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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25 views

Multicollinearity and categorical predictor with three levels

If I have a continuous DV and two IV, where one is categorical with three levels and the other is continuous, what assumptions do I need to check for multiple regression? Scatter plots are for ...
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2answers
69 views

Can I safely use variable importance of a random forest in a paper?

Background: I just started with machine learning and I'm considering using it on old data based on which I'm writing a paper. The paper deals with radiation-induced lung damage and the data comprise ...
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1answer
11 views

Finding the significance of certain questions in a survey

My company performs a Go/No-Go questionnaire to determine whether or not to go after a particular opportunity. In this questionnaire is a series of 14 yes/no questions. We have accumulated a ...
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1answer
82 views

Change in order of predictors breaks logistic model estimation (glm, R)

I am fitting a binomial logistic regression in R using glm. By chance, I have found out that if I change the order of my predictor variables, glm fails to estimate the model. The message I get is ...
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1answer
16 views

Generalized linear model - independent variables with many zeros

I am carrying out glms on count data, several of my variables consist of largely of zero values, i was previously told to exclude these variables as it would reduce the model fit. I can't find a ...
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1answer
23 views

Interpreting Coefficients of a Dummy variables derived from an Ordinal variable

I have a variable that is measure societal complexity (SC) on a 3 point scale. 1 being the least complex and 3 being the most complex, and I think that this can safely be classed as a ordinal ...
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1answer
30 views

Can I add more cases and/or predictors to existing set of data?

If I have a set of data with 40 cases and 3 predictors can I add more cases and/or predictors later to the existing set of cases if I want to explore the effect of more possible predictors? The data ...
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0answers
23 views

Logistic regression and IV that depends on another IV value

I am modeling the effect of aspects of house change and marital status change on a (binomial) DV. Each observation in my data is a 3-year period in someone's life. Thus, for family change, I have a ...
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1answer
39 views

Is testing predictors separately theoretically sound?

I am running a regression analysis to understand the effect of several IVs on the transport mode choice of questionnaire respondents. My sample of respondents is of 100, and I have more than 10 ...
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2answers
118 views

Predictor variables sum up to 1 but not necessarily correlated - is it a problem? [closed]

I am trying to fit hierarchical mixture model (using ML and MCMC, but this shouldn't matter) where the linear predictor part contains 17 independent variables. These are habitat variables: for each ...
2
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1answer
80 views

Is log transformation a proper way to reduce the weight of high vs. low values in logistic regression, and how do I diagnose when the DV is binary?

Consider the following case: I am analyzing a the effect of (among other variables) the age of a firm on a specific binary event. Theoretically my perception is that age matters, but not linearly. ...
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57 views

Multple linear regression, adding one predictor with almost perfect fit make others irrelevant

I found something interesting while playing with some data and linear regression. I built a regression with various predictors, more or less correlated with the outcome. Then I added one predictor ...
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20 views

Variability within Predictor Variable and Random Forest Over-Fitting

I have a predictor variable that has low variability within it (small range of values) and it is rated very high importance within my Random Forest Model, will this cause over-fitting of my model? I ...
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0answers
70 views

Linear regression with log dependent variable

I have the following regression: $log(Y) = \alpha + \beta X + \epsilon$ with $E[\epsilon] = 0$ and $var(\epsilon) = \sigma^2$. There is no assumption on the distribution of the errors $\epsilon$. In ...
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1answer
58 views

graphical representation of fixed effects from lmer

I have run a lmer model in R: ...
1
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1answer
67 views

Assume (x,y) are drawn from independent & identical distribution when y=f(x)

Sometimes we say the following: $X$ is some training data given by $X:=\{(x_1,y_1),...,(x_l,y_l)\}\subset R^d \text{x}R$. Assume that the training data had been drawn from independent and identical ...
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1answer
116 views

Prediction with categorical variables in Cox regression

I'm doing survival analysis with Cox PH. I have my final model based on averaged models and I have four categorical variables with multiple levels each. I computed the fitted values using ...
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48 views

logistic regression with sparse predictor variables

I am currently modeling some data using a binary logistic regression. The dependent variable has a good number of positive cases and negative cases - it is not sparse. I also have a large training set ...
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8 views

Coefficients flip sign in general linear model depending on what predictors are included: collinearity is NOT a problem [duplicate]

I have a general linear model with several predictors (~10). The sign (beta) of one of the predictors (Pred1) is negative when all predictors are included. It's STILL negative when the most correlated ...
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19 views

How do I best possible model future risks for Organization?

Using following concept is it possible to define or layout future risk or in security terms future root-causes that are critical for organization operations and businesses. Those concepts are:- 4 ...
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0answers
24 views

Dealing with covariate*predictor interactions

I have one DV, four IV and 4 covariates. The assumptions to do the traditional ANCOVA are not met, therefore I am including the interactions predictor*covariate (1 to 4) in my model. My covariates are ...
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0answers
63 views

Using log of dependent variable as regressor

I am running a regime switching (hidden) Markov model, and I found out that if I construct the following model, it gives very interesting and useful state switches: $ y = \alpha_{S_t} + \beta_{S_t}\ ...
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0answers
7 views

given a set of pairs of graphs, build a model that accept a graph and predicts it matching graph

Training Data I have a set of pairs of normally distributed graphs, each with a concrete last sample (maximal X) Question I want to build a model (formula) from the data input: a single graph ...
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1answer
206 views

CHAID decision tree - Binning continuous variables

I am running a CHAID classification tree on SPSS to classify my data set. I have a couple independent variables including categorical and continuous ones. For continuous variables, I've noticed that ...
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14 views

How to model data

I am attempting to model a specific variable's sensitivity to a feature set. In concrete terms, I am trying to predict the duration (PAUSE_KS) of a letter (...
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1answer
82 views

combining/merging correlated variables

I performed a correlation analysis on my IVs to see which are related. As this is data from an experiment, I also have variables that are in general not so easy to capture from people in real life ...
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39 views

How to apply transformations to the predictors of a GLM?

This post discusses why we need to transform $Y$ before estimating the predictors exponents in order to reduce the problem to a linear fit. The example builds on $Y$ log-normal. In the case of a GLM, ...
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7 views

Dependent predictors with converse effects on the target

I am trying to create a predictive model for marketing in the natural gas field. The model is supposed to guess how probable it is to make a contract in that particular building given many internal ...
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1answer
100 views

Product price prediction - include important external factors

I need some hint over what is the general prediction solution to modelling products prices in such a case: I have several models (types) of the product I want to predict prices for each of these ...
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3answers
586 views

How to Use Neural Networks to Forecast Time Series Data with Predictor Variables?

I have browsed a lot of topics here, but the ones I see were all about forecasting a single variable, depending on its historical values. Whereas I want to predict a variable, by estimating a ...
2
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1answer
58 views

Drop highly correlated items?

I have two IVs that are highly correlated with each other at 0.979 (Pearson) & 0.919 (Kendall's). IV1: Quality of response IV2: Quality of Technical Advice Sample Size: 252 Considering the ...
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1answer
29 views

What can I do to if I have a nominal dependent variable and scale independent variables?

Our dependent variable is a nominal variable that asks respondents "would you share this video?" with the responses being either yes or no. The hypotheses we are testing are all relational directional ...
2
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0answers
87 views

What is predictor offset in linear regression?

I've been reading the post about removing intercept that boosts $R^2$ (HERE). An answer to that post said that: In essence, that means our predictor had better have a strong mean offset itself ...
3
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1answer
316 views

Predicting the Weather

Given a tree trunk with concentric circles, can we predict the weather for each year? Each concentric circle accounts for a year that the tree has been on the Earth. The innermost circle is the oldest ...
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1answer
38 views

Clustering Two Variables With Disease Information

I was proposed a problem and I am not quite sure how to go about it. The problem is I want to find a relationship between two variables. For the simplified case there are only two variables, lets say ...
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161 views

Baseline predictors parametres

I've implemented baseline predictors model. It trains on data: "user_number item_number rating_ui" And then I need to predict raiting for "...
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0answers
59 views

Leave-one-out cross validation in selecting predictor

I am a newbie here. There are 155 total samples. Five different predictors Xi (i=1,2...5) are used to predict Y, like X1 X2 X3 X4 X5 Y .... The objective is to find the best predictor Xi to ...
2
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0answers
60 views

Calculating confidence intervals when binary input variable equals zero

I am building a multivariate regression model and trying to use the model to predict a High, Medium, and Low estimated outcome for each individual in a group of people. I am using the 95% confidence ...
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0answers
45 views

Running time dependent covariate model in R, getting warning

I am trying to fit this model, but it's giving a warning message I don't understand: ...
2
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1answer
121 views

Choosing variable transformations in non-linear relationships

I am confused about how to apply a transformation to my predictor/response variables to test curvilinear relationships. I read about log transformations, polynomials, quadratic functions. But I am not ...
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3answers
73 views

Can we use ordinal or multilevel predictors directly into logistic regression?

Can we use ordinal/multilevel predictors directly into binary logistic regression model? I guess not. we usually here convert them to multiple predictors to have values 1/0 for each category. Also ...
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0answers
28 views

Effective ways to display confusion matrices from different predictors in an academic publication?

I want to display the results of two different predictors' performance on a dataset. I have a confusion matrix for each of the predictors' results on the test cases. I want to present these confusion ...
1
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1answer
69 views

Are explanatory variables considered random in PCA?

One of properties of PCA states that the sum of the variances of the principal components is equal to the sum of the variances of the explanatory variables. I wonder how to interpret this as I've ...
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0answers
20 views

Logistic regression understanding variable nature

I have 2 categorically dependent variables(both binomial) in logistic regression which individually both give positive estimates against the response(binomial). However if modeled together one give ...
0
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2answers
47 views

Significance of independent variables in linear regression models

I am trying to make some sense out of the results of a linear regression model. I have a dependent variable X, and, say, 3 independent variables Y1 Y2 Y3. I set up 5 models : (m1) X ~ Y1 (m2) X ~ Y1 ...
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1answer
80 views

Can anyone suggest me articles where they have used multivariate logistic regression models and explored in detail about the role of each predicor?

I am doing logistic regression analysis using multiple predictors for a binary outcome.I had about 10 predictors and tried to find the best model using 'glmulti' package in R. I have got a significant ...
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51 views

How to calculate prevalence ratios?

In a reviewer request from a submitted manuscript, they requested that I use prevalence ratios instead of odds ratios. The reviewer says: Using logistic regression when prevalence of outcome is ...
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3answers
106 views

How to select predictors in regression using backward method?

When is it appropriate to use a backward method in regression? I have read that it is permitted for exploratory model-building, but I have also read negative things about it. I am making a model ...
2
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0answers
29 views

Computing the likelihood for multiplicative error in the independent variable

A problem has recently arisen for me which involves estimating relative strength of various mechanisms contributing to an overall quantity. These strength parameters $q_j$, $j\in[M]$ stays fixed ...
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1answer
98 views

Individual level prediction of a person’s probability of voting without their vote history

Is it possible to create individual level predictions of a voter's probability of voting when you do not know their vote history? In the data set provided in my homework assignment, I am given data on ...