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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19 views

Multiple regression when the dependant variable is unmeasured or hidden

Say I was measuring the individual performance of each of a group of athletes every week. I measure things like running speed, jumping height, grip strength etc. I want to use these scores with ...
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Interpret Coefficeint as change in Percentage-point or Percentage

Having carried out the regression below, I'm struggling to determine what the correct interpretation of the predictor variable would be. Given that the dependent variable is binary, where 1=...
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What is a reason that in Lasso Regression we can force all coefficients positive & intercept =0?

I have a regression problem where I need all coefficents to be positive and the intercept to be zero. I can do this in sklearn but i don't understand how the algoritm can force this conditions through ...
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How to interpret coefficients of parametric terms in comp.risk?

I am trying to fit a flexible competing risks semiparametric regression model with the timereg package. My primary goal is to estimate the effect of Z on the cumulative incidence of the event of ...
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Interpreting the Odds Ratio of a logistic regression model

I'm currently working on building a logistic regression model with the aim of predicting whether a given stock index will go up or down the following day. The table below shows the 3 models I've ran ...
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Using the autohotencoder in PySpark for a linear regression but no reference category

I created dummy variables using the autohotencoder and as I have learned dummy variables you also need to have a reference category. However I have 7 dummy variables for the weekdays for example, so I ...
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Regression coefficient problem

The question asks that when the case is $X_1 = 1$ (when I am an asian instead of other ethnicity, a dummy variable), then what is the value of $Y$? As the $b_1$ has a P-value much larger than $0.05$, ...
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Insignificant coefficient in a regression

To simplify the question, for example, the interception, which is Beta 0, is +500 and the predictor X1 being 1, Beta1 being negative 100 and other predictors Xi are all 0. i.e. Y = 500 -100 X1. The ...
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fitting suitable regression model to identify predictors from contingency table

so a study was conducted for some game and the probability of success of the game was noted in a contingency table. I have a 5x5 contingency table which is age group by task difficulty(split into ...
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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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Significance of fixed effect coefficients in multinomial logistic regression

I am trying to do a multinomial logit regression, and I understand that the fixed effects coefficients are a bit difficult to interpret and that they can in some cases be 0 or negative but actually ...
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Slope estimator for the regression line through the origin

For a regression line through the origin with the equation: $$ \tilde{y}=\tilde{\beta_1}x $$ How did we use OLS to get the below equation? I know it is by minimising the SSR but I can't seem to work ...
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main effect significant while interaction insignificant in moderation analysis?

Please help with the following output. I have two IVs Example: (happiness IV1) (genderIV2) say on performance (Dv). question 1- I ran simple regression for happiness and performance as well as gender ...
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Impact of individual features under multi-collinearity

Assume the following scenario: I have four features: $x_1$, $x_2$, $x_3$, and $x_4$ There are non-negligible multi-collinearity among the features. I want to predict $y$ (response variable) with ...
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Removing multi-collinearity with PCA for regression analysis

I'm interested in studying the impact or importance of each feature on the response variable. I'm thinking running multiple linear regression with multiple features, and running regression analysis ...
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R quadprog for coefficient constraints

I have the model that I need to estimate, Q = B0 + B1*Q1 + B2*Q2 + B3*Q3 + B4*Q4 + B5*Q5 with the coefficients constrained to: B2 * B5 - B3 * B4 = 0; I believe I can use the quadprog package to ...
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simulate data for multiple regression based on standardized coefficients and covariance among predictors

I want to simulate data for multiple regression based on standardized coefficients (denoted $\beta^{'}$) and covariance structure among predictors. My problem is that I don't know how to determine the ...
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SpatioTemporal regression

I have a data-set containing rain value for 6 stations and station coordinates (lat,lon). I used lm function taking lat,lon,day, their interaction and rain as below: ...
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Identifying most significant variables in multiple regression

Imagine that the total cost for 100 patients undergoing the same procedure in a hospital, is further broken down into 10 cost categories (such as the surgery fees, room charges, consumables cost etc). ...
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Dealing with oversized effects in Linear Regression

I'm learning about GLMs and interpreting regression coefficients and so I'm experimenting with simulated data and pymc3. I've synthesised a dataset where X is an array of 5 normally distributed ...
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Interpretation of higher coefficient for group with smaller mean

I am running a fixed effects poisson model with robust standard errors in STATA (xtpqml). The model I run it on has my count data as dependent variable and then as my independent variable I have a ...
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Interpreting Logistic Regression Categorical Coefficients

So I have this question: If we fit a logistic regression with categorical predictor X with categories A, B and C, and have the estimated coefficients β0=−2.5 and βB=0.5 and βA=−0.2. (a) Interprete ...
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Converting coefficient of slope to autoregressive factor

I realize this is very fundamental. I apologize. Is there any way to convert the coefficients from a linear model into the decay factor if i want to express it as an autoregressive model? For a ...
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Interpretation of a quadratic term on a log transformed target variable

I've done some searching and found several posts related to this, e.g.: In linear regression, when is it appropriate to use the log of an independent variable instead of the actual values? Suppose I ...
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Interaction term and main effect multicollinearity [duplicate]

If I have the predictors $X$, $Y$, and $XY$ to fit a linear regression model. Won't I be increasing the standard error of the regression coefficients? This is because $XY$ is collinear with $X$ and $...
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In Simple Linear Regression $\hat \beta_1$ and $\bar Y$ are independent [duplicate]

I want to show that, in simple linear regression $\hat\beta_1 $ and $\bar Y$ are independent. My attempt: I have calculated the $\mathcal Cov(\hat \beta_1,\bar Y)$ and it turns out to be $0$.I also ...
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How to perform a multiple non linear regression without knowing the functions for each variable and the constraints for their coeffcients?

I have a data for number of cars and its causal variables are identified as GDP, population, urban fraction and fuel price but they have non-linear positive correlation but I don't know what that is. ...
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compare coefficients from different regression

I estimate the following models using the Hausman-Taylor estimator: $$y_{i,t} = a_{0} + B_1 controls_{i,t} + \beta_1x_{i,t=2000} + B_2 Year_t + B_3 x_{i,t=2000}*Year_t + e_{i,t}, (1) $$ $$y_{i,t} = ...
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Spatial Lag or spatial Error Model? Deciding by using the Lagrange multiplier diagnostics

Honestly, my knowledge of geostatistics is limited. My assumptions are as follows: If I want to choose between a Spatial Lag Model (SLM) and a Spatial Error Model (SEM), I can use the Lagrange ...
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Capturing effects / Controlling for variables [duplicate]

I understand the idea behind regressions and know how to interpret them, however, when I hear the term "capturing the effect of.." or "controlling for.." so far I've just accepted it without ...
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Selected variables varies depending on whether or not standardization is in lasso regression (glmnet)

The paper often suggests both standardized and unstandardized coefficients in the lasso model (glmnet in R). However, when I run glmnet, the selected variable is different depending on standardized =...
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SVM as linear equations

I'm using SVM for a regression problem (sklearn.svm.SVR). After I train my model I use these 2 attributes svr.coef_ and ...
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How to compare LME regression coefficients across models with same variables but different sample sizes/trial numbers?

I have a quite specific situation that does not seem to be covered by other, similar posts: I ran a study where a task (Task A) was periodically interrupted by a probe that asked participants what ...
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Using a subset of parameters in joint confidence region of a linear model

For a standard linear model of the form $y = X\beta + \epsilon$, where $\beta$ is a vector of parameters. we can calculate an individual confidence interval for each parameter (of 1-$\alpha$ quartile)....
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How to interpret the resulting negative regression coefficient? [duplicate]

How to interpret the resulting negative Poisson's regression coefficient? I am investigating the effects of environmental factors on mortality. A negative regression coefficient means feedback (proves ...
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Dynamic regression, models with coefficients = 0 chosen as top models

I am running auto.arima on part of a time series (training data) using all possible combinations for several external regressors. I then choose the top 5 models according to fit to testing data using ...
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Incorporating rank-ordered logit results from different samples

I would like to create rank-ordered logit models to predict the outcome (winner in this case) of variants of a multi-player game. For the most part, the predictors for each variant differ. However, in ...
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How to calculate coverage rates for 95% confidence intervals for estimands (like regression coefficients)?

I'm working on a Synthetic Data Generation model, and I'm confused about a metric mentioned in a seminal paper (details of paper added below) Context: Synthetic Data Generation involves sampling from ...
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OLS reinterpretation (?)

I have read on a book written by a professor something similar to the following and want to check this statement on the forum, since it is the first time I have heard it. I have read that the OLS ...
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Determining Intercept for Regularized Logistic Regression

Going off of the standard set up, we have $N$ observations and $P$ predictors stored in the data matrix $\mathbf{X} = \{ x_{i,j} \}$ for $i = 1, \ldots, N$ and $j = 1, \ldots, P$. The response is ...
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How do I compute a Cohen's d from b weight and standard error?

From Table 3 and the paragraph below, you have a good deal of the information I have available. N's for taser and non-taser conditions are 339 each. My initial inclination is to take the raw B weight ...
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Logistic regression vs segmentized logistic regression

This might be a very rookie question. I need help interpreting the results of my logistic regression. Assume I have the following model: y ~ categorical + numerical...
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Interpreting Multiple Regression [duplicate]

I am working on my thesis and I had to do a multiple regression. I have only 45 samples and I checked that there were not problems of multicollinearity (VIF < 3) and the residuals are normally ...
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Are correlated errorbars useful for regression fitting?

Let's say I have a dataset that roughly fits a 2nd order polynomial but I trying to fit it using a linear regression (see image). The error in my y measurement depends on y, but the coefficient of ...
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How Do I Summarize To a Single Estimate After Bootstrap Resampling For Various Statistics?

It is relatively straightforward when I want to know about the coefficients, fitted values, residuals and residual standard error of my (ordinary least squares) regression models, especially if you ...
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How to average 1000+ regression coefficients?

I am doing OLS regression on 1000's of stocks forward returns against a factor score. All the factor scores are the same model results for each stock at some point in time and vary between 1-5. My ...
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Non-linear regression where dependent variables are dependent on different independent variables in R

I would like to know how to proceed with the following non linear regression analysis, which is a simplified version of my real problem. 5 Participants where asked to observe the speed of three ...
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48 views

Closed-form solutions for constrained multiple linear regression

Normally a multiple linear regression is unconstrained $$y=X\beta+\epsilon$$ so that closed-form solutions in the case of data orthogonality ($X^\top X=I$) are $$\beta=(X^\top X)^{-1} X^\top y$$ ...
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Correlation coefficient of x and y

If we have $$ X\sim Poisson(\lambda), Y|X = x\sim Binomial(x+1,p) $$ What is the correlation coefficient of X and Y? So I used $$\rho=\frac{Cov(X,Y)}{\sqrt{Var(x)Var(Y)}} = \frac{E[X[E[Y|X]]-E[X]E[...

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