Questions tagged [multiple-regression]
Regression that includes two or more non-constant independent variables.
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Variable selection in logistic regression [duplicate]
So I'm trying to make a multivariate logistic regression model in R studio. I'm not sure how to go about this. What seemed to make sense to me was to model every predictor against the response ...
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Time series model without ARMA component and with exogenous variables
I am trying to know what is the most simple model for time series data, with exogenous variables. What is the most simple framework I can use ? Is it possible to build a model more simple than ARIMAX, ...
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Interactions in regression and choosing appropriate model?
I’m running the following regression model, where MR is a continuous predictor and grade, gender, and race are all categorical.
DV~MR + grade + gender + race
I’m also interested in how the 3 ...
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Modelling 3-level MLM of longitudinal data in R
I have two questions regarding modeling a 3-level multilevel model in R.
I have a dataset of different variables that were assessed 4x as part of a longitudinal study. At each of the four assessments ...
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Measuring total relative influence of groups in particular coefficients in a multiple linear regression
Suppose I have run a multiple regression model:
Y = B0 + x1B1 + x2B2 +..+ xnBn, weighted by w, from a dataset with such covariates and the weight variable of size N. Say there is another column in the ...
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Mediation analysis with interaction effect
I am currently trying to amend a paper that I am writing in order to include the interaction effects (a4 and b4 in the spreadsheet) This results in the following sum-product $$a_1b_1 + a_2b_2 + a_3b_3 ...
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using standardized residuals from a previous chi-square analysis as an IV in a regression
I have an analysis I'm considering, and I'm not familiar enough with the statistical details to tell if there are any potential errors. I've done my best to describe the analysis briefly below.
I ...
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Regression analysis with single outcome variable and binary independent variables
This is a meta-analysis. I'm looking at factors associated with vaccine acceptance in many countries. Looking at previous studies in different countries, I recorded factors that are associated with ...
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Why is there a negative bias in this example of omitted variable bias?
I was learning the mechanics of omitted variable bias in the context of linear regression. I built the following simple model with R:
...
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Compare beta of single regression and multiple regression
Given that:
$$
\text{Corr}(Y, X_1) > 0 \\
\text{Corr}(Y, X_2) = 0 \\
\text{Corr}(X_1, X_2) > 0
$$
Consider 2 regressions:
$$
Y = a X_1 + \epsilon \\
Y = b_1 X_1 + b_2 X_2 + \epsilon
$$
Which one ...
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Pros and cons of different methods for comparing betas in regression
In my line of work, we often hypothesize that one continuous predictor will have a stronger relationship with some outcome than another closely related (i.e., collinear) continuous predictor. We fit a ...
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Choosing number of predictor variables for regression models based on sample size and desired power
Based on comments a previous question, I have changed my question.
I am wanting to see if the addition of "novel" variables to a model containing "traditional" variables improves ...
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Sample size and power calculations for multiple linear regression looking for an r squared increase [duplicate]
I want to perform a multiple regression, comparing a model with "traditional" risk factors, with a model that has two tested "novel" risk factors.
I'm want to make sure that the ...
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Knots in regression and the dummy variable trap
I am running a knot-like type of regression and have a couple of questions:
Imagine that we are working with daily data that spans over $3$ years.
Consider the following model:
$y_t = \beta_{0, t} + ...
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Robust way to add predictors to existing linear model
I'm looking for a robust way to gradually build up a regression model -- namely I have a linear base-model with a robust set of predictors for which I'm fairly certain I have near optimal weights for, ...
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Does it make sense to talk of "multicollinearity" in the context of simple linear regression?
As far as I am concerned, "multicollinearity" referers to the presence of collinearity between two or more variables, even if there is no pair of variables that have a particularly high ...
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Can I do a multiple linear regression analysis with a mixture of raw data and index data?
I'm trying to do a multiple linear regression analysis in Excel using the Analysis Toolpak and I am not good at math, let alone stats. So please excuse my total ignorance. I'm using the following ...
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When does SEM have little to no benefit over multiple regression, and there is a distinction without a difference between two approaches?
I recently saw a case where someone fit a SEM with 20 latent variables (with many indicators each) predicting a single latent variable (of several indicators), and suggested it was evidence for some ...
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Standardizing OR from logistic regressions with log-transformed variables for meta-analysis?
I´m trying to meta-analyze odds ratios from logistic regressions; some of which log-transformed the independent variable first.
(i.e. some studies present an OR per +1 in the independent variable, ...
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How to deal with interaction terms in regression that cannot have a negative product?
Assume we have the following model: $y = \beta_0 + \alpha_1 * x_1 ^{\beta_1} + \alpha_2 * x_2^{\beta_2} + \alpha_3 * x_1^{\beta_1} * x_2^{\beta_2}$ where as we have the following priors for our IV's $\...
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Dealing with 0's in loglog regression by using indicator functions I(x > 0)?
Assume we want to estimate the following model
$y = e^{\beta_0} * x_1^{\beta_1} * x_2{\beta_3}$ which we can linearize into
$\log(y) = \beta_0 + \beta_1 * \log x_1 + \beta_2 * \log x_2$
Assume that ...
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loglog regression with 0's in IV's
Assume we have 2 predictors $X_1$ and $X_2$ and an outcome $Y$ that we wish to model with the following function
$y = e^{\beta_0} * X_1 ^{\beta_1} * X_2^{\beta_2}$
Also assume that we have some priors ...
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Could multicollinearity be messing up my logistic regression? Can I overcome it?
My data has 5 binary dependent variables, 9 categorical independent variables, and 3 continuous independent variables, with a sample size of 1232. The 5 dependent variables are just different ways of ...
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Are these two definitions of the coefficient of determination $R^2$ equal?
I want to do multiple linear regression as explained on this Wikipedia site: I am given the following data:
$$
yx=(~(y_1,x_{11},\ldots,x_{1p}),\ldots, (y_n,x_{n1},\ldots,x_{np})~)
$$
of $n$-many ...
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Should I perform CFA after the EFA and then move to multiple regression analysis with the outcomes?
I have gathered 41 variables that are supposed to explain dependent variable Y in a dataset. Is the following reasonable?
First, I will conduct EFA, reduce the dimension, conduct CFA to confirm/reduce....
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Should I remove the intercept when I have one dummy variable that covers all the categories in a categorical variable?
I have a categorical variable that has $4$ categories, and I have two dummy variables, $x_1$ and $x_2$, that cover this categorical variable. The $x_1$ variable has values of only $1$ without any ...
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My "lagged consumption" variable accounts for all the variation in my dependent variable
I chose the topic of consumption for my assignment in econometrics. My explanatory variables are interest rate, consumer credit, oil price, disposable income and lagged consumption by 1 year.
Using ...
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Methods for fitting a distribution to regression data
I'm hoping to find a method/algorithm/approach for fitting to a distribution to regression data.
Essentially I have a problem where I have survival data with independent variables, but only cases that ...
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GLM (Poisson regression) and Linearity
I understand that an assumption of Poisson regression is a linear relationship between the transformed expected response in terms of the link function and the explanatory variables. Therefore you ...
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RSE increases when RSS decreases (relative to increase of number of predictors)
In Intro to Statistical Regression (Python) on page 88 "RSE can increased when newspaper is added to the model given that RSS must decrease." Newspaper is just another predictor.
The text ...
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Interaction with dummy variable: How to access std. error, t value, p value, (and others) for the opposite manifestation of dummy
Preparation
Using R-Libraries: library(dplyr)
The situation
Data
Given the data
...
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Proof of general linear process autocovariance
I am struggling to get to the general formula of the general linear process autocovariance.
If $Y_t = \mu + \sum_{k=0}^\infty \omega_k e_{t-k}$ where $e \sim WN(0,\sigma_e^2)$ (a.k.a. the general ...
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Using original or imputed datasets in analysis plan for survey data?
I'm working with survey data that includes about 100 variables and about 3,500 respondents. Out of the 100 variables in the dataset, a lot of them had more than 5-10% missing data, and many were ...
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Questions regarding indirect effects mediation regession
I am trying to test the significance of the total indirect effects of 3 variables and I am required to use multiple regression modelling (the mediators were unintended). While I found an equation from ...
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Definition of fixed effects used in Journal Papers
I have a question regarding time fixed effects and their definition in empricial Papers.
Authors often talk about (1) estimating an OLS Regression and employing time and country fixed effects. What I ...
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Can you run a t-test with regression betas/coefficients from the output of separate models?
In layman terms, if all covariates included in the individual models were the same, would it be fair to run a t-test comparing the coefficients for a variable of interest, e.g. age, where I want to ...
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I am unable to relate the normal distribution dependency for regression . I need a mathematical intuition cum understanding for regression assumptions [duplicate]
What is the mathematical significance for the assumptions of linear regression to hold true for arriving at a single/multiple regression formula? Can anyone use the assumed normally distributed ...
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Addressing Multicollinearity
Say you regress $Y = x_1,….x_k$ and find out that you have multicollinearity. I propose the following solution:
Say you suspect $x_1$ and $x_2$ are collinear. I regress $x_1$ on $x_2$ and get the ...
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Is it possibile to reverse engineer a partial R2 squared from a multivariate regression table?
I don't have any data, so I'm trying to extract a partial R-squared for one of the predictors from a linear multiple regression, in order to calculate the sample size for a regression study I would ...
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meta-analysis of prognostic factor for models with different parameters
I am trying to meta-analysis the OR of a variable (troponin) for a dichotomous outcome (mortality). There are several multivariate logistic regressions out there, but none of them share the exact same ...
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Feature selection in a traditional regression model to an experiment data
I have an experiment data (total of 96) with 10 predictor and 2 response variables. I want to build a traditional multiple linear regression model to them in R. My aim is to build clearly ...
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Indicator variables in a Linear regression
For linear regression analysis, will changing the indicator variables
change the table of sums of squares? Is there a way to illustrate this through a mathematical way via statistical formula or ...
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How to test the importance of environmental variables to a crocodile when selecting a nest site (geographic location)
I don't have a background in stats so please bear with me. I'm developing a study on where saltwater crocodiles nest. I want to see how variables such as slope of the bank, sun exposure, vegetation ...
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How to deal with different orders of integration between explained and explaining variables?
Is there a standard, or at least a valid, regression approach if you are trying to regress a dependent variable with a unit root against a set of stationary independent variables? I know I could ...
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Multiple linear regression homoscedasticity/linearity
My question is about the implications of the violation of homoscedasticity/linearity for multiple linear regression. I have tried to find the answer in multiple sources but could not figure it out.
I ...
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Understand multiple linear regression coefficients
There are multiple posts asking for help in understanding the coefficients. Yet, I failed to find one that addresses the following.
...
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What tests should be done before running a multiple regression model on balanced panel data?
I am currently a graduate student working on writing my master’s thesis. The master's thesis includes conducting a regression model on a set of bank data, which includes financial indicators for the ...
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I want to fit a linear regression model, but I know that if I treat each instance as unique there will be many pseudo replicates
I want to fit a linear regression model, but I know that if I treat each instance as unique there will be many pseudo replicates.
I want to explore the relationship between 2 binary variables and one ...
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Negative adjusted R2 in fixed effects model: what could this mean?
I have a large panel with 4 time periods and 10,000 observations per year. First I do OLS regressions for each year, and they all look fine. Then I run the FE model for the entire period, and one ...
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Test or training data? R², predicted R² and adjusted R²
I would like to understand the difference between simple R², predicted R², and adjusted R². I have done several research and readings, but the difference is still not clear to me. I have even reached ...
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