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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Multiple regression summary for different predictor classes

One question on regression: Following model: M1 <- lm(y ~ x1 + x2 + x3) x1 and x2 are in ratio scale, x3 is a nominal variable, they have values as x1= 1.4, 1.3, 1.2,... x2= 2.1, 2.2,2.3,.... ...
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19 views

Combining Collinear Variables

I have a set of 10 variables: 9 explanatory, 1 response. I wish to do a constrained regression on the variables and use the values of the coefficients as weights in a TOPSIS analysis. I am having ...
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1answer
26 views

What impact does squaring a dependent variable have in a model?

I am performing some regression here to study the association between the length of time an auditor has been auditing a company and the choice of auditor. In this case, my DV is auditor tenure ...
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1answer
23 views

Justification for variable reduction by removing predictors with near zero variance

I have a large number of variables that I'm trying to reduce, and I've stumbled on Kuhn's (2008) suggestion that I eliminate variables with zero or near-zero variance. This makes sense to me, it's ...
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35 views

Choosing predictor variables (IVs) for inclusion in multiple regression

I am stuck on something and think I may have made a big error. My DV is ticket sales and I have 5 potential IVs: ticket price, income, review score, travel distance, and performance costs. I am ...
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1answer
60 views

Getting negative predicted values after linear regression

I'm using linear regression to predict a price which is obviously positive. I have only one feature which is gross_area. I standardized it (z-score) I got this kind of value: ...
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57 views

Opposite of $p$-value for null hypothesis?

Disclaimer: I have no statistical background. So please excuse, and correct me please, if I make several amateur mistakes below. I have two groups (let's call them $A$ and $B$) and a particular ...
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27 views

Recommendation system and baseline predictors

I'm participating in programming contest, where I have a data, and where the first number is a user, second number is a movie, and the third is a number in then-points rating. ...
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32 views

Cox regression with two dependent expanatory variables

I am doing a Cox regression to model how survival times of sick patients, after taking a certain number of pills. There are in total 10 different pills patients can ...
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18 views

What issues may I face when interpolating my dependent variable in an OLS regression?

I'm doing my undergrad dissertation on what host-country factors impact FDI inflows - FDI inflows to the UK is my dependent variable. All of the independent variables I have managed to find at a ...
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25 views

Why added predictors improved performance in decision trees if they don't appear in the model?

although I've been working some months now with decision trees, I still have issues understanding some things and also finding a right source to answer my questions. Maybe I'm not using the right ...
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13 views

A control variable that forms part of the definition of the dependent variable: Drop it or transform it?

My aim is to analyze, using OLS, how Y (firms' benefits) depend on some factors. To normalize Y, I divide it by firms' size (S). Therefefore, my dependent variable is Y/S. To know how size affect ...
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8 views

Identifying the significant properties

(The problem is in linguistics.) I have a list of vowels from various words, some of which underwent a change, and for each of them a list of phonetic properties. I believe that the mechanism is this: ...
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19 views

Setting predictor values in MLR

I am just getting started with Multiple Linear Regression (MLR), and had a question regarding setting predictor values while using the prediction equation. Suppose, I have two predictors $x_1$ and ...
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1answer
30 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
106 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
14 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
104 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
22 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
26 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
35 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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24 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
41 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
124 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 ...
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1answer
90 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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60 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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22 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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82 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
94 views

graphical representation of fixed effects from lmer

I have run a lmer model in R: ...
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1answer
76 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
152 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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57 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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20 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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26 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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73 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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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
286 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
125 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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41 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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8 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
106 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
841 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 ...
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1answer
60 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
42 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 ...
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100 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 ...
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1answer
320 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
59 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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219 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 "...