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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45 views

Can a missing independent variable from a regression model produce a flawed analysis?

I am doing a regression analysis on the various factors which influence accident levels in my city. The 2 factors used in my regression model are covariates : i) ...
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What is an example to show Wald's test does not confirm significance at the same level for each variable?

Suppose that the true model is given by Y=0.3+X1+X2+X3. Assume we have 100 training examples where each covariate vector (X1, X2, X3) is randomly drawn from some distribution P and Y is generated ...
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160 views

Conditional Expectation and Prediction

This is taken from the book "A First Course in Probability" from Sheldon Ross: Sometimes a situation arises in which the value of a random variable $X$ is observed and then, on the basis of the ...
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Deciding upon the important regressors for a SARIMA model

I have 14 months(01/07/2018 to 30/08/2019) of one minute data, which I have aggregated to 10 mins block. So I have a data of dimension "61056 * 350". From this I am using 12 months of data to train ...
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How many covariate are too many? [duplicate]

I have a sample size of 167. In addition to DV, IV, and a moderator variable, I would like to use 6 variables. Is it too many? How do you determine acceptable numbers of covariates? Also, fewer ...
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41 views

Method(s) to predict binary outcome from onset of variation in time series data

Context: I have data on 100 patients showing their time of attendance at a service. They attend on a roughly daily basis between 9-5 (except weekends and occasional missed appointments). They access a ...
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1answer
69 views

ANCOVA baseline as covariate

just a simple question, to make sure this is correct. I watched some videos with similar study questions and I'm following them. I have 4 groups of treatments(Ct+A1+A2+A3), and two different moments, ...
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258 views

Understanding predictor importance in clustering with SPSS

SPSS offers a certain metric to assess predictor or variable importance in clustering. How it is calculated has already been answered in the following thread: "How is Relative Variable Importance ...
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What are good public datasets for time series analysis with “certified” (by papers in the literature) good predictors of the target variable? [closed]

I have to test different models for time series forecasting and predictors (exogenous covariates) goodness evaluation and I would like to use datasets used in relevant scientific publications that ...
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21 views

Regression variables not used/applicable for certain cases [duplicate]

I have a rank-ordered logit model in which the various independent variables are pertinent for some cases but not others. For example, cases 1, 2, 3, & 4 might have variables A & B that are ...
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133 views

Can adjusted r-squared (or r-squared) be used to compare the strength of independent variables in linear multiple regression?

Relative newbie with quantitative analysis here, so forgive me if the question is naive or ill-specified. I have argued in a manuscript that along with using Beta values in linear multiple regression ...
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time series data modeling for deep learning

what is the best format to feed the input data, which are time series with varying density over time, to a deep learning network, while at any iteration we want to feed a batch of data including a ...
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State-of-the-art ways of selecting variables for predictive models

I'd like to know about reputably reliable methods of selecting variables for predictive models based on large multivariate data sets and how they guard against spurious results (something analogous to ...
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Interactions between categorical and continuous independent variables

Im going to investigate if a disease have a negative impact on the development of children. The disease is the independent variable with additionally 10 confounders. Do I have to check for ...
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Is there a way to assess how much and in which direction a predictor is associated with class in my classifier?

I searched the forum, and couldn't find a matching question. I am building an MLP to predict an outcome (occurrence of a medical condition) in Weka. Previously I identified positive and negative ...
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How to identify important categories in categorical variable to predict some binary variable?

Imagine that we have two sets of data. The first one contains information about websites visited by certain users: ...
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1answer
95 views

binomial correlation?

I am interested in testing for a correlation between two variables, both of which are binomial. I guess this is equivalent to a Model II regression where both variables are binomial. Ideally I want ...
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Controlling for a variable: t-test or covariate?

Lets say I have some data that looks like this: ...
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1answer
159 views

Regression response and explanatory variables

I am a newbie to regression, and I was trying to answer the following question using this data. Is there a meaningful difference between the distribution of damage caused by hurricanes with ...
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29 views

Am I doing the right model? lm or lmer?

I am looking at the effect of land cover (tree species, grass, woodland) on soil carbon at 3 depths. I have site as a random factor and biomass a covariate. I ran a ranova which revealed there was no ...
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R: Modelling estimates for the factors and 'sub-factors' of a predictor variable within a GLM

To take a simple example, let's say the model contains a dichotomous predictor variable with the factors {Group X, Group Y}, where all observations can be categorized into one of the two groups. ...
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Prediction of change in response behavior in survey

I am working with a longitudinal survey with responses from three separate occasions, so far. It follows individuals in childhood and adulthood. One of the questions asked is: "Has your child ...
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1answer
76 views

How to interpret mixed effects logistic regression of 2 categorical predictors?

if A is the Group reference level & Noun is the Class reference level, is this summary telling us GroupC is significantly different from GroupA at only the level of nouns(intercept)? Or is it an ...
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1answer
544 views

a covariate versus a random effect [duplicate]

I have looked around on cross validated as well as other places but can't seem to find an answer. I'm running a generalized linear mixed-effects model. Y~initial abundance + Treatment + (1|Month) ...
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988 views

What do clinicians mean by “independent predictor”?

I am a biostatistician and often clinicians use the term "independent predictor". For me, it is not clear what exactly is meant by this, and my strategy has so far been to not use the term myself... ...
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38 views

How to interpret the results of multiple regression when two dummy coded predictor variables are depended?

I am would like to get the estimated influence of two conditions (variable A and variable B). When I dummy-coded variable A and B and carried out a multiple regression analysis, both of them were ...
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53 views

What do these SAS plots imply about the regression outcomes?

I have run a Tobit regression, and in the output the following charts are generated (at the end of parameter estimates). I cannot relate these charts with the parameter estimates. Could someone give ...
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What is covariate?

I'm confused with this term: covariate. What is it? Is is just the observed outcomes of some random variables that contain information that could help us enhance our prediction of another random ...
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Is the accuracy equal if the true positive and false positive rates are equal between two groups?

I was reading the paper "Equality of Opportunity in Supervised Learning" (link). In that paper there is a feature $A \in \{0,1\}$ and a binary outcome $y \in \{0,1\}$. The population is divided into ...
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strong and independent extra explanatory variable doesn't improve linear regression

So I already have a linear regression on 3 predictors $Y = X_1 + X_2 + X_3$. Now I have an extra predictor $X_4$. Before I put in $X_4$, the original predictor using $\hat{Y} = X_1 + X_2 + X_3$ has ...
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Use of a predictor variable for a dependent variable that is directly related to the predictor variable

If I would like to predict a binary variable x, and x is true if y is true and ...
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1answer
13 views

Can i use an independent variable in % units in a probit regression

I want to do a probit regression and one explanatory variable is given in % units. Do i have to transform it in decimal units or can i use it in % units in my probit regression?
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1answer
42 views

Censoring linearly splined predictor in regression

I'm developing a logistic regression where one of the independent variables has a non-linear relationship to the probability of the event occurring. I have created linear splines based on this ...
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1answer
31 views

How to determine which independent variables to add in a multivariable regression model when sample size is small

As a post-doc I am working on some data that I did not collect myself. The central question I'm trying to answer: is there a difference in the fat mass of neonates born to mothers with gestational ...
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63 views

controlling confounding variables vs. excluding confounding variables

I'm working on a meta-analysis project that looks at the effect of "pure" depression (i.e., depression with no anxiety) on mortality. For studies that looked at the effect of pure depression on ...
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57 views

Regression output scale

I am using XGBoost (gradient boosting) to predict the value of a continuous dependent variable The figure below shows a histogram of both the dependent variable data and the predicted data. (blue is ...
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1answer
15 views

Do you need to correct for a confounding factor if two groups are matched?

I'm looking for group differences in a variable (Power in a frequency band) that depends on age (i.e. increases linearly with age). If the two groups are matched for age, do I still need to include ...
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1answer
39 views

How to check covariate is balanced?

I try to check if the covariate is balanced by computing the absolute pooled standardized difference, but I don't know how I could get the sample variance for my treated and control covariate? any ...
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17 views

Contribution of a predictor in Nonparametric regression

Is there an equivalent to a beta weights in a nonparametric regression? I am using the NP package in R and running a local linear regression where my bandwidth estimates are produced using least ...
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1answer
38 views

What are criteria to select the variables to include in a multiple Cox regression model

I have a group over about 500 subjects and want to perform a cox-regression to find predictors of an event taking into account the time to event. I have 12 potential predicting variables that I want ...
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2answers
496 views

How to calculate Mean adjusted by Covariate?

I need to calculate the mean of a variable, adjusted by another variable. Both variables are ratio scaled. I found this online: https://ideas.repec.org/c/boc/bocode/s344803.html which does what I want,...
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42 views

Interaction not significant anymore in ANCOVA, but in ANOVA

I have a significant interaction $A\times B$ in my regular mixed ANOVA, but when I add a covariate, the effect $A\times B$ is no longer significant, but the interaction effect with the covariate $A\...
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1answer
644 views

Why covariates are used in Latent class analysis (LCA)

I am trying to learn this relatively new statistical technique LCA. In literature i saw that for some LCA models, covariates are used. I am having a difficulty to understand why additional covariates ...
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1answer
26 views

How to show that the error variance of the best linear predictor is inferior to the proportional predictor?

Let's consider the 1D case. How do we prove that the error variance of the Best Linear Predictor (BLP) is inferior than the Proportional Predictor (i.e. the Linear Predictor without the intercept)? ...
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1answer
148 views

Dealing up with collinearity predictors' choice in xreg using auto.arima

I'm trying to do a regression with arima errors in R, with xreg in auto.arima following https://otexts.com/fpp2/ by https://robjhyndman.com/ but I have some questions about the predictors' choice in ...
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1answer
42 views

accounting for age effects: two age groups and within-group age differences

I am analyzing data from a study with two groups of about 25 healthy participants each, one young (mean age ~25 [~18-~30] years) and one older (mean age 59 [45-75] years). The study was set up mainly ...
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1answer
25 views

Difficulty working out which statistical test to perform [closed]

I am writing a research paper looking at how reported PTSD scores vary in those who have lost a loved one to homicide. I am investigating whether there is a difference in the scores at different ...
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1answer
105 views

R auto.arima with transformed covariates

I have a non-stationary output time-series (oil prices) that is to be forecasted with 20 different input time series. The series are all non-stationary. I am considering two approaches. Approach 1) ...
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1answer
124 views

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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1answer
467 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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