Questions tagged [predictor]

Refers to the variables used in a model to predict a response. This tag can also be used for $X$ variables in explanatory & descriptive modeling, not just predictive modeling. This same construct goes by many names in different contexts, including: independent variable, explanatory variable, regressor variable, covariate, etc. This tag can be used for any of these synonymous terms.

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21 views

Question on Optimal predictors for the 0-1 loss function

The input $X \in \{0, 1\}$ and label $T \in \{0,1\}$ are binary random variables, and the set of predictors that we consider are the functions $y : \{0, 1\} \rightarrow \{0, 1\}$. Recall the $0$-$1$ ...
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linear regression/ANOVA with a covariate measured on a different set of objects

CV has helped me many times but it's my first question as I am struggling to find the right type of analysis for my problem. I have a continuous response variable measured in several groups and my aim ...
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33 views

OLS Regression equation [closed]

This question is primarily for my understanding; say we have a regression equation of the form Y = Xb, where X is a matrix of a few explanatory variables. If you are told that the vector 'b' does not ...
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17 views

Test subset data with extra variable against predicted value from top model built using full dataset

I am trying to test if a response variable is predicted by spatial/temporal variables + genetics, but I only have genetics data for 25% of the data. Can I test if genetics play a role in the following ...
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1answer
62 views

How to detect differences between two repeated measures for not-normally distributed variables, adjusting for a continuous covariate?

I'll explain briefly the experimental set-up. One gruop of subject measured in two conditions: A: with treatment (Instrumented gait analysis with foot orthosis) B: without treatment (Barefoot ...
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Is the logistic regression with quadratic and interractions terms (special case) similar to QDA? And what about the prediction performance?

Beyond the fact that the the two methods have different assumptions : Logistic on the residuals extrem value distribution & utility theory. QDA on the predictors multivariate gaussian ...
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1answer
18 views

Including pre-treatment covariates in difference-in-differences: what if my covariates determine treatment but are outcomes of treatment?

I have the following question: What should I do with covariates that affect treatment in the pre-treatment period but are affected by treatment once the treatment is in place in a difference-in-...
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Why test.modmed (mediation package - moderated mediation) does not find covariate?

I am trying to do a moderated mediation analysis in R with the mediation package, test.modmed function. In summary, my dataset consists in: Y = cell_size (continuous); X = body_size (continuous); ...
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1answer
39 views

Effect of getting closer to a normally distributed covariate on result of ANCOVA

I have a question about ANCOVA. We did an experiment with 2 groups and then calculated an ANCOVA to see if the DV differed between the groups. We included one binary and one continous covariate (PO). ...
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7 views

Multi-level Mixed-effect Modeling of Growth Trajectories: Grand mean-centering or Transformation to Normal for Dependent Variable?

When it comes with analyzing growth trajectories by multi-level mixed-effect modeling, What would be technically appropriate when the dependent variable originally shows a severe positive skewness ...
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How to implement the OLS regression model on my data

I want to find out if economic sanctions effect the educations system of sanctioned countries. There are some studies that have worked already on providing evidence that sanctions negatively effect ...
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Determining independent vs dependent variables for multiple regression models

I am trying to create a multiple regression model in Python that takes hours slept, minutes of exercise, and my average daily mood to fit a 3D surface of $1^{st}$ (plane) to $5^{th}$ order polynomials....
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Are SHAP values potentially misleading when predictors are highly correlated?

Are SHAP (SHapley Additive exPlanations) values potentially misleading when predictors are highly correlated? How and why? If so, is there any guidance on when not to use SHAP? Are there any rules ...
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Leave out non-significant covariate [duplicate]

In my study, I make use of two covariates. After running a two-way ANCOVA in SPSS, it turns out that only one of the two covariates is significant. Should I remove the one that is not significant, and ...
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Ensemble of boolean predictors

Lets say we have a 0/1 predictor, that is right in p % of cases. If we ensemble N of those uncorrelated predictors, what is (an elegant solution for) their accuracy? (The ensemble's answer is the most ...
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34 views

Adding external factors to a time series model

I've been working on time series for a while now and using Hierarchical time series forecasting, Croston TSB methods for demand forecasting. I want to add external factors which affect the forecasting ...
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13 views

Multiple explanatory variables in Hierarchical Model

I want to use a MLM with two levels and was planning to include multiple explanatory variables on both levels. After studying various textbooks, I am no longer sure if this is a good idea. Does ...
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1answer
25 views

If we say "Survival difference adjusted for covariates" do we mean just additive adjustment, or allow also the interactions?

Let's make it independent from any statistical package, so I will provide just numbers for the illustration. Maybe this give you some clues. I want to compare two survival curves, for group A and B. ...
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How autocorrelation work based on the data plot?

Let's suppose we have a time series is a=[1,1,1,3,3,3,1,1,1,3,3,3] as then the autocorrelation figure for this time series is The lag here is 4, for lag = 1, the autocorrelation is between [1,1,1,3,...
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43 views

Logistic regression model

I have a question about binary logistic regression. I have been trying see which IV are the independent predictors of an outcome of a categorical variable ​in a sample of 680 cases, where 30% of cases ...
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1answer
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Multiple Linear Regression Assumptions

I have a question regarding the assumptions. I have a categorical predictor alongside three continuous predictors (moderators) and my dependent variable is continuous. I would like to understand how ...
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1answer
32 views

Simple Linear Regression Anova statistics

I am running a regression test for which I overall have two variables (x and y) and I would like to test if any of them is dependent on the other. ...
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1answer
38 views

Which covariates need to be adjusted for in a model

I am building a time to event model. I have many variables, but I would prefer a simple, but correct model, so I have drawn a DAG with daggity, to decide what variables to adjust for. My exposure is a ...
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Finding the Best Linear Predictor

Really easy question here! Given the following model: $X_t = \epsilon_t + 2\epsilon_{t-1}$ I want to try calculate/find $Pred(X_t | X_{t-1})$ Here's what i've done so far Find $X_{t-1} = \epsilon_{t-1}...
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1answer
28 views

Modelling independent ordinal variable

When we have nominal independent variables in Regression we model them by using binary dummies with levels equal to the levels of the nominal variable minus 1. I prefer the base to be zeros and not ...
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30 views

Lagg/lead variables

Please, I have a simple question related to the variables time series. I am using panel fixed effect regression to see the impact of instrument issuance on firm performance. My independent variable of ...
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10 views

Adjusting for continuous covariate in two-way ANOVA

I hope this question is not too basic, but as I am not a statistician I would really appreciate some input. Basically, I have the following problem: I have a continuous dependent variable (FA). All of ...
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8 views

Time dependent regression on time series

Let's say I have a time series of product demand on a monthly basis, from January 2018 to December 2020. This product demand number is a summation of demands based on 10 different locations and 2 ...
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3answers
835 views

Given a predictor that explains 10% of the variance in an outcome, how accurate can my prediction be for a person with a known score on the predictor?

Sorry if this is really elementary stuff, but I'm a stats noob trying to wrap my head around this. Intuitively, I'm imagining that a model with this information alone would be able to accurately ...
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1answer
16 views

How to model a count-data predictor in a linear regression model

I have the following data: Predictor X: positive count data (range 0 to 43), in this case the number of symptoms present out of a total of 43. Outcome Y: binary I want to test, whether (or to what ...
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2answers
39 views

How to interpret categorical covariates for Cox regression

I am using Cox regression to model the deterioration of bridges using covariates such as salt (tons/mile), average daily traffic (ADT), average daily truck traffic (ADTT), span length, snow day per ...
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Non-significant regression model (MLR) with significant predictor

I'm having trouble with interpreting some results I've found. In order to test my hypotheses I've conducted four analyses. My hypotheses were: X1 positively predicts Y1 and Y2 X2 does not predict Y1 ...
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23 views

Update an existing ordinary linear regression with new data and another covariate

I am studying a case on a topic that was studied a while ago, related to food technology. To simplify the question, let's say that there are old OLS models that relate this property ($Y$) in certain ...
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50 views

If I want to adjust survival analysis for a covariate, like age, should I add it "smth+age" or add an interaction with it "smth*age"?

I have a survival analysis with a categorical predictor called "smth". I want to adjust it for age. I don't have any idea if they can interact or not but I guess they can. Now, about the the ...
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1answer
117 views

Am I correct in my understanding adjustment for covariate vs. stratyfing by it in the Cox regression?

I'm trying to understand the difference in a Cox model between adding a single categorical covariate like sex = {male, female} and doing stratification by it. I'm not saying about such trivial thing ...
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1answer
113 views

LASSO/Ridge regression with adjustment for a covariate

I'm trying to address the following analysis problem in high-dimensional biological data. The setup is bulk gene expression data where multiple cell types (tumor and immune cells) can contribute to ...
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23 views

Event-based markov chain with observed covariates at non-constant time intervals

I am working on a project where I need to identify the traffic state at intersections. More specifically, I want to classify the situation in 5 states: undersaturation (where the traffic needs to stop ...
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14 views

3-way mixed ANOVA with a covariate - how does SPSS adjust for the covariate?

I'm trying to run a 3-way mixed ANOVA (3x5x2, where the 2 is the between-factor) in SPSS. My dependent variable is accuracy, and thus cannot exceed 1. However, when I enter the covariate measures as ...
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30 views

Is the train/test split necessary for parametric models like logistic regression?

In order to have an estimate of the prediction error in a machine learning model, I am used to split my dataset into a training set and a test set. I will train my model on the training set and ...
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11 views

How can I investigate the effect of covariates in different data frames?

I have two data frames that measure the same sizes of ears of each person, one of the data frame contain measures from the bad ear and de other from the good ear. The Ear variable is a dummy and the ...
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21 views

Why do we care about removing correlated predictors in predictive modeling?

Many basic methods in predictive modeling/statistical learning are good at picking one of the correlated predictors while removing others, such as Lasso. However, isn't it the case that in general, ...
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80 views

In multivariate analysis using PERMANOVA and NMDS, is it appropriate to include both predictors in my model?

I am an undergrad student new to much of these statistical tests. I am using NMDS ordination and Permanova testing to analyze variation in cover type (using count data) at different distance classes (...
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32 views

"Double gradient" design for ecotoxicology of particulate substances

We are designing an investigation on effects of microplastic particles (MP) on experimental soil in growth chambers. Question: Is there a significant effect? And if so, what is the form of the ...
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33 views

Random vs deterministic predictors in regression

I am reading Elements of Statistical Learning (ESL) and trying to have more of a grasp of machine learning techniques. I am a little bit confused about when to treat predictors as fixed, and when to ...
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1answer
43 views

Variable controlling in linear regression and covariates

I am analyzing cardiac data and have interests in cardiac problems and exercise. I just want to focus on the exercise effect and found AGE is significant variable. I'd like to control AGE variable ...
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36 views

Unscaling predictor variables from a GLMM using predictorEffect()

I'm running GLMMS on scaled and centered data, which has worked well. However, now I am trying to visualize my data using the Effects package and I cannot find a way to backscale my predictor ...
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3answers
53 views

Robust regression for heavy-tailed random design

As far as I know, there are robust regression methods for outliers in response $Y$ and heavy-tailed error $\epsilon$. The settings for the design matrix (predictor) $X$ is either fixed design or sub-...
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305 views

Why is the explanatory variable non-stochastic or fixed in repeated samples?

I am studying econometrics. I have been learning about deriving the variance for the OLS slope statistic in a simple linear regression model. Why is the explanatory variable considered to be non-...
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1answer
36 views

Validation on independent biological dataset

I am working in the space of cancer statistics. I am looking for reasons why it is important to validate statistical observations in an independent biological dataset. Does anyone have a list or a ...

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