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.

Filter by
Sorted by
Tagged with
0
votes
0answers
14 views

Interpreting truncated normal and lognormal coefficients

I am running a truncated normal regression and a lognormal regression as the second part of a double hurdle model. The dependent variable is transaction revenue and I have several independent ...
0
votes
0answers
28 views

Interpreting inverse hyperbolic sine transformations with indicator independent variable in polynomial regression

I have a regression discontinuity design with the following specification: The specification is a polynomial regression with an indicator variable to capture the average treatment effect. What is the ...
0
votes
0answers
32 views

ACF and CCF interpretion

I plotted ACF and CCF for two time series. But, I do not know how to exactly interpret them. I want to know how and which information can be found from these plots. Can I get any coefficient from CCF ...
0
votes
1answer
24 views

Formulating Null and Alternative Hypotheses in a Regression Model

I have a simple question regarding formulating research null and alternative hypotheses in a simple econometric regression model. In my research Hypothesis, I expect X to negatively affect Y (from ...
0
votes
0answers
17 views

Comparing betas of two different regression analyses

I have a basic question related to regression analysis as I’m looking for the right method to address my problem. In a first step I run a regression with Y as my dependent variable and CA as ...
0
votes
1answer
35 views

How to code variables to avoid singularity in quasibinomial glm model of pre/post test means?

I'm performing a meta-analysis of studies that compared treatment and control groups from pre-test to post-test using means of count data. Other authors have performed similar analyses using raw ...
0
votes
0answers
9 views

The baseline-category coefficient in the multinomial regression

After I run multinomial regression, I get N-1 beta coefficients. I need to obtain also the last coefficient, which is the baseline category. Any suggestions in R? Thanks
0
votes
0answers
28 views

What is Ridge trace and Lasso path?

More specifically, what does "Ridge trace of regression coefficients" and "Lasso path of regression coefficients" mean? Are they the same as "Ridge coefficient paths" and "Lasso coefficient paths"?
0
votes
0answers
19 views

cents per gallon to gallons per person - regression intepretation

Equation: fuel = B0 + -7.441gatax fuel: motor fuel uses in (gallons per person) gatax: state gas tax in (cents per gallon) How would you explain a 1-unit increase in the gas tax? thanks for any ...
0
votes
0answers
12 views

Find highest scoring yet most consistent performer

I have several measurements with a mean and a standard deviation (but I believe that the coefficient of variation is more useful here). For example: A: Mean (in regards to the total): 90%, CV: 25% ...
0
votes
1answer
39 views

Linear Regression with coefficient of a, b, c and d?

First I am a beginner and am trying to understand linear regression. I was reading an article and saw a formula: ...
0
votes
0answers
18 views

How can I compare incremental-r2 between groups?

0 I have a dataset (n=6000) of individuals belonging to three different ancestry groups (african (n=2000), native american (n=2000), european (n=2000)). I am studying how well a predictor (x) ...
0
votes
0answers
50 views

Linear regression $Y=X\beta+e$ with random coefficients $\beta$

Consider a linear regression model $Y=X\beta_0+\epsilon$. Here $Y$ is the response random vector of length $n$, $X$ is an $n\times p$ matrix, $\beta_0$ is a constant vector of length $p$, and $\...
0
votes
1answer
31 views

How to find coefficients for Logistic Regression [duplicate]

How do you find the coefficients for logistic regression? Is it a case of just transforming the samples from the dependent variable and preceding to fit it like a linear regression?
0
votes
0answers
37 views

Bootstrapping logistic regression coeffs with z-scored predictors in logistic regression

Hoping someone can help clarify why there is a discrepancy in regression coefficients when applying bootstrapping to z-scored vs. raw data. There is probably a simple explanation but in all my ...
0
votes
0answers
33 views

Correct interpretation of coefficient estimates from GLM on binary outcome data [duplicate]

I'm currently analysing an experiment where animals were presented with a stimulus under two different treatments (Po & Br) ...
0
votes
0answers
37 views

Different Formulas for the Standard Error of Regression Coefficient

Premised that I am no expert in stats or algebra (but I am trying to learn), I have found these two different formulas for the standard error of the regression coefficient. \begin{align} \...
0
votes
1answer
32 views

Regressions - variable effect

This is more of a theoretical question than concerning a specific data set. Say that I have a dependent variable Y and want to check if 4 different explanatory variables X1, X2, X3 and X4 has an ...
0
votes
0answers
16 views

Variable Importance sorting by absolute value of x or fully standardized coefficient?

I am looking at the output of a linear regression model and would like to sort the IVs by feature importance. In this case I want to use the absolute value of the standardized coefficients since my ...
0
votes
1answer
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 ...
0
votes
0answers
22 views

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=...
0
votes
1answer
30 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 ...
0
votes
0answers
28 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 ...
0
votes
0answers
26 views

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 ...
0
votes
0answers
18 views

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 ...
0
votes
1answer
21 views

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 $0.05$, ...
0
votes
0answers
18 views

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 ...
0
votes
0answers
8 views

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 ...
0
votes
1answer
72 views

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 ...
0
votes
0answers
21 views

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 ...
0
votes
1answer
21 views

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 ...
0
votes
0answers
24 views

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 ...
0
votes
0answers
9 views

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 ...
0
votes
0answers
22 views

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 ...
0
votes
0answers
12 views

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 ...
0
votes
0answers
18 views

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 ...
0
votes
0answers
19 views

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: ...
0
votes
0answers
30 views

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). ...
0
votes
0answers
14 views

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 ...
0
votes
0answers
32 views

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 ...
0
votes
0answers
17 views

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 ...
0
votes
0answers
13 views

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 ...
0
votes
0answers
148 views

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 ...
0
votes
0answers
16 views

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 $...
0
votes
0answers
55 views

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 ...
0
votes
0answers
18 views

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. ...
0
votes
0answers
42 views

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} = ...
0
votes
0answers
133 views

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 ...
0
votes
1answer
19 views

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 ...
0
votes
1answer
29 views

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 =...

1
23 24
25
26 27
29