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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, ...

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Comparing importance of predictors among different groups

The research is about understanding the importance of 3 factors on personal ratings for some restaurants. There are 3 independent variables(Food Quality, Services, Environment) and 1 dependent ...
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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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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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27 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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17 views

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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18 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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7 views

study design for inerventional sudy

I want to conduct a research between two type of surgical intervention and I want to compare means of preoperative and post operative heamoglobin level between the two group
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1answer
86 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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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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11 views

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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1answer
25 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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36 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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54 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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1answer
32 views

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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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
45 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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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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41 views

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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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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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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19 views

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. ...
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22 views

Parameter estimators of linear predictors

Suppose a linear predictor of the form $a + b'X$. To find estimators for a and b, should we minimize $E[Y-a-b'X]^2$ or $E[(Y-a-b'X)^2|X]$. Former gives $\hat{a} = E[Y] - b'E[X]$ and latter gives $\...
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25 views

Dealing with independent variables that are distinct based on levels of categorical variable.

Marketing campaign effectiveness example. I am trying to model App installs as a function of the following variables Click through rate Ad Spend - CPM (Display) and CPC (Paid Search) Media Type -...
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Dependent variable is dependent from the outcome of other dependent variables

I have a problem with formulating my problem, so I cannot search for a solution. Here is my issue: I would like to analyze the impact of the user behaviour of cars on the error rate of its components....
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57 views

Incorporate a lag effect into a linear mixed regression model

My data: I have 80 years of repeated measures data (forest growth rates) that unfortunately were not sampled regularly. My understanding is that this precludes me from using time series analyses (at ...
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1answer
57 views

Multiple values in a variable for linear regression

Say I have a data set that I am trying to perform a linear least squares regression on. Suppose that the end goal is to predict y from x. The training data set I am working with has the form ...
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Accounting for non-linear trend in SOME samples of a linear mixed model using quadratic

I have the following data: Pine Forest Biomass ~ Age | Plot: Each black curve represents whole-plot biomass for each individual plot I want to formally examine ...
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40 views

Non-linear relationship among independent variables in linear model

Assume that we have a linear model with 4 independent variables, and 2 of them have a strong non-linear relationship (between them). How this fact could affect my model ($R^2$, or implications on the ...
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1answer
339 views

How to interpret regression coefficient if predictor itself is on a negative scale?

I'm looking at effects of tree mortality (using "Biomass loss") on forest growth patterns. I incorporate loss into a mixed effects model like so (using lmer in R): ...
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44 views

Is it valid to add a regression predictor that shares a raw variable with a calculated response?

I want to know how forest growth rate has changed through time. $Growth Rate = \frac{(Biomass_t - Biomass_{t-1}) + Biomass.lost_t}{Year_t - Year_{t-1}}$ where $t$ is the time period of interest. I'...
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Is a binary probit model which predicts the outcome of every observation to be true still useful for analysing effects of explanatory variables?

So I have a binary probit model for labour participation restricted to a specific age group and region. The remaining explanatory variables include sex, education, and having dependent children (all ...
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Find predictor variables for categorical variables in a dataset with few correlations

I have a dataset with numeric and categorical variables. I have 3 dependend ordinal scaled variables, which I want to predict. Each one has between 4 to 5 levels. I need to find 3 to 10 predictor ...
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25 views

Variable types or which analysis to use

I am not sure on which analysis type I should use for the following experimental design. Participants are shown 20 items, and after each item display, they have to rate each on a 0 or 1 scale. This ...
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What can you infer about the effects of explanatory variables in a binary probit model?

Obviously in linear regression, the coefficient tells you whether the effect of a change in an explanatory variable on the response variable is positive or negative and how much a change of one unit ...
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A proper function for nonlinear regression with 3 predictors

There are three independent variables in my experimental work, namely flow rate (0.5 ≤ Q ≤ 9 where ΔQ = 0.5), particle size (a = {6, 10, 15}), and a geometric parameter (AR = {AR1, AR2, AR3, AR4}). ...
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Binary predictor associated with high and low values of a dependent variable

What approach should I use to incorporate a binary predictor variable in a regression model in the following situation? When the binary predictor takes the value 1 then the dependent variable tends ...
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55 views

Do I need to satisfy the assumptions of multiple linear regression to use the predict() function?

I recently used multiple linear regression to model monthly species abundance (y) and environmental variables (x) 2005-2016. To ensure assumptions were satisfied for multiple linear regression I had ...
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Right-censored predictor where censorship means “never”

My major predictor is "time to consultation" (of the specialist), which ranges from 0 to roughly 72 hours. There are also patients in this group whose value is "never". That is the procedure in ...
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Binary logistic regression (SPSS) only predictis one outcome

I'm running a binary logistic regression from 5 predictor variables. The outcome variable is match/no match. Although I've run this for four different groups of data with varying distributions on the ...
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How to create users preferences vectors from clickstreams, presuming the existence of multiple distributions of the preferences

Let's imagine we do have products which are conceptually described by 40 features, with scores between 0 and 1. Let's imagine the first feature is 'color'. 0 would be white, 1 would be black, just for ...
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44 views

Including/excluding predictors directly related to the response (linear models in R)

I want to estimate the average speed of cars in a race by using a linear model with the average speed as a response and decide which of the other data do I use as my predictors. I have data on; ...
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244 views

Change score analysis vs multiple regression

I have a pre-treatment (T1) and post-treatment (T2) DV measure, and 4 IVs. I am interested in how (and which) IVs predict change in DV from pre- to post-treatment, or predict post-treatment outcome (I'...
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33 views

Compare the relative effects of an independent variable on two different dependent variables?

This question is mainly theoretical, but based on problems I have experienced when previously designing research. I want to know whether there is an approved statistical method to compare the effect ...
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23 views

use ratio as dependent variable in glmm

I have a dataset I am working with to model the proportion of fish caught at length conditioned on the total catch at length. I have paired observations (n=96). The paired observations consist of a ...
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89 views

gam predict with categorical and smooth terms

I have a model: m9<-gam(Indicator~ShellDamage+s(AirT,SH,bs="ts")+s(SH,bs="cr")+s(AirT,bs="cr")+s(DeckDur,bs="cr"),data=df33.1,family="binomial") ShellDamage ...
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Feature selection before or after correlation analysis?

At first let me say that I'm just an average guy and I'm kind of hitting the walls of my ignorance here! :) I have two large sets of data, each measuring the same variable in the same system but ...