Questions tagged [multiple-regression]
Regression that includes two or more non-constant independent variables.
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Multiple regression or mixed-model to account for altitude in model
Let's say I'd like to test the concentration of a particular mineral (conc) in different parts (part) of two different mountains ...
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Moving 1 SD along one variable in multiple linear regression vs simple linear regression
In simple linear regression done via the usual least squares method, it can be easily shown that the estimated line passes through the point of means, $(\bar{x},\bar{y})$ and moving one sample ...
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Should a non significant predictor be included in a multiple linear regression model? [duplicate]
Given the multiple linear regression model: $$Weight = \beta_0 + \beta_1\cdot Height+\beta_2\cdot Age$$
If we get that $Height$ is significant (p<0.05) but $Age$ is not (p>0.05), should we ...
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Statistical models with values in non-freely generated R-modules
Setup
My general understanding of most statistical models is something like: a type of model for $n$ variables is a vector space $V$ and a function
$$E: \mathcal{P}(\mathbb{R}^n) \times V \to \mathbb{...
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Multiple regression correlated predictors
I am trying to fit a multiple regression model with several predictors. Here you can find a reproducible example.
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Assumptions of Linear Regression (homoscedasticity and normality of residuals)
I am confused about some assumptions of linear regression: homoscedasticity and residuals are normally distributed. These two require residuals, but to get the residuals, we need to fit the model ...
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Linear Regression with point data and continuous data [closed]
Question - What type of regression or other statistical technique would be used for continuous data linked with point data?
Data - My data is for product that goes through continuous processing lines ...
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Estimating correlated features in OLS regression
I am given decently large sample set of $y_{i,t}$’s and ${\beta}_{i,j,t}$’s from following rolling (in time $t$) OLS regression fitted by someone else and I want to estimate timeseries $x_{j,t}$ :
$y_{...
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Model reduction in linear regression by stepwise elimination of predictors with "non-significant" coefficients
Before we start: yes, I am aware that stepwise model reduction suffers from many drawbacks and it is advisable to use regularisation methods such as LASSO instead. However, I found a procedure that ...
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Sequential sum of squares with svd
I am studying some methods to determine the coefficients of a linear regression and I am wondering how to find the sequential sum of squares, or the second column of the ANOVA table which shows ...
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Data correlation with effect of nominal variables
I have a dataset with 4 relevant column with the following information:
the main independent variable (it is a nominal variable, it can take discrete values but hypothetically an infinite number of ...
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Predictor variables vs control variables for power analyses in multiple regression G*Power
I am using GPower to get an idea of the sample size needed to detect a medium effect size in the second and third steps of a hierarchical multiple regression model. To do this, GPower asks for the ...
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Added variable plot and CCPR plot for categorical variable
Do the added variable plot and the CCPR plot make sense for categorical variables? The significance of the variable can be obtained from the partial F-test, and non-linearity only applies to ...
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What is in-sample vs out-of-sample in a multiple linear regression?
I was just thinking about what would be considered interpolation vs extrapolation for multiple linear regression, and realised I'm not sure exactly how it would be defined, nor could I find an answer ...
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Deriving MSE($\hat{\beta}$) under Linear regression
I was able to derive the MSE, but there's a part of the derivation which I don't really get. Here's what I got:
Facts:
$\mathbb{E}(\hat{\beta})=\hat{\beta}\space$ (unbiased estimator)
$\text{Cov}(\...
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Checking linearity assumption in regression with quadratic terms
I have a few questions about checking the assumptions for linear regression: What is the best way to check linearity? Many recommendations I saw said to check scatterplots, but since linearity refers ...
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Does ceiling effect of outcome variable violate linearity assumption of linear regression
If there is a ceiling effect in the outcome variable, e.g. in my case the outcome variable is limited to a certain value and 25% of data points have that highest possible value, does this mean that ...
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How to visualise the value of one predictor in a multiple linear regression
I'm looking for confirmation on whether the approach I have is statistically correct / straightforward, and if there might be any references supporting this line of thinking on how to visualise ...
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What is the best architecture for multi-target text regression?
I'm building an AI model using Google's 'Civil-Comments' dataset. It has 7 different labels, each a float than can be anywhere from 0 to 1. Embedding Bags, which I have read about. do not perform well....
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What I have to do more to improve my regression model in r [closed]
I want to make beverage sales predicting model. I am doing regression analysis. All the column types are integer. The dimensions of the data are 15375 rows x 400 columns. The dependent variable $y$ is ...
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Standardized regression coefficient
I was wondering the following: Does the standardized coefficient on X1 in a regression of Y on X1, X2, ...,XN go to one, as the bivariate correlation of Y and X1 goes to one. If so, why?
Or is it ...
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Residual plot has pattern when all predictors included, less of a pattern when only weak predictor included
I saw something unexpected in the residual plots of my regression analysis: when I plot the residuals vs. the fitted values for the full model with multiple predictors (R squared = 0.24), I get a plot ...
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Selection of confounding variables in multiple linear regression with AIC, BIC, or both? [duplicate]
I am using multiple linear regression to control for confounding variables. I have analyzed my data using Bayesian Information Criterion. Is there an advantage to also using Akaike Information ...
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zero values in dependent variable , correct structure of the data , zero-inflated model
I want to understand the relationship between macroeconomic variables and the fundraising volume for specific funds (secondary funds).
I got the following dataset (assume a table:
1.
Fundname:
Abbott ...
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Why estimates of data via residuals has 50/50 effect on significance compared to original data?
I am generating data according to the following model
In the real dataset, variable I is unknown and the goal is to study the relationship between I and D, B11, to see if it is significantly non-zero....
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Assumptions of linear regression, when its results are input for a ranking based algorithm
I ran a linear expression with gene expression being the explained variable. Two characteristics of the cell in which it is expressed (the data is single-cell data) are predictors. There are about 300 ...
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Residual index / partial covariance interpretation in linear regression
(START EDIT) to address EdM's comment for clarification. I do not have a specific relationship I want to measure or study. Instead, a paper I am reading already used the regression of (residuals of ...
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Is regression on aggregated continuous independent variable adequate?
I´m trying to analyze some cohorts that reported aggregate data (mean, SD, and n) of a physiological parameter for the outcome of interest, which is a nominal variable but fairly lineal with the ...
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Adding interaction to regression: main effect AND interaction non-significant
I've used two models on my dataset:
Model 1: clinical score ~ X + Z: both X and Z are significant.
Model 2: clinical score ~ X + Z + X*Z: X is not significant anymore, and neither is the interaction, ...
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How to standardise confidence intervals for a regression with parallel mediation
I've run a regression with parallel mediation on SPSS using Hayes Process Macro, and it calculated the standardised beta for me - but I've just realised that the 95% confidence intervals are all ...
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The link between SLOPE and the Benjamini-Hochberg procedure
Suppose we are performing $m$ tests which generates $m$ p-values $p_1 \leq \ldots \leq p_m$ (with the indexes ordered). The BH procedure is as follows:
For a given $\alpha$ (the desired FDR level), ...
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Logistic Regression on German Credit data from UC Irvine
I am trying to learn the logistic regression and encountered the German credit data set (https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data). My query is - how to formulate regression ...
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Finding the true "causative" covariate - in joint versus separate modeling
This is regarding some genetic assignment.
Assume we have two random covariates (SNPs) $X1,X2$, and a random response $Y$ (disease). I believe that only one of $X1,X2$ is “causative” for $Y$ ,
but do ...
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How does SYSTEMFIT from R work (from statistical perspective)?
In R, there is a package for estimating systems of equations with OLS, called systemfit. Initially, this package does not seem to add that much of a value, as I can ...
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How to apply Bonferroni correction method in step-wise selection process?
Let me provide the question in an easily understandable example for clarity.
I have 4 phenotypes (response variables) and hence 4 different models. Each model is passed through a step-wise selection ...
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Use of multiple comparison correction and composite DV
I am investigating the effect of a single X on a composite DV, Y. I want to also investigate the effect of X on the 36 sub-components of Y. Thus, I would have to run 37 multiple regression models, ...
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Is there any value in models that have a larger out of sample RMSE than a standard deviation?
I am predicting y values from x values using various regression models, elastic net and partial least squares regression (PLSR). To quantify performance of models we utilize root mean squared error (...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 = \...
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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 ...
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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) ...
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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 ...
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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;...
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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 ...
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