it ...
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Constraining regression coefficient to non-negative [duplicate]
I have a regression problem where I don't want the coefficients to be negative. Is setting negative coefficients of OLS to zero the same as constraining the coefficient to be non-zero and solving it ...
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Interpreting continuous explanatory variable in Poisson rate regression with offset term?
There are several excellent questions and answers on Poisson regression hosted by CrossValidated. I have consulted extensively with these, for example:
When to use an offset in a Poisson regression?
...
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Difference-in-Difference regression model for sensitivity analysis
I have 5-year sales information from a grocery store in Canada. I want to check whether an event that happened in 2017, affected the effect of the price of a product on its sales.
For example, imagine ...
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Can I interpret the signs of coefficients in a VAR model?
I estimated a VAR-model. I checked the time series for stationarity and after estimating the model the residuals and all is fine.
I know that it makes no sense to directly interpret the coefficients ...
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norm of ridge regression estimator
is there a characterization or an upper bound on the norm of the ridge regression estimator (coefficients)? As the Tikhonov regularization attempts to regularize these coefficients as part of the ...
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Standard practices for displaying thousands of regressions - what are they?
I thought this might be a fairly simple question, but I've failed gather adequate information on the subject.
So what are the standard pratices to display the results of thousands of regressions? I ...
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Testing equality of coefficients from two probit regressions
I need to test the equality of coefficients from two different regressions (PROBIT). So, I performed two times the same regression (containing multiple independent variables).
The only difference is ...
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positive and negative correlation after controlling variables [duplicate]
i used OLS before i've include control variables i found negative association between X and Y after including the Size i found positive association but the correlation is negative. why this happen ?
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1answer
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Find optimal weights for a regression model with some restrictions
I have created a hybrid recommendation system which contains 4 recommendation models.
In my case i am trying to predict the ratings of the products and after that recommend the high rated (predictions)...
