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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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p-value of Covariate differs between Regression and ANCOVA

I'm using SPSS to run a regression analysis in order to predict navigational performance (continuous dependent variable) from self-assessment scores (continuous predictor), sex and age group (both ...
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How to model change in independent variable for Cox Proportional Hazards Model?

I'm interested in calculating a time to event model (Cox Proportional Hazards Model) and one of my independent variables has multiple measurements. I'm planning on using a time varying covariate for ...
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1answer
37 views

How to control for age in analyzing longitudinal data using mixed effects regression

I am analyzing a longitudinal dataset. Elderly subjects perform a cognitive test once a year, for five consecutive years. I want to know if there is a decline in their performance through the study ...
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1answer
17 views

Multiple nominal categories predicting a continuous variable

This is a simplified version of the problem that I'm having, but I'm really looking for the appropriate type of analysis for the question in which I'm interested. Set up: I have a large data set in ...
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8 views

Censored Dummy Regressor

I have a dataset that contains factors corresponding income ranges of sampled persons, like people with factor 1 earn between 10,000 to 20,000, 2 between 20,000 to 30,000 . I could just make dummies ...
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15 views

Adding covariates to a log-ratio transformation

Let us say I am interested in predicting the evolution of market shares over time with a model that includes not only past lags of the variable, but also some covariates that are non-compositional, as ...
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14 views

Model comparison for predictors with differently shaped distributions

I want to use model comparison to evaluate the claim "Predictor A outperforms Predictor B in modeling Dependent X". The (potential) problem is that within my sample, Predictor A and Predictor B have ...
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60 views

Proving that $ (\hat{\beta} - \beta)' (X' X) (\hat{\beta} - \beta)$ is independent with SSE

Exercise: Prove that $ \mathbf{(\hat{\beta} - \beta)' (X' X) (\hat{\beta} - \beta)}$ and SSE are independent for a Least Squares Regression Model. Attempt: Note that by $'$ I denote the transpose ...
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34 views

Regression problem with constant predictor

I have a regression problem in which inputs are generated uniformly in [0,1] and output is of the form y=ax+e, with e being the error or noise. The predictor is built to get a constant value estimated ...
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1answer
11 views

Are dichotomous predictor variables appropriate in a quantile regression analysis?

I want to model the changes in a dependent variable with non-normal distribution (e.g. abundance of micro-organisms) as a function of changes in dichotomous independent variables (e.g. gender, sick or ...
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1answer
30 views

Autocorrelation in a predictor variable

Suppose that my main purpose is to model (using GLM e.g.) an annual count data by using two predictors one of which is mean annual water level measurement which, in itself, is auto-correlated (i.e. ...
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Does reversing a covariate change the within-subject effect in a repeated measures ANOVA? Why?

I noticed I coded a variable in the wrong direction, so I reversed it. I thought this reverse would not matter for drawing inferences from the ANOVA analyses (e.g., p-values or effect sizes) as there ...
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Is ANCOVA suitable/scientifically sound for my case?

I have a question regarding the suitability of co-variate. I have three groups with different levels of production in pcs (low, medium, high), material waste in % and number of machine setup changes. ...
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Zero-inflated highly skewed predictor variables

I've thoroughly searched this website and multiple others and can't seem to find an answer to my question. This is also my first post so I hope I've followed all the rules. I apologise for the length, ...
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Linear Unconditional X-Y, Non-Linear Conditional X-Y

Intuitively, I can imagine that an unconditional (i.e., unadjusted for any covariates) Y~X relation can present as a linear relation, whereas a conditional Y~X|Z relation can present as a non-linear ...
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Bayesian Statistical Conclusions: We Implicitly Condition On the Known Values of Any Covariates, $x$?

My Bayesian data analysis textbook says the following: Bayesian statistical conclusions about a parameter $\theta$, or unobserved data $\tilde{y}$, are made in terms of probability statements. ...
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Balancing independent variable and model performance(using SMOTE)

I have a data set which contains 7 independent variables and one dependent variable. I tried applying some of the classification algorithms to predict the binary target variable. I got about 96% ...
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1answer
28 views

Independent variable derived from the dependent variable in regression modeling

We wish to model ratings (1-9) based on several predictors. We hypothesize that the effect of some predictors may vary across the ratings. That is, some predictors might distinguish higher ratings (e....
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P value for main effect of independent variable is changing when moderator is changed

I am trying to run a model 1using PROCESS within SPSS. I am running models with the same independent and dependent variables, and covariates, and changing only the moderator. Each time I change the ...
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Covariate adjustment of non-normal traits for subsequent two-sample analysis

My dataset is composed of two populations, and I have numerous continuous traits that I’d like to compare between the two populations. All the traits are non-normal. The covariates for each of these ...
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27 views

Hierarchical multiple regression and importance of individual predictors across multiple analyses

I'm currently reading an empirical study using regression and want to determine whether to consider it a trustworthy, well-analysed piece of literature. I'm more familiar with ANOVA models, so I was ...
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20 views

Different significance in GLMM interactions

I am confused about different significance results obtained in GLMM's. First, my set of relevant variables: ...
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1answer
93 views

Binary predictor with highly skewed distribution

I am running a linear regression model and I have a binary predictor that has a highly skewed distribution. For example, one category represents 96% of the data. In terms of frequency, the other 4% ...
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Is it possible to “tune” a trained model in one population so it can be used for a different population (i.e., by swapping variables)?

Say, I have trained a model to classify patients into cardiovascular disease (CVD) and non-CVD. The model building process is as follows: There is a gold standard to compare the model with. The ...
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19 views

Show outcomes decline over time [modeling stage] [closed]

I am conducting a medical work implying to [Show that after Establishment of the procedure, Outcome (post-procedure Risk factors) decline over time]. I am from math Background, so i Interpret as ...
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Get the variables that most seperate two groups letters

Problem Description I have a dataset, for which it's quite complex to explain what the data contextually means. However, I hope that I can explain the goal with some fictitious data. Let's say we ...
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Including predictors with HLM

I have data from healthy controls and patients that I plan to use in a multi-level model. I am comparing how mindfulness improves performance on cognitive tasks in healthy controls and patients. ...
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1answer
28 views

Coordinates as model features

My goal is to predict the taxi demand depending on location and time in NYC. Hence, among other variables my dataset contains coordinates. My question is, can I use them as a predictor for my models? ...
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1answer
41 views

Checking for multicollinearity in selection of variables for regression model

For the selection of variables for a regression model, I did a pairwise correlation matrix between the different predictors and the response variable. From the pairwise correlation matrix, I realise ...
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1answer
21 views

Interpretation of main effects in mixed regression with covariate

I am trying to analyse the effects of 3 different psychological interventions on my outcome (imagery vividness) on 3 timepoints in a linear mixed regression model in SPSS. I also want to add a ...
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56 views

Adding Input Variables

My goal is to predict taxi demand in NYC depending on location and time. I have a dataset with ~18 million observations. With that said, I could add a large number of predictors. But when would I ...
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27 views

Interpretation of residuals in linear regression

Assume I have the following regression model: $Y=\beta_0 + \beta_1*X_1 + \beta_2*X_2 + \beta_3*X_3 + \epsilon$ If I run the same model without introducing the variable $X_3$, can I interpret the new ...
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1answer
114 views

What model to use with repeated measures of count data with a factor and a covariate?

My design involves three dependent variables. They are count data: frequencies of certain words used in a 20-min conversation. They're also repeated measure: the same subjects engaged in three 20-min ...
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28 views

GWAS: how do we know we have the most appropriate model?

[Note: crossposted on Biostars here] My question is about how to know when the optimal statistical model has been selected for a GWAS (genome wide association study). I appreciate that statistical ...
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1answer
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How to compare two groups on one continuous variable, in function of one ordinal variable

I've got two groups (independent variable), one continuous dependent variable, and one 4-level ordinal variable (socioeconomic status). I would like to compare the two groups but (within each group) ...
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1answer
39 views

How to chose covariates to adjust for in a regression analysis

I am running a regression analysis where my primary interest is to see if the outcome differs by the group (treatment vs. control). However, I have some 80 other clinical and socio-demographic ...
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How to interpret the output from an all-subsets regression?

I have a dataset in which I'm looking to explore the relationships between nine input variables (X1 - X9) and five outputs (Y1 - Y5). For some of the outputs I hypothesise that certain inputs will be ...
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1answer
54 views

How to check if a polynomial regression has any predictive value? [closed]

After fitting the polynomial data to a given curve, how can I check which of the many curves has the most predictive value ?
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25 views

Adjust correlation for categorical variable

In setting up a model that predicts a continuous outcome variable based on a continuous input variable, I was wondering whether I could adjust a (partial) correlation for a categorical variable (e.g., ...
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40 views

GLMM for non-normal nested data (SPSS)

I have a dataset which is quite complicated to me. The data is nested; multiple measures within the same person. Therefore no ANOVA, but GLM/GEE. However, my independent variable is not normally ...
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what does it mean when A and B are non-significant but the interaction of the two is significant?

I carried out hierarchical regression model using stepwise method and included interaction terms (predictor variable x moderator variable) in the third step of the model. It was found that only ...
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1answer
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continuous independent variable with three levels

I have a model with a variable named "Delay" as a predictor (measured in seconds). It sounds reasonable to treat it as a continuous variable. However, I have two thoughts regarding this: I sampled ...
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1answer
27 views

Are scatter plots between independent and dependent variables reliable ways of refuting linear relationships?

Suppose that we want to predict $y$ using a subset of the variables $x_1 \dots x_n$ using linear regression. Suppose I regress $y$ on $x_1$, from which I obtain fit $f_1$, regress $y$ on $x_2$, from ...
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In Regression Analysis, why do we call independent variables “independent”?

I mean some of those variables are strongly correlated among themselves. How / why / in what context do we define them as independent variables?
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29 views

Markov model using MSM Package in 'R' for Loan Delinquency

I want to build a model that can help predict the loan delinquency status. There are 6 "states" for delinquency, viz. 0 (current), 1 (30-59 DPD), 2 (60-89 DPD) and so on. The dataset has various other ...
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Time-series: Using decomposed components (seasonality+trend) as covariates in multiple regression?

I need to evaluate the effects of a treatment on a time-series data-set by comparing multiple dependent variables to a prior time period without treatment in effect. I'm using a python library to ...
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2answers
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Why do coefficients in a binary logistic regression model differ according to the number of predictor variables?

I fit a binary logistic regression model with a single categorical variable, for which I received a coefficient. When I added further categorical predictor variables, the coefficient of the original ...
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28 views

Adding covariates to a binomial model [glmer in R]

What's the best way to add covariates to a binomial model? Should I just add any potential covariates as fixed effects in the glmer model? For instance, in my model it would look something like this: ...
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Adding a covariate for the variable dose of a drug

I have a study where subjects (mice) are sleep deprived. Some receive caffeine in their water and others do not and comparisons of their memory are tested over several days. Caffeine consumption, thus,...
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Proper Description of a Hypothesis for Moderation

I read a paper where a person was hypothesizing to see moderation. A was predicting B. They said that for participants at HIGH levels of C, the relationship between A and B would be stronger. ...