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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8 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
33 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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22 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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4 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
65 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
86 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
49 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
19 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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23 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
292 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
29 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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59 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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31 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 ...
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32 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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31 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: ...
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1answer
47 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
44 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
19 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 ...
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1answer
57 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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19 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 ...
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2answers
35 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
69 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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28 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
62 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 ...
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26 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
62 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 ...
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20 views

Clustering on structural variables?

I'm working with land surface models. These models basically take a bunch of meteorological forcing data (downward radiation, wind, rain, humidity, etc), and run it through some ...
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1answer
83 views

Interpreting LASSO tables in SAS

I have been working on LASSO in SAS lately, and I'm still trying to figure out how to work with the options, but my main question for which I have not been able to find an answer on the internet so ...
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1answer
529 views

Step function in R for regression modeling

I have to implement a regression model and i have about 30 variables in the model. Some variables does not have much influence on the model, but i need to use a formulized method for eliminating ...
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56 views

Are the predictors in an ANOVA assumed to be independent of one another?

I ran an ANOVA with 4 predictors (2 categorical, 2 continuous) and 1 covariate (continuous). Including the covariate did not significantly alter the main effects of the ANOVA when it was run without ...
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17 views

Nearest Neighbor Algorithm for Prediction

I am building a prediction model based on k-th-nearest-neighbor (KNN) method, as I have similar events. As the algorithm doesn't classify points to Sets, I was wondering if there is a way or ...
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2answers
92 views

Analyzing relationship between two Likert-items (is it possible?)

I am a student and am new to statistical market research. Is it possible to analyze the dependence of answers to one Likert-type item on answers to another Likert item (predictor)? If so, what test ...
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0answers
13 views

Using AIC to select an upper bin for a counting variable

I have some patient data and I'm using a logistic model to explore factors that might affect a patient considered severe/not severe for a disease (the DV). Many of the variables are counts; counts ...
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0answers
10 views

MANOVA, when independent variable is not multi-level and P value significance variance [duplicate]

My research has 4 independent variables and 4 dependent variables. My hypotheses are constructed in such a way that 1 independent variable's effect size is checked on 4 dependent variables. My ...
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0answers
84 views

MANOVA, when independent variable is not multi-level and P value significant in MANOVA but not in ANOVA

My research has 4 independent variables and 5 dependent variables. My hypotheses are constructed in such a way that 1 independent variable's effect size is checked on 5 dependent variables. MANOVA is ...
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4answers
332 views

Improving a linear regression: Add predictors or change model?

I am trying to model a time series variable $Y_{t}$ with $4$ physical predictor variables. I used the following linear regression: ...
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3answers
855 views

Can independent variables with low correlation with dependent variable be significant predictors?

I have eight independent variables and one dependent. I have run a correlation matrix, and 5 of them have a low correlation with the DV. I have then run a stepwise multiple regression to see whether ...
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0answers
18 views

Variable Selection Methods in R [duplicate]

regsubsets and stepAIC are the two most common options for variable selection in R; they can be found in the ...
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33 views

Very small coefficients

Suppose that you are running a linear regression model with outcome $Y$ and explanatory variables $X_1$ and $X_2$. You also run a linear regression model with a quadratic transformation of $X_1$: ...
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82 views

Correlation between a categorical predictor and a continuous outcome variable

How do I perform correlation between a categorical antecedent variable and a continuous outcome variable? Like for example, correlating EACH attachment style with another variable, i.e, social ...
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1answer
31 views

How do you calculate the amount of parameters needed to be estimated?

I don't quite understand this. A question was, pretend we have 4 predictors and all of them are binary - for the Naive Bayes method, how many parameters are there to estimate in the training step? ...
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34 views

Building prediction model with estimated predictor variables

I'm planning to use logistic regression with multiple (~5) predictor variables to predict whether something happens or not. I have two types of predictor variables: known (measurable) variables and ...
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1answer
43 views

Combining variables

I'd like to combine several variables into one variable. Here is some context: Let's say I have two variables Red.Beads and ...
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55 views

How to analyse the relationship between many categorical variables

I have a number of categorical variables, each with varying numbers of levels: gender of client (m,f) age group (0-8, etc.) diagnosis (mild, moderate, severe, profound) location (community, ...
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49 views

Creating an index with objective and subjective variables

I have some objective and subjective variables. My aim is to create a statistical index which defines variables and interrelations of variables accurately. For instance, suppose that I am creating a ...
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0answers
33 views

Comparing two subsets in multiple regression

I am doing subset selection for multiple regression model with "exhaustive" search. What is the most appropriate way to compare two such models based on subsets which are nested? I was thinking to ...
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3answers
202 views

Dummy variables and number of predictors in logistic regression

I have a problem with logistic regression. I had found out (here) that one of the assumptions of logistic regression model should be min. of for example 50 observations per predictor. But if I had ...
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1answer
89 views

Weird bootstrap bias for Predictor Importance (MeanDecreaseAccuracy) in Random Forests

Below, using R, I: 1. Create a data set with a bunch of factors. All of them are predictors and 'y' is the dependent variable. 2. I run a classification Random Forests for y with predictor importance. ...
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1answer
114 views

Nuisance covariate or variable of no interest in machine learning

I'm trying to differentiate two groups of patients using various machine learning algorithms, including support-vector machines (SVM). As far as the details of the analysis go, I would like to train ...
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0answers
44 views

price prediction

In the project I am working we have a couple of items with different prices. Each item has description but it is possible to find items with similar description. I would like to know how can I ...