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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31 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 ...
2
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0answers
10 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
44 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
269 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: ...
4
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3answers
153 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
13 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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0answers
28 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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0answers
21 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
23 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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0answers
27 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
31 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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0answers
30 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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0answers
28 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
25 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 ...
0
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3answers
112 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 ...
1
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1answer
46 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. ...
1
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1answer
64 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
37 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 ...
0
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1answer
55 views

Issues regarding max. number of independent variables, multiple regression with dummy variable and validation

I have developed a regression model, and found some issues with the overall process. As I do not have depth knowledge on regression, I need some expert advice. I have n observations (4700+) from ...
0
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1answer
36 views

How to estimate the best predictor?

I have a continuous variable $y$. Using univariate linear regression I have tested $a, b$ and $c$ as independent variables against $y$ as a dependent variable. I have gotten different $R^2$ and ...
2
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1answer
128 views

Coding for an ordered covariate

I am performing ordinal regression, I have 5 response categories and several predictors both continuous and categorical. I would like to add a predictor which is categorical but ordered (1, 2, 3, 4). ...
0
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1answer
58 views

Random forest importance differs between rf$importance and importance()

My model is working ok (the AUC is 0.7) but the importances from a randomForest run for my binary classification problem differ depending on how I retrieve them. Is ...
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0answers
32 views

Should predictive buying models change when products/prices change?

My company creates logistic regression models that predict who will buy based on 1st & 3rd party online click data. We use this to target online visitors with interventions like retargeting ads. ...
3
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1answer
305 views

Converting standardized betas back to original variables

I realise this is probably a very simple question but after searching I can't find the answer I am looking for. I have a problem where I need to standardize the variables run the (ridge regression) ...
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3answers
304 views

Econometrics - choosing the best model when removing variables

So I am looking into a regression model that is supposed to predict the value of a house based on numerous independent variables. What I don't quite understand is how to select the "best" model when ...
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0answers
75 views

need name, reference, and/or study for the following variable reduction procedure in regression

I have seen the following commonly used: 1. fit a model with all variables, 2. in a single reduction step, remove from the model all variables at once that do not fit some criteria (p-value, ...
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1answer
78 views

Entering dummy variables and other covariates into the regression model

I am controlling for education-level (two dummy variables) and another covariate in my regression model. I'm conducting a hierarchical regression analysis. I was confused about how I should be ...
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0answers
64 views

Determine Relationship between Categorical & Latent Variables, running SEM Model

I have a two part question, I believe. After running some analysis, a (predictor) strategy variable in my model is (unexpectedly) categorical. I now have four categories of strategy. I need to ...
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0answers
199 views

PLS-DA with binary predictors in R (package mixOmics)

I am trying to analyse a dataset with at minimum 50 explanatory variables coded as 0 and 1 for presence/absence and a binary response variable (case/control). The goal is to see how the variables can ...
1
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1answer
36 views

Compare mean effect of one predictor based on another predictor

I would like to compare the effect of one IV ("sensation seeking") on a DV ("intended infidelity") based on another IV ("gender"). Actually I really just want to compare the means. So I would like to ...
7
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2answers
725 views

Forecasting hourly time series with daily, weekly & annual periodicity

Major edit: I would like to say big thanks to Dave & Nick so far for their responses. The good news is that I got the loop to work (principle borrowed from Prof. Hydnman's post on batch ...
3
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2answers
248 views

Is a log transformation of predictors a suitable way of dealing with multicollinearity in multiple regression?

Suppose two independent variables in the linear regression initially have very high correlation of 0.95. This introduces severe multicollinearity into the model (as indicated by very high variance ...
5
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0answers
94 views

Using percentiles as predictors - good idea?

I am thinking about a problem which is to predict log(spend) of a customer using linear regression. I am considering what features to use as input and wondering if it would be OK to use the ...
0
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1answer
412 views

Continuous and categorical variables in SPSS GLM

I'm currently writing the results part of my thesis and I'm extremely bad at statistics. My variables are Dependent variable: Response time (with two levels) Factor: manipulation variable (exposing ...
0
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1answer
172 views

Count data as an independent variable in OLS- using a dummy variable+ the variable linearly to account for skewness

I am using OLS to model the relationship between amount of foreign aid (dependent variable, logged) and media coverage (number of newspaper articles, count variable). I assume a linear relationship ...
3
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1answer
56 views

Convert back standardized linear predictor

I guess this is a quite basic question, but I have been struggling with this for quite some time, so I hope someone can help me with this. I have a model (type is not relevant for now) which includes ...
1
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2answers
47 views

GLMM predictor variables

I have a question regarding predictor variables in a GLMM analysis. I am running a study investigating multi-modal communication in primates, specifically, primate gestures that accompany ...
1
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1answer
157 views

Effect of including/excluding predictors on beta estimation in linear regression

I'm interested in the effect on beta estimation of including/excluding independent variables in linear regression. I've made this data below: ...
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2answers
524 views

Which number is of value in linear regression: R Squared or P?

I am attempting to compare a two pitching stats (W/L% and ERA) to determine to what extent the latter can predict the former. After entering my data and performing the appropriate linear regression ...
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0answers
31 views

Necessary amount of countries in the model for 1 macro level predictor?

Within my logistic regression models I use cross-national data in order to say something about individual soft drug tolerance. In total I have 29 countries with 37.000 individuals. My supervisor ...
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2answers
451 views

What statistics should I use for evaluating the accuracy of predictions?

I have two variables representing 1) players' predicted fantasy football points and 2) players' actual fantasy football points scored. What statistics are best for assessing the accuracy of the ...
1
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1answer
58 views

Strongest predictor of outcome

I am doing a study that is on 5 biomarkers a, b, c, d, e which are continuous variables. Having high a, b, c is bad and low d, e is bad for the body -- it causes bad outcomes. Now, I collected data ...
3
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1answer
135 views

Can we use as predictor a variable that was used in the calculation of the dependent (a ratio)?

I wonder if someone could give me some advice on using ratios as a dependent variable in a Generalized Linear Model. I have a variable referring to the increase of "size at Time 1" to "size at Time ...
2
votes
2answers
73 views

Can you use proportions as a covariate in a Cox proportional hazards model?

In R's survival::coxph function, can I mix a covariate representing proportions (in the range 0.0-0.5) with an integer covariate (in the range 1-15), or should I ...
3
votes
1answer
793 views

How should I handle a left censored predictor variable in multiple regression?

I have a dataset (N=350) for which I would like to regress a neuropsychological test score (continuous) on age, education, symptom severity (continuous), and diagnosis (binary). Symptom severity is ...
0
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1answer
320 views

Interpret significant predictor in non-significant regression? [duplicate]

Possible Duplicate: How can a regression be significant yet all predictors be non-significant? In a simple linear regression with multiple predictors, is it valid to interpret the ...
3
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0answers
234 views

Rule of thumb - number of predictors - Poisson regression rates

I am interested in estimating a Poisson regression for mortality rates, with number of deaths as the dependent variable and log(population size) as the offset. I have 50 observations (states). I am ...
1
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1answer
159 views

Comparing continuous predictors for a dichotomous variable

I have two continuous predictor variables to predict a dichotomous variable. In addition i have constructed two (interaction) models, based on domain knowledge which use both variables to predict the ...
4
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0answers
117 views

Making new variable instead of correcting for temporal autocorrelation in a GLMM. Is it a valid alternative?

I am doing some forest disturbance research, in which the aim is to predict the probabilities of wind damage occurrence in forest stands of different site (altitude, slope steepness) and stand ...