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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3 views

Covariate on a MANOVA mixed-effects model

I have a 2(condition) x 3(group) mixed-design study. I’m interested in the effect of a continuous time-constant covariate (i.e., a unique value for each subject, similar to age) on the interaction (...
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conceptual model and mixed anova

I am doing a research about the influence of a food label on Perceived Healthfulness of a product. My independent variable is Label (absent/present) and my dependent variabel is Perceived ...
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Year as a fixed or random effect in GLM with only two levels

I'm using glm to predict species richness from different combinations of environmental variables. Species data were collected at 40 to 125 sites in summer over two ...
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1answer
23 views

Entropy based measure of explained variation

I have an observable $Y$ which is a function of some set of variables $\mathcal{X}=\left\{ X_{n}\right\} _{n=1}^{N}$. Now $Y$ is a deterministic function of the $X_n$s, but conditioned on only a ...
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11 views

Correlation between prediction error and regression dependent variable

I've trained a regression model which predicts the dependent variable from several independent variables. I noticed that there is a strong negative correlation (-0.86) between the dependent variable ...
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33 views

How to adjust for known prognostic clinical or molecular confounders when using survival analysis methods that are not based on Cox regression?

When using penalized Cox or Coxnet regression for survival analysis, it is possible to account and adjust for known prognostic clinical or molecular confounders by including them in your model as ...
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How to interpret deviance from a logistic regression output table

I'm having difficulty interpreting the given output table. My goal is to interpret the deviance for when I include my x-variable in my model versus when I do not. Additionally, I would like to find ...
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Is there any formal guideline that would indicate the necessity for adjustment for baseline when analysing change from baseline?

As in the topic. I saw many critical discussions along with mathematical explanation on why the baseline shouldn't (or mustn't) be employed as a covariate, when analysing the change from baseline. ...
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Lagged (in)dependent variables: 2 time periods

Summary I have a dataset with observations regarding an industrial process in two time periods. My goal is to find predictors of future performance, and I am wondering whether panel data regressions ...
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I'm unsure which of two variables should be my dependent/independent variables?

Let's say I have 100 different samples of child speech. Let's say I want to see if children use more abstract words later on. Let's say I also want to control for the length of the words. (These aren'...
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What to do when difference-in-differences affects covariates

Consider the model $y_{it} = \alpha_i + \beta_{it}did_{it} + \gamma_{it} + \phi_i + \zeta_t + \varepsilon_{it}$ for group $i$ and year $t$. $\phi_i$ refers to group fixed effects and $\zeta_t$ to ...
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Bias of Random Predictor in Linear Regression

Suppose we have a linear regression model with stochastic $X$ such that $$ Y = \beta X + \epsilon,$$ and $X$ and $\epsilon$ follow a bivariate normal distribution with $\mu_x = \mu_{\epsilon}...
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Is an interaction term considered as a covariate?

If I have a model y ~ A*B which is y ~ A+B+AB, is the interaction term AB considered as a ...
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Am I correctly controlling for variables in multiple regression?

I want to run a OLS regression that predicts total violent crime per year using annual violent video game sales as a predictor variable. I also want to control for age, sex and race. I have data on ...
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Semantics: Dominant predictor?

This is more of a semantic question that I am not really sure about. So let's say I have developed a logistic model, and I have done some bootstrapping/cross-validation on it. From the method I have ...
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How can I compare incremental-r2 between groups?

0 I have a dataset (n=6000) of individuals belonging to three different ancestry groups (african (n=2000), native american (n=2000), european (n=2000)). I am studying how well a predictor (x) ...
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How to treat a multi-level continuous variable?

I'm not sure what type of covariate I'm dealing with and how to treat it in a glmm. I have many 1600-ha blocks. Within each block, I'm interested in the variable Burn Severity. There are 4 possible ...
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Predictor for the sum of random variables

I have a random variable Y, which is a sum of N random variables $$Y=\sum_{i=1}^N Y_i$$ Some of the $Y_i$ may be positively or negatively correlated to each other and in fact many of them are. I ...
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How to code a predictor in regression when some values are purposefully unknown

I ran a psychological experiment involving two conditions. An independent variable - made up of numeric values - was present in one condition but not in the other. Accordingly, in one condition the ...
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What statistical method deals with a continous predictor characterized by low variance?

I would like to run a study in which my predictor is a continuous variable. I already know that the distribution is normal but most of the points fall close to the center. I think that small ...
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58 views

How do machine learning models treat nonlinear predictors?

How do machine learning models (including neural networks) respond to the presence of a nonlinear attribute among predictors in a training set?
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Proportion or (adjusted) count variable as regression predictor with varying denominators

I am attempting to run various different regression models for different health outcomes (i.e., ordered logit for self-rated health, Cox models for survival). My predictor variable is quality of care ...
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1answer
49 views

SEM: High SRMR in model with binary predictor. Other fit indices OK [closed]

In this post I may display severe lack of understanding in SEM and the math behind it so please be kind. I am testing the following structural model with lavaan using MLM (due to non-normality) with ...
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1answer
33 views

Including a quadratic effect for an ordinal variable in a regression analysis

It's common for many datasets to have ordinal versions of numerical variables, such as age groups (e.g. "Under 20", "20-30", "30-40", etc.) or time groups (e.g. "Less than 15 minutes", "15-30 minutes",...
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Merging dramatically different data sources

I want to analyze some data that asked the same set of survey items, but across dramatically different samples (all demographics and most other variables are statistically significantly different ...
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How to combine continuous variables for logistic regression: ratio and absolute numbers

I am trying to fit a logistic regression and I have variables which are in terms of ratios and in absolute numbers. How should I treat them? Is it okay to treat them equally, or should the absolute ...
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Determine useful predictors for logistic model [duplicate]

I have dataset at my disposal and I am suppose to run a logistic regression. What is the best way to determine which explanatory variables are useful and which are totally off? Is there any general ...
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Identify Influential predictors

I have a dataset with binary class as outcome. I was exploring the data through plotting the variables for both the classes. For example, something like below ...
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1answer
42 views

Does it make sense to fit an ARIMA model to the remainder component of a timeseries?

Suppose I have a timeseries, something like this: ...
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Cannot understand a notation detail in ESL's Statistical Decision Theory EPE minimization

In The Elements of Statistical Learning, at page 18 the authors explain that, in order to minimize the EPE (Expected Prediction Error defined as the mean of the loss function: $\text{EPE}(f) = \mathbb{...
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Does it make sense to see if success of one thing determines the success of another

I have modeled whether a bird is detected by an antenna (1=yes, 0=no) with the following predictor variables: length of visit, species, and site. Individual ID is a random effect. I am not also ...
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2answers
28 views

Multiple regression with predictors for different “directions”?

I expect predictor A to negatively predict my dependent variable, and predictor B to positively predict the dependent variable. Can I include both predictors in a (linear) multiple regression model ...
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1answer
57 views

Should I compare two models using AIC?

I calculated duration (IV) in seconds using two different ranges (0 to 5s and 0 to 10s). The aim was to find out which range contributes to higher word learning outcomes (dichotomous DV). I ...
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Which statistical method do I need for my research (combining correlation and independent two-samples t-test?) [closed]

I need some guidance with my model, because I believe I need both correlation as well as an independent two samples t-test for my research.. is that even a thing? My model claims that firm ...
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What would happen to a partial dependence plot if the assumption of independence was violated?

I understand the largest issue with PDPs is that it's assumed that the feature/s of the plot that are calculated are not correlated with other features. Let's use something like height and weight to ...
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How to quantify a parent's influence of a node in a Bayesian Network?

Consider a toy bayesian network that models purchase of items at a store. The nodes include: {Brand, Price, Purchased}. It is possible that when you marginalize over price, P(purchase|brand) may ...
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34 views

best predictor and best linear predictor

Suppose that $X$ and $Y$ are independent, each distributed as $\mathcal{B}(1, p)$, if I define $Z=X+Y$ and $W=X-Y$ how can I calculate the best predictor and the best linear predictor of $Z$ given $W$,...
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How to calculate the Bayes Estimator (Predictor) for the following problem?

Given the error function: $$ e(y,\hat{y}) = \begin{cases} 2,& \text{if } y<\hat{y}-1\\ 0,& \text{if } |y-y|\leq 1\\ 1,& \text{if } y>\hat{y}+1\\ \end{cases} $$ I need ...
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34 views

Regression by python of mixed types of variables

I have a data set contains mixed types of variables numerical variables, binary categorical {Yes,No} and categorical (very high,high, medium,small,..). It is required to apply the following scikit ...
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effects of a covariate on fixed effects interactions in a mixed models (in lme)

We measured brain responses to two stimuli (2 lvls factor condition) from two brain regions (2 lvls factor hemisphere) and measured across 4 years (4 lvls factor time). We focus on development of ...
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2answers
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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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136 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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23 views

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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78 views

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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1answer
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
57 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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215 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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18 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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