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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9 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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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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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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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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Is it always a bad practice to make use of p-values for independence for feature selection?

I know that if I want to assess independence between categorical variables (e.g. results of a pharmacological intervention) I can use a Pearson chi-squared test, for example. Such tests will give me ...
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Definition of independent events in probability theory (Wasserman)

In Wasserman's "All of Statistics" p.26 he gives an example of an "independent event" as "flipping a fair coin twice", where the first flip has no effect on the second ...
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What is an appropriate method for comparing strength of predictors between two regressions on different populations?

I have two populations of interest: Population 1 and Population 2. I have conducted the same linear regression model (outcome Y and predictors A B C) on both populations, for a total of two models. ...
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Is it possible to use a dependent variable derived from one regression as an independent variable in a different regression?

For my thesis, I'm researching the effect of employee job satisfaction on customer satisfaction, mediated by service quality, using a company as a case study. I was planning to determine employee ...
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What is the exact formula how the CBPS package calculated the weights?

I am currently working with the CBPS package to estimate propensity scores. However, I am wondering about the weights, the package creates. The balance plot with incorporated weights looks great, but ...
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What is the general trend with Mallow's Cp when adding/ removing variables?

Mallow's Cp is a statistic used to help with model selection within linear regression. It tests all possible subsets of predictors from the true model to find which combinations are optimal. The ...
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Should only confounders (but not covariates) be added to a multiple regression?

Up until now, it was my understanding that multiple regression can be used to estimate the influence of an independent variable (IV) on an outcome (DV) while controlling for the influence of other ...
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GLMM with 1 predictor, still necessary to do Chi-Square with Anova in R e.g.?

I frequently use the Anova function in R to test if any predictors in a GLMM are significant. Is this necessary if I only have one predictor. Can I just use the significance of the predictor instead? ...
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Independent Variable effect's change Between Two Periods

I used the Cox proportional-hazards model, which investigates the relationship between the time of occurrence of an event and a set of explanatory variables in the presence of censored data. The goal ...
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How can I classify time-series given a predictor for each of them?

Say that I have two time-series and a predictor for each of them. I would like to build a classifer that given a window of future (and unseen) samples returns which series is more likely to have ...
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Ho to explore the interaction between dependent variable and covariate?

I'm conducting LMM by adding three dependent variables (A, B, C) and a covariate (E) as the fixed effect, and the random intercept for each subject as the random effect in the model, as shown here ...
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Predicted values well outside of the censored range

I am working with an endogenous independent survey variable, $x$, which has a value range from 0 - 10, where 0 is always and 10 is never. Because the question pertains to wrongdoing, the answer to the ...
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Can predictors with little relationship with target still be useful for a model?

For example, do interaction effects mean that it is still worthwhile to train a model with predictors which do not have much of a relationship with the target?
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How to model binomial data when the dependent variable is LITERALLY dependent on the independent variable

I am designing an experiment where a subject makes a choice between two options ("low value" (LV) and "high value" (HV)). The subject will experience both options during a trial ...
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Poststratified average predictive comparisons?

Does it make sense to do something like Multilevel Regression and Poststratification (MRP) for average predictive comparisons? I have survey data with stratified sampling where minority groups were ...
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Is the assumption of independence between the error terms of a predictive regression with non-stationary or highly persistent regressors unreasonable?

Let us define a predictive regression as follows \begin{equation} y_t=\beta x_{t-1}+\varepsilon_t,\quad\text{for}\quad t=1,\cdots,n \end{equation} where $y_t$ is usually very noisy (and stationary - i....
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Running ANCOVA with multiple covariates

I'm running an analysis to explore what factors predict adherence to an intervention study involving different types of activity (either learning a language, learning computer skills, handicrafts, ...
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Running ANCOVA with categorical covariates - how to set up dataset?

I am analyzing a dataset in which 80 participants rated 4 products (product A, product B, product C, product D) from -3 to 3 depending on how much they liked the product. Each participant provided ...
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Predictors that are integers and factor types in a linear model

I am running a hierarchical linear regression. My predictors consist of both continuous and categorical variables. R treats the continuous variables as integers, and I have converted the categorical ...
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How do I solve the best predictor of X+Y given X-Y?

Suppose that $X$ and $Y$ are independent, each distributed as $$f_U (u) = p^u(1−p)^{(1−u)}$$ with $u = 0,1$; $U = X, Y$. Find the best predictor and the best linear predictor of $X + Y$ given $X − Y$. ...
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Is using a categorical variable with one category representing a specific outcome in the response 100% of the time appropriate for the model?

Not sure of the best way to word the title but this is the scenario: I am running MLR and GLMs to predict a numeric count response. Lets say one of my predictors is binary with two levels, "Yes&...
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Accounting for non-independence of measurements in analysis of variance

I have 10 measurements for each of 50 patients, each obtained at 3 different time points (i.e. 500 + 500 + 500 values). The measurements are of the same quantity but were measured in different ...
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What statistical evidences are needed to prove/disapprove a regression

I will be very honest, This is related to an assignment but i am not asking for the answers. I am just asking for guidance since it is kind of throwing me off. Background The question that i am ...
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Defining my conceptual model

How can I define my conceptual model in terms of independent/dependent and moderating/mediating variables? My research topic is about anthropomorfism and how it can reduce food waste. My research is ...
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Interpretetion of linear predictor of a random variable that follows a gamma distribution

Assuming that: $0 < \nu, \alpha, y < \infty$ $$f_Y(y; \nu, \alpha) = \frac{y^{\nu-1}{\alpha}^{\nu}e^{-y\alpha}}{\Gamma (\nu)} \mathbb{1}_{Y \in (0, \infty)}$$ $$ = \exp \{ -y\alpha + \nu \log \...
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100 views

Multiple regression vs propensity score matching for covariates in observational study [duplicate]

I want to determine if smoking is related to this cancer in an observational study. I have data from 1000 subjects with following variables: ...
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GLM Model confuses Covariates with positive and negative influence

at the moment I am working on my thesis as a biology Student. I observed a number of Insects (dependent variable) at three different Traps(Lures) at six different Locations (=categorial explanatory ...
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1answer
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Adding new variable with no historical data (only incremental new data) to existing regression model

I have trained a linear regression model on several hundred observations. I have recently begun collecting data on a new predictor variable and would like to retrain the model with data from this new ...
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GLM in R: Subset Covariate to specific factor Levels

I like to conduct a GLM in R like this: glm ( Y ~ X + Z + a + b + c + d, quasipoisson(link="log")) with Y as dependent Variable (Count Data, integer) X as ...
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Can binomial logistic regression be used with repeated measure predictors?

I was reading about binomial logistic regression assumptions and a basic requirement seems to be : independence of observations. So I was wondering if predictors could be repeated measures (pre-post ...
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Multiple regression: Is it acceptable to include a categorical covariate with few observations across levels?

I am interested in the influence of age and body mass index (bmi) on brain size in a patient group. I have the following multiple regression model (using R): ...
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1answer
42 views

Principle component regression accounting for age and sex

Should I include covariates (i.e. age and sex) in the principle component regression as predictors? Or do I not need to do that because they were accounted for in the PCA? Any help would be greatly ...
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How to represent the effect of one covariate on regression results?

I was running association analysis using --glm genotypic from: https://www.cog-genomics.org/plink/2.0/assoc with these covariates: sex,age,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,TD,array,HBA1C. The ...
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How do I designate a variable in a linear model to be a covariate in R?

So I want to make this equation for example: y = mu + Strain + Insect + Strain*Insect + BW_final Of all these variables, strain and Insect are controlled variables, but BW_final is an independent ...
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28 views

Which variable is independent variable here?

I am confused. Both can be used as the dependent variables. But which will be the appropriate one? The Director of the School of Business is interested to study if there is a relationship between the ...
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23 views

Correct way (if any!) to apply preprocessing to hold out dataset

After cross validation and grid search the below are the desired pipeline steps and hyper-params for my model. ...
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12 views

If 'test that all slopes are zero' is non-significant, do levels of IVs need to be checked?

I've run a univariate ordinal regression in Minitab and my 'test that all scopes are zero' value is non-significant at my chosen alpha (p=0.141). According to the Minitab guide, this value represents &...
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1answer
42 views

GLS estimator with number of predictors equal to number of observations

Assume linear model $$Y = X \beta + \epsilon \\ \epsilon \sim \mathbb{N}(0, \Omega), $$ where $\Omega$ is a known covariance matrix. The GLS estimator for $\beta$ is well-known: $$\widehat{\beta}_{GLS}...
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Johnson Relative Weights Analysis - Predictor variables

For Johnson Relative Weights analysis, can I have a mix of binary variables and continuous variables as predictor variables? I am doing relative weights analysis on a survey data which has rating ...
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Determine Y using result from a Generalized additive model

I have a question regarding actually using the model created by the gam function from the mgcv package. Once I have tested my ...
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78 views

Which analysis to use for nominal (binary) dependent variable with multiple IVs of various types?

I have 1 dependent variable (DV) measured in binary (Exam, pass/fail), 3 independent variables (IV-1 is continuous (age, in years), IV-2 is binary (Country, Canada vs. US), and IV-3 is nominal, but ...
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18 views

Using baseline categorical response as a predictor

I have a longitudinal data where the categorical response is collected at two-time points. I was wondering if it's possible to adjust my categorical response at baseline as a predictor and run a ...
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118 views

Compare models with AIC when log transforming predictors but keeping the response variable exactly the same

Can I compare say two models where one have predictor P and the other have log(P) all else being equal? I have found posts on the topic but I can only decipher from those that you cannot transform the ...
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What's the difference between endogenous variable and multicollinearity?

Investopedia says that: Multicollinearity is a statistical concept where independent variables in a model are correlated. Also Investopedia says that: An endogenous variable is a variable in a ...
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1answer
351 views

ARIMA model with multiple covariates, XREG

I have shared the main data d2015.txt, includes 5 columns. The first column is the $y_t$ the time series observation. The 2nd to the 4th column is the covariates/regressors and the fifth column is ...
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28 views

Python ML library for multi categorical + discrete predictors for a discrete outcome

I hope this message will find you well. First of all, I'd like to apologize for not using the most commonly used words in statistic's field : I have no background at all in that, and my knowledge is ...

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