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Questions tagged [multiple-regression]

Regression that includes two or more non-constant independent variables.

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The function step.lmRob() is not working [closed]

I have a linear model, which i analyzed (in R) through: lmrob_object<-lmrob(diff_mg ~ age + bmi + energy + fiber + ca + phos + iron + potas + supp + uni, data = data), where: diff_mg is the DV (...
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Significance of the coefficients of a multinomial mixed regression built with npmlt function

I fitted (using npmlt function) a mixte multinomial regression model which ran without errors. I expected that the outputs of the model include p-values that I didn't find. My need now is that someone ...
Romaine TCHEDJI's user avatar
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How to perform model comparison based on multinom( ) function of nnet package in R?

My independent variables are gender and sequence, and the dependent variable is intervention (including 3 intervention methods). I established a multinomial logistic regression model to examine the ...
zhang xia's user avatar
6 votes
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Interpreting interaction term

Suppose I am interested in estimating the effect of a treatment (vs a control), fully and conditional on different levels (say, 3) of a baseline caracteristic (say, income level). I would have a ...
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Multiple Dependent Variables, One Independent - with dummy variables

I am trying to run regression models and don't know what type of regression to be running. I have one independent variable (binary variable) and 8 dependent variables (3 discrete, 3 categorical, 2 ...
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How to determine probabilities that maximize likelihood in logistic regression in case of categorical variable [closed]

Edit: Let's say that we want to predict if mouse is Obese (Y=1) vs NotObese (Y=0) given that the predictor is the fact that a mouse has a normal Gene (X=0) vs Mutated Gene (X=1). I can deal with this ...
amous's user avatar
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What is the difference between a) multilevel modelling and b) adding a categorical IV to a multiple regression?

The examples of multilevel modelling I have seen are equivalent to treating the group as an extra categorical IV in a multiple regression. For example, if children are grouped into N classes, you ...
Mohan's user avatar
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Multiple Linear Regression/ANOVA Help in Excel

I am working on a linear regression model and keep getting zero as my coefficients and the NUM! error as my p-value. I was told to organize the data this way but was then told by someone else that I ...
Professional_Half's user avatar
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First and second moments of OLS slope when Conditional Expectation Function is not linear in covariates

Suppose I have a joint distribution of "outcomes" and "covariates", $(Y,X)$. Define the slope of the population "best" linear predictor of $Y$ given $X$ as $$\beta = \...
stats_model's user avatar
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Fitting multiple linear regression models to select molecules for which a feature of interest significantly alters concentration

I'm using data from a proteomics platform called Olink. In this case, the data comes from samples of 16 patients and 16 controls. The patients can be further classified into 2 subgroups. The assay ...
maglorismyspiritanimal's user avatar
7 votes
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General Linear Mixed Model: How do I fix 'Rescale variables? Model is nearly unidentifiable' error on glmer

I'm trying to fit a generalized linear mixed model (GLMM), but I'm getting a persistent error. I'm looking at the relationship between weather (continuous variables: rainfall, maxtemp, and mintemp) ...
Hazel's user avatar
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Count predictor and binary outcome

Is a binary logistic regression the best approach when I have a count predictor and a binary outcome? Can I apply a multiple binary logistic regression model if I have more than 1 predictor that is a ...
stephan_phd's user avatar
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Linear regression applied to time series with trend - how to deal with it and how to interpret results?

I need to fit a linear regression model on time series. For simplicity, let's assume that we have only three variables: Y - has visible trend; after differencing is stationary X1 - has visible trend;...
Brzoskwinia's user avatar
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Adjusting a multivariate predictive model for drifting seasonalities

This question is a repost of a question originally asked in Quantitative Finance. I was alerted that this would be a more appropriate place for it. I have a time series of daily observations that get ...
Guillermo 's user avatar
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Is duplicating dataset an augmentation?

For a very small dataset, there is a lot of overfit in the random forest regressor model. I have removed extraneous data, scaling and feature selection, but overfit is still there. The oversampling ...
Erfan Mollai's user avatar
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logit model - labor probability(odds ratio) [duplicate]

The logit model is given: $$ \text{Labor probability} = \alpha + \beta_1 \cdot \text{income} + \beta_2 \cdot \text{age} + \beta_3 \cdot \text{education} + \beta_4 \cdot \text{young kids} + \beta_5 \...
silvia's user avatar
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Moderation in linear mixed model

I ran a Linear Mixed Model in R with 2 centered predictors and a Group variable. fit1a <- lmer(DV ~ Predictor1*Group + Predictor2*Group + (1|...), data) One of ...
KayAnn's user avatar
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GLM Multiple Comparisons

I am performing several Generalized Linear Models in my analysis and I am wondering which method to use for adjusting p-values due to multiple comparisons. I have 4 outcomes (judgement of intensity ...
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Manual selection of parameters and features and bad results by gridsearch

For a very small dataset that I have, when I set the parameters with the help of gridsearch, the test and training results are not acceptable at all and have a huge difference. I have to manually ...
Erfan Mollai's user avatar
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Using long-run propensities to compare magnitude of association among independent variables

Is it feasible to add up all significant lags of respective independent variables (disregarding insignificant ones) in order to compare the strength or magnitude of their respective association with ...
Mandarina's user avatar
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Multiple regression with two continuous predictor variables with R

I'm trying to find a suitable multiple model (with two continuous predictor variables) for my data and I'm not sure if a linear model with lm() would be sufficient ...
Mogens's user avatar
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1 vote
1 answer
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How to improve a model with little dataset? [duplicate]

I have a dataset that has 20 features and 65 samples. I did data scaling. I also did feature selection in different ways. But this is the result. ...
Erfan Mollai's user avatar
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When is E[Xi*ui]=0 violated, given that the residuals are by construction of the model uncorrelated with the predictors?

To begin: I am aware that the concepts of the "error term" and "residual" two distinct ones. Yet, I have difficulties understanding their implications for (multiple) linear ...
Econ4221's user avatar
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Is the intercept of a complex sum-coded regression basically useless for interpretation? Maybe even for some simple models?

In regression analysis, one may choose to code categorical variables differently depending on interpretability considerations. One such coding scheme known as sum coding (a kind of effect coding ...
nsa's user avatar
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In a mixed model with intersubject and intrasubject variable what random effects should i put?

My master's thesis director wants to use a linear mixed model to analyze my data. My experiment has a task where participants click on a touch when a stimuli (a word) appears on screen. The dependent ...
rose r's user avatar
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VIF/GVIF for binary logistic regression

How do I decide if there is multicollinearity or not in my logistic regression? Specifically, the GVIF OR GVIF^(1/2*Df). How ...
Diyan Milla Hanifah's user avatar
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1 answer
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Proving the equivalence of two distinct approaches to multiple regression for binary classification

I'm stuck with this peculiar problem that uses multiple linear regression in order to solve a binary classification problem (note: it's not considering the logistic version or any other GLM approach). ...
evans5's user avatar
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How to convert residual predictions back to original unit in a multiple regression model

I am trying to use a multiple-linear regression model based on observed air temperature (ta) and precipitation (pr) time series ...
StatsNewbie0's user avatar
1 vote
1 answer
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Understanding the coefficients of highly correlated features in generalized linear models

I am trying to fit a generalized linear model, for simplicity assume that is a linear regression. I have a bunch of features and I fitted a linear model to it, the feature ...
shurik's user avatar
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Derivative of Linear Model with respect to Residual

I am looking at two sections on the wikipedia page for total least squares, specifically: #Allowing_observation_errors_in_all_variables and #Example I have two questions, the first is how does one ...
A Friendly Fish's user avatar
4 votes
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Is lasso preferable to ridge or principal component regression in multicollinear settings?

Consider a $N\times p$ data matrix $\mathbf X$ with columns $\mathbf x_j$. ESL recommends standardizing the inputs before performing ridge regression, which I understand to mean centering the columns $...
Tomo's user avatar
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Logit model not predicting any values < 0 despite class imbalance

I am building a logistic regression model to identify potential channelling factors that predict whether a patient will initiate of one of two antidiabetic drug classes at a specific stage in their ...
jos0909's user avatar
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2 answers
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Regression: negative intercept with only positive values [closed]

I'm doing a multiple linear regression on some standard deviation values, but the intercept and the coefficient for many of the predictors are negative. Any ideas on how I should interpret the results?...
barbara's user avatar
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OLS Coeff importance of variables

I am using and OLS model to determine the importance of independents on the dependant. All variables are scaled. I am currently using the coeff as follows : Independent 1 coeff = 0.04 Independent 2 ...
milo204's user avatar
3 votes
1 answer
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Multiple logistic regression with ordinal predictors

I'm looking for resources on general guidance for how to perform and interpret multiple logistic regression using SPSS, with ordinal predictors. I have 2 ordinal predictor variable. Each ordinal ...
stephan_phd's user avatar
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Logistic Regression - categorical predictors [duplicate]

I'm looking for resources on general guidance for how to perform and interpret multiple logistic regression using SPSS, using categorial predictors. I have 2 categorical predictor variable. Each ...
stephan_phd's user avatar
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15 views

Multiple regression model for devices located in different countries

How can I deal with a regression problem where I have a group of time-series signals (40) and predict a few features, the situation is that the data comes from different devices located around the ...
Yassin's user avatar
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Why are these Partial Residual Plots similar despite having different y-axis (partial residual range vs. component + residual)

I am working to produce some partial residual plots to better communicates the effects associated with each independent variable in a multiple regression model. I am using two methods to plot these ...
Kaliber's user avatar
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Reviewer wants "post-hoc" power analysis for a mixed effect model [closed]

I have a multilevel model in a mixed design: mod <- lmer(score ~ group*time + (1|id), data = df, REML = FALSE) Where score is continuous variable, group is a ...
Imhotep's user avatar
2 votes
1 answer
134 views

Assumption in multiple linear regression

The principles of multiple linear regression are widely described, however there are still some aspects I don't truly understand why. Specifically speaking I don't understand why heteroscedasticity ...
Javier Hernando's user avatar
3 votes
1 answer
99 views

What does it mean to run a power analysis for one variable in a multiple logistic regression?

I'm analysing a published logistic regression with 11 predictors, $\textrm{logit}(Y) = \sum \beta_i X_i$. The study unfortunately only reports which of the $X_i$ are significant predictors, and does ...
Mohan's user avatar
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My model is giving inconsistent results

"I ran a Polynomial multiple regression model on a dataset with just 98 points. when I ran the model on different subsets of data (training and testing). It gives me r2 value ranging from ...
NEERAJ YADAV 's user avatar
1 vote
0 answers
54 views

How to use a multi-linear regression to forecast meaningful values

I have built a multi-linear regression model based two predictors $P_1$ and $P_2$ to predict $Q$: $$ q = A + Bx_1 + Cx_2 + Dx_1^2 + Ex_2^2 + Fx_1*x_2 $$ where $x_1$ and $x_2$ are the ...
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The distribution followed by the number obtained by dividing coefficient a1 by the other coefficients a2 in multiple regression

Given the following multiple regression model. $$ y\sim N(a_0+a_1x_1+a_2x_2,\sigma) $$ where $y$ is the response variable, $N(\mu,\sigma)$ is the normal distribution following the mean as $\mu$ and ...
SekiTake's user avatar
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Variance ratio when including irrelevant variables in a regression model

I am interested to know if there is a general formula for the ratio of the variance of regression coefficient for a predictor in a correctly specified model and a misspecified model. Specifically, let'...
librus's user avatar
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1 vote
1 answer
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What is the best model for this case?

I have the following problem: A data set, which is about the soft drink consumption of people, that covers 300 subjects are available to us. Using Excel tabulations and graphing capabilities only: ...
raffaello.sanzio's user avatar
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6 views

Sequential Approach lower error than Simultaneous approach

I'm looking for a situation/code example and dataset (preferably 2 datasets), where the greedy/sequential approach has a lower cross-validation error than a simultaneous approach (where comparison is ...
matthew George's user avatar
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13 views

How can I measure paid search/SEM effects on sales with MMM while accounting for funnel effects?

I am reading hello fresh approach on building a Direct and Indirect marketing mix model to avoid funnel effects (details in this link : https://engineering.hellofresh.com/bayesian-media-mix-modeling-...
user412233's user avatar
1 vote
1 answer
43 views

Which technique to use for models that include Likert-scale items?

I have three or more independent variables (a Likert scale was used) and GWA as my dependent variable, to see what variables are influencing student compliance with respect to a school's grade ...
Emma's user avatar
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Error less observations than random effects in lmer with time varying covariate

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has ...
Jongjay70's user avatar

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