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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39 views

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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31 views

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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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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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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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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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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128 views

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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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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57 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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Interpret GLM log link coefficients

I am currently doing a college assignment in which I have a GLM model in the gaussian family with a log link. I would like to know what the impact per variable is. I know how to calculate the ...
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How to interpret coefficient if the independent variable has negative values?

I have a simple OLS regression: Y=a+B*X+epsilon X is the percentage change in house prices and has negative values (except in the 99th percentile). If the sign of 'B" coefficient is positive, how ...
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1answer
108 views

comparison of coefficients (they are not Standardized Beta ones)

I estimated a model with three-stage least squares (3SLS) and have two main explanatory variables (the model also includes a set of control variables but they not important at the moment). I want to ...
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interpretation of coefficients of independent variables in fractions

I am a bachelor student finishing my thesis and desperately need some help with the interpretation of regression coefficients. I am currently quite confused and my deadline is three days away. I have ...
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Elastic Net - number of non-zero variables

I have a question regarding the interpretation of the trace of coefficients when running Elastic net with the package glmnet in R. This is the plot I obtain with alpha = 0.5 My understanding is that ...
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Will LASSO choose variables that are highly correlated with the outcome variable?

Suppose we have access to an outcome variable $Y_i$ and a $p$-dimensional vector $X_i$ for $i=1,\ldots,N$. We run a LASSO regression of $Y$ on $X$ for every penalty/shrinkage parameter $\lambda$ in an ...
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1answer
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Corroborating a differnce in differences identification strategy

I read in Mostly harmless econometrics that a good way of testing whether a difference in differences is a good identification strategy is running this equation: where the first sums are post-...
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Validity assessment of multiple regression marketing mix model

My employer has engaged a consultancy firm to carry out marketing mix modeling in order to quantify the impact of various marketing activities and promotional campaigns on overall sales and also for ...
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the constant coefficients are penalized in the ridge logit conditional model?

I am estimating a conditional ridge logit model, there is very little bibliography about it, and I do not know if the constant coefficients are penalized. My model has 2 variables and 3 alternatives, ...
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Correlation formula for a Quadratic

I have used quadratic regression on a dataset to find the graph of best fit, that is, finding the coefficients a, b and c in the general formula of y = ax^2 + bx + c. Having done that I would now ...
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56 views

Regression coefficient signs different for log/asinh and level versions of same variable

I ran two regressions. First, say, y=βx, and the second one, asinh(y)=βx, which I read is asymptotically equivalent to log(y)=βx, where x and y are the same variables with same data set for these two ...
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Interpreting ridge coefficients as a function of regularization

Data consists of 40 observations with 4 dimensions and a response-variable. When doing a ridge regression on my data and plotting the coefficients and coefficient errors (MSE of the ridge ...

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