**1.012 regression-coefficients questions.**

I have a linear regression model $\bf Y=\bf{X}\bf{\beta}+\epsilon$. I want to assign a prior on $\bf\beta$ in order to derive the posterior predictive model $p(y_{predictive}|\bf{y},\bf{X},\beta)$. ...

Based on the path estimate from Structural Equation Modeling in a scholarly text, I would like to state that an x% increase of Variable1 leads to y% increase in Variable2 - I know that this is not ...

Let's say one of the predictor variables in a regression model is 3-point shooting percentage. However, some of the observations (players) only have one or two attempts while others have several more. ...

I would like to know under which assumptions you would prefer one concept to the other.
I would like to conduct an analysis of the relation between sales and expenditures in a sample of financial ...

I am running a negative binomial regression using statsmodels on Python.
My DV is count data and zero-inflated. The one IV in my model is categorical and I have no constant term, and my understanding ...

I want to regress crop yield against total rainfall collected over many years.
For each year, rainfall could be computed for different time periods i.e. total rainfall can be calculated
between 1st ...

I have a couple of empirical studies examining the determinants of credit ratings. Here, the dependent variable is a binary variable indicating whether a firm has a credit rating or not ($rating$). ...

How do I estimate price elasticity in a non-linear price setting? Non-linear prices are seen in utilities (electricity, water etc.) where the price per unit is determined by quantity purchased.
So a ...

Basically I want to know how the 'constant' value differs in each of the following models:
Model 1:
DV=income; IV1=gender (0=male, 1=female); IV2=location (0=east, 1=west)
Here, I understand the ...

In regularized least squares 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 ...

I noticed small differences between the coefficients generated by cv.glmnet() and glmnet() when the same lambda was applied. I am wondering why this happens.
Codes below will reproduce the phenomenon ...

I'm new to hierarchical models and am learning to use the lme4 package. My understanding is that the fixed effects generated from the lmer() function are suppose to match the coefficients from lm(). ...

I am using Python's scikit-learn to train and test a logistic regression.
scikit-learn returns the regression's coefficients of the independent variables, but it does not provide the coefficients' ...

I'm currently working on building a predictive model for a binary outcome on a dataset with ~300 variables and 800 observations. I've read much on this site about the problems associated with stepwise ...

My model is a log linear when my dependent variable is log GDP, and my predictor is not log transformed. One of my regressor is ratio between consumption and income. I understand that to interpret log ...

I have fit a negative binomial model in R, and would like to report the findings, but I'm unsure how (or if) I should convert the estimates to reportable coefficients. Here is my output:
...

I have performed a simple regression of the following form:
$y_t$ = $\beta_1x_{1,t}$ + $\beta_2x_{2,t}$ + $\beta_3x_{3,t}$
the $R^2$ turns out to be 38%.
$\hat\beta_1$ = 0.7 (significant at the 1%-...

I had this question at the back of my mind for a while. Consider any data for linear regression problem. Optimization algorithm calculates the coefficients of each feature and stops when cost function ...

I am looking for a procedure to test for a significant difference in the beta coefficient for a given predictor at one step of a regression model versus another step.
That is: if, say, at step 1 ...

I would like to ask you how to express the following regression analysis.
$Y=a+bX+cZ+u$
I want to mention that $b$ is significant.
In this case, which sentence in the following correct?
(1) The ...

There's many questions on related topics but I have been unable to find one that precisely answers my question.
Let's say I'm performing a regression on multiple predictor variables $x_1...x_n$ for ...

I am building regression models to evaluate the effect of several characteristics of genetic variants (my predictors) on a handful of phenotypic parameters (continuous and binary - hence I am building ...

I understand that r tells us the strength of the linear relationship between two variables, R shows how closely two variables, and β shows which dependent variables would change if we change the ...

I am using linear regression to look at the relationship between some variables using SPSS but I'm having trouble understanding the results:
In the table of coefficients, I know most of the rows ...

I realize that this is a very basic question, but I can't find an answer anywhere.
I'm computing regression coefficients using either the normal equations or QR decomposition. How can I compute ...

Background:
Our thesis explores the relationship between Corporate Social Responsibility (CSR) and Return on Assets (ROA). ROA is computed by multiplying the firm's Profit Margin with Asset Turnover ...

I need some advise of how to quantify the uncertainties around the regression estimates.
I have collected crop yield data across multiple locations and multiple years.
The crop broader cultivation ...

I am working on a project to find Productivity of a person in near future and i am using Decision Forest Tree in Azure Machine Learning studio. I have a good amount of sample data (5459 rows) & 9 ...

After seeking clarification about linear model coefficients over here I have a follow up question concerning non-signficant (high p value) for coefficients of factor levels.
Example: If my linear ...

I've been trying for a while to understand how to perform this type of analysis, but I can't seem to find any literature or even forum posts about it, so any help or guidance anybody can offer would ...

I am testing whether self-reported days' use of illicit cannabis in the previous 28-day period predicts levels of a cannabis metabolite measured in participants' urine. There are four 4-week periods, ...

I am using the RMS package in R to conduct a logistic regression that contains a three-way interaction. As part of my modelling approach, I have conducted chunk tests of the interaction (using Wald ...

I'm fitting a response variable that assume values between 1 (Very Dissatisfied), 2 ,3 ,4 and 5 (Very Satisfied). My explanatory variable assumes also values between 1 and 5, in other words, dependent ...

I need some help with some basic regression method. Let's say that we have a tri-variate linear model with continuous variables (as dependent and as independent).
$$y=\beta_0+\beta_1 x_1+\beta_2 x^*...

I have difficulty of understanding p-value from
An Introduction to Statistical Learning by Gareth James • Daniela Witten • Trevor Hastie
Robert Tibshirani (2015) : 67
Consider simple linear ...

I made an earlier question regarding this, but the formulation was quite unclear so I had to make a new one; consider the following problem:
Let's say I have a dataset with 2 variables and I want to ...

My question is based on the following discussion we often see when people try to model citation counts for research articles.
The outcome variable is citation counts for an article and some typical ...

I am estimating a binary logistic regression with L1 norm. According to the regression coefficients, the sign of x1's ...

Two variables $X, Y$ are suspected to be perfectly correlated. However, we want to test this, i.e. whether $R^{2}_{X,Y}=1$ (positive or negative $r=\pm1$ i.e. perfect determination). Ideally, we want ...

I am analysing data on symptoms, signs, and autopsy findings (a set of binary Y/N variables), viral serology, for several viruses, and a few other covariates (Age, gender, site) in pigs. After some ...

I am undertaking some medical research using R.
My outcome of interest is mortality in the intensive care unit.
Data
My data looks like this (there are ~15,000 rows).
...

I am using multivariate autoregressive (MAR) models to fit my long-term dataset of species abundances and environmental variables but when I use only the data from a specific period of the year (e.g.,...

For two independent variables, what is the method to calculate the coefficients for any dataset in logistic regression? The equation we know is => logit = ln(P/1-P) = B0 + B1 * X1 + B2 * X2
On the ...

I am trying to cluster data for a regression problem and wonder if I am way off in my approach or if there is something in it.
Problem: make a model of impact of variable L1 and L2 in Output. Output ...

I'm wondering whether there is a way to check whether a coefficient in a linear mixed effects model changed significantly after introducing a new variable. I have two models:
...

I am trying to analyze this data by linear regression and found experimental outcomes given below:
...

Last week my team and I discovered a strange phenomenon with the coefficients of a logistic regression (LR). As we included more samples from a static dataset, the magnitude of the coefficients of the ...

I have a multiple regression problem. Let's say there is a physical system with a true model:
$$
y = b_0x_0 + b_1x_1 + b_2x_2 \;\;\;\;\;\;\;\;\;\; (1)
$$
Now, imagine I only have access to a ...

I am wondering the difference between them. Basically they do the same job at the end finding coefficients of parameters, but they look just different the way we find the coefficients. To me, Least ...

I'm researching the correlation between the magnitude (a measure of brightness) and redshift ($z$ - a measure of distance) for a variety of galaxies called quasars. Plotting the magnitude against $log(...

- regression
- multiple-regression
- r
- logistic
- interpretation
- linear-model
- statistical-significance
- categorical-data
- interaction
- hypothesis-testing
- correlation
- least-squares
- generalized-linear-model
- standard-error
- standardization
- p-value
- econometrics
- nonlinear-regression
- confidence-interval
- linear
- logarithm
- time-series
- self-study
- anova
- spss

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