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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18 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
33 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
12 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
52 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
18 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
29 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
64 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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17 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
49 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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23 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
51 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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19 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
46 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
137 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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0answers
53 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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0answers
13 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
65 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
12 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
67 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
309 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
495 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
14 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
31 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
52 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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0answers
31 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
39 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
44 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
39 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
173 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
74 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
85 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
42 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
81 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 ...
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1answer
45 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
222 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
74 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
37 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. ...
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1answer
564 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
578 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
76 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
115 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
69 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
280 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 ...
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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
1k 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
350 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 ...
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0answers
102 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 ...