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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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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Combined reliability for statistically independent tests/measurements
I'm perplexed by this statement in Wikipedia article, 'Consilience':
"Statistically, if three different tests are each 90% reliable when they give a positive result, a positive result from all ...
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How to prove this relation for Kendall's distribution function (or Kendall's measure)
Kendall Distribution Function (Nelsen, 2006, p. 163) Or Kendall Measure (Salvadori et al., 2007, p. 148) Or Kendall Function (Joe, 2014, pp. 419–422) is the cumulative distribution function (CDF) of ...
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How does conditional logit deal with binary predictors in choice sets of three or more alternatives?
I am interested in clogit, not multinomial logit, and the overall effect of the predictor on the choice of n alternatives that can only be identified by their attributes (i.e., the choice sets are ...
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A causal path problem of prediction models
I am doing a study on predicting the 10-year risk of diabetes in people without diabetes using factors such as glucose, HbA1c, and triglycerides. The blood sampling time is not limited to fasting. In ...
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impact of time to particular event on response
I am trying to evaluate which variables have the impact on the outcome. My clinical team wants to check if time to particular event has an impact on the outcome. If particular event occured, then time ...
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What distribution should I use to predict three possible outcomes
I am 70, left school at 14 but took to maths a few years back to ward off dementia so please excuse the naivety of my question.
I have been using Poisson distribution to solve my problem but I dont ...
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Is there any difference between an "independent association" and to be "independent predictor"?
I wish to know if there is a difference between being an independent predictor of a variable or an specific event, and to be independently associated with i.e. hormones are independent predictors of ...
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Difference between an ANCOVA and an ANOVA on residuals that the effects of covariates are already removed
My psychological experiment has a repeated-measures design. Each participant performed 4 conditions (Factor A [2 levels] X Factor B [2 levels]). Let say the DV is the accuracy of the participants and ...
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Same p-value of overall model and binary predictor level 2 versus Intercept (lm function in R)
I have a response variable PC1 (it is PCA scores for a bunch of observations). I have a response variable category with two ...
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Why would independences tests show significant correlation between variables while VIF shows no multicollinearity?
When conducting independence tests among the variables, some exhibit significant correlations, yet the VIF analysis indicates no multicollinearity. Is this common, and what implications does it hold ...
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Can Multiple Regression Output (coefficients, ratios, etc.) Be Given a Predictive Interpretation?
Generally speaking, regression output can be given a causal interpretation for typically one variable in the model (that is under the assumption of no unobserved confounding and this is not to speak ...
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Synthetic Control - difference in data regarding the frequency
I am trying to make a small impact evaluation, using the synthetic control method. The outcome variable is a monthly time series, whereas a potential predictor variable is only available on the yearly ...
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Can you infer that non-significant variables in full model won't be chosen by stepwise regression methods?
I recently encountered this question twice, on my exam. If you fit a full MLR additive, model, can you infer that the insignificant predictors (p-value > 0.05 from lm output) will not be chosen ...
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Position in hierarchy as ordinal predictor in linear regression
I'm writing my thesis and I'm unsure about the analysis procedure. One of the hypotheses is: "A higher position of the deceased in the attachment hierarchy of the bereaved predicts a higher level ...
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PSPP multiple variable linear regression analysis
I'm just starting with linear regression, and I'm having trouble understanding it. It doesn't seem to make any sense to me. Yes, this is school work, but instead of asking for direct answers, I need ...
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How to convert pairwise dissimilarity matrix into continues predictor in R
I have collected plant species data and AGB from 25 different sites and I want to do regression analysis of Beta-diversity (i.e. dissimilarity among 25 sites) as a predictor and AGB as response ...
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Reporting results for time-invariant predictors in latent growth curve models
I am results for reporting a time invariant predictor of the intercept and slope factors within a latent growth model, adjusted for covariates such as age and sex. My dependent variables and ...
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Regression with depended predictors, animal study sample size [closed]
I have a few questions for help,
One of my predictors is for treatment and control. Another predictor is dosage. Treatment group has 4 dose levels which control group only has one. Can I still use ...
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Impact of correlation method using mice::quickpred()?
I use Multiple Imputation with MICE in R for dealing with missings in my survey data. I have two huge question marks in my head at the moment:
As I have quite a lot of predictors (actually all ...
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Logistic regression with highly left-skewed data for the independent variable
I am using the in-built GLM function in R to identify the model that best predict frogs' occupancy based on survey data. One of the independent variable (saturation) is highly skewed, as 36 of the 57 ...
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What's the difference between Mediators, Co-Variates, Moderators and Confounders terms?
I was wondering if someone could shed some light on the difference between the above-mentioned terms since I see them used frequently in many research publications I've read.
If I have an outcome ...
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Testing Exogeneity of regressors
Good evening,
I have a problem with solving this exercise:
I could calculate the F-Statistics with the weak instrument test on my own. But I don't know how to test for exogeneity with two endogenous ...
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Count data and proportion covariates: best practices
I'm working with spatial data and I have the following log-linear model for count data. Let $y \sim Poisson(\lambda_{i})$ such that
$$
\log \lambda_{i} = \text{x}_i^\top\beta_{} + \epsilon_{i}
$$
such ...
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Do I have to use the exact same variables in each step, if I have a two-step propensity score match followed by a regression?
I am using the propensity score match to grow my sample based on a smaller dataset of existing units that received the treatment. The match will find more likely units from its large population that ...
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Prediction Machines Word Problem (Conditional Probability?)
I am new to statistics but had what I think is a pretty simple question:
Prediction machine 1 correctly guesses the outcome of binary (yes/no) events 60.4% of the time.
Prediction machine 2 correctly ...
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Question regarding control variables in a serial mediation model
i encountered questions regarding running a serial mediation model in mplus. my IV is ERS(excessive reasssurance seeking) DV is depression (DEP), my proposed two mediators are INU (internet use) and ...
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What is the number of "explanatory variables" in the adjusted R2 formula
I am trying to assess the goodness of fit of a surface I've developed (i.e., model predicts a variable y based on 2 variables x1 and x2). My model has 5 parameters which are estimated (and is likely ...
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Can a Dependent Sample T-Test be Used on Sample Group (A) of 100 Electrical Devices, Tested at time, t1, shuffled, and Tested at time, t1?
Description:
I have a group of 100 electrical parts being testing for Forward Voltage, at time, t_1. This is my sample group, S1.
This same group is undergoing a stress test that may or may not affect ...
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When should one control for covariates?
Suppose that one wants to estimate the effect of X on Y in the following causal diagram
Should one take Z as a covariate (and why/why not?)
For example, suppose that one wants to estimate the effect ...
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Logistic regression sample size calculation and 10:1 rule of thumb
I need to calculate the sample size for a proposed logistic regression. For the dependent variable, outcome is coded as disease or no disease. The major predictor is a continuous variable. There are ...
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How to mathematically prove the "transitive property of nested predictors"?
QUESTION
I am studying the structure of experiment data sets, and I want to propose a rule that I call the "transitive property of nested predictors".
The general idea is that…
if there are ...
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What references assert that the effect size of a nonsignificant covariate may be high?
An answer to a FAQ notes:
The effect size of a covariate may be high, even if it is not significant.
Does anyone know of papers I can reference to support that assertion?
FWIW, I do understand why ...
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Can logistic regression predictor variables be nominal?
Can logistic regression predictor variables be nominal (like the charlson comorbidity index which ranges from 0-6)?
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Interpreting Regression coefficients where Y is an index
I am having trouble with the correct interpretation of my regression coefficients. My independent variable is fund allocation which is a continuous variable. I have two output variables on which I ...
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With logistic regression, how does one choose a number of predictors when preregistering a study?
Harrell's Regression Modelling Strategies suggests that the number of predictors should not exceed $m/10$, $m/15$ or $m/20$.* For logistic regression $m$ is $\textrm{min}(n_1, n_2)$, where $n_1$ and $...
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exact match on covariates or include in a model?
The scenario is I have a binary outcome, a treatment group variable (3 groups) and 3 covariates. It is possible to exact match all 3 covariates on 1:N from group 1:2 and group 1:3. Would you perform ...
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Does adding a random intercept for subject address confounding variables within subjects (e.g. sex or age)?
Let's say I am interested in identifying associations between a blood protein and disease activity, but I have multiple measurements per subject. Based on a literature review, I expect sex differences ...
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Estimate one regression coefficient for different variables
I would like to know whether it is possible to compute one identical coefficient for three different variables in a regression. So instead of estimating $\beta_1$, $\beta_2$, $\beta_3$ here:
$$
Y=\...
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Target variable is defined by combination of input features
I am trying to create a classification model which predicts whether or not a customer comes back to make a second transaction (after having made an initial transaction). I have details on date of ...
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Is it possible to apply a Kruskal-Wallis to data without clear dependent/independent variables? [closed]
I'm trying to find a NHST for my data that, as far as I know, are only compatible with a Kruskal-Wallis test. However, my variables aren't really identifiable as either dependent or independent; ...
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Effect of two independent variables on a dependent variable, each containing several factors in Likert scale form
For example, the first independent variable consists of 30 factors, the second independent variable consists of 21 factors, and the dependent variable consists of 21 factors. There are four options ...
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Interpretation of ANCOVA output
I am struggling to interpret my ANCOVA output - I have looked at similar questions and can't figure it out.
I am looking to determine whether there are gender differences in the impact of a specified ...
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Choice of Variables for Linear Regression
Say, I have a dependent variable $Y$ that I want to model with a linear regression and independent variables $X_i$.
I am assuming that two of my independent variables $X_1$ and $X_2$ have an effect on ...
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Modeling considerations when data spans different events (time) and exhibit a (relatively) low mean and high variance
I have weekday data ($n = 1551$) from the past 5 years (2019-2023) with attendance at a large restaurant. I am just getting started, and for each weekday I calculated the mean and the variance as per (...
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Experimental design study on arousal/attention
I hope this question is simple enough, suits this forum and does not consume much of your time. Essentially, want to make sure I have the appropriate design that answers my research question without ...
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Interpertation of a conditional quadratic latent growth curve model (i.e., with predictors)
I have a conditional quadratic latent growth curve model and am wondering how to interpret the results.
My predictor of interest is significantly associated with the slope factor (B = -0.45, p = .001) ...
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How to multivariate regressors work?
while I seem to understand that there is a general matrix-based formula that allows us to solve for multivariate regressors, when looking at the non-matrix solution for a bi-variate $\beta$ I realised ...
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Structural Equation Model design
Is it necessary in structural equation modeling (SEM) to incorporate all potential independent variables that could affect the dependent variable? Or is it acceptable to examine the influence of only ...
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Can cross-validation be involved in model-building rather than validation?
I have a general idea in mind that would go like this:
randomly split the data into training/testing
build a model on the training data by choosing from among candidate predictors
evaluate it on the ...
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