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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12 views

Correlation or dependence between NDVI and pollution data sets

Is there any statistical test or measure to evaluation the degree of correlation or dependence between two sets of data-points ? First set is represented by NDVI values in each pixel and second set is ...
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21 views

Index-variable as an independent variable

In my regression on gdp-growth, I also want to bring in something like a "freedom"-variable, to show how free a country is (press freedom, economic freedom). now there is no number for this, except ...
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11 views

Percentage/Mixture data with many zeros

My dependent variable is continuous. My independent variables can be looked at in two ways. In the first, they are a bunch of count data with a large cluster at zero. In the second way, we can ...
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12 views

Should I include a ratio as predictor if I don't have the complete numerator?

There is a ratio 'dti' calculated using: the borrower’s 'total monthly debt payments on the total debt obligations', excluding 'mortgage and the requested loan', divided by the 'borrower’s ...
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35 views

How good is my computer aided diagnosis system vs the expert?

I have developed a systematic method that attempts to quantify the amount of disease present in medical images. E.g. % area abnormal. In my dataset, I have healthy people with no disease, and people ...
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39 views

Logistic regression on all data in order to analyze predictors

I have some experience working with classification, and in those instances we always use a training and a test set (and possibly validation sets). However, I'm currently facing a different problem. I ...
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21 views

Too small baseline predictors

I'm trying to implement a recommender system, based on SVD-algorithm. I have a matrix with binary rates, i.e. 0 and 1. This matrix is very sparse. I'm using a formula for learning process: ...
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16 views

Predictor variable relative importance in this regression

I am performing a linear regression analysis which has a continuous numeric positive value dependent variable and 7 independent variables. The independent variables include one continuous positive ...
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28 views

Any role of redundancy analysis for inclusion of predictors in regression model?

Is there any role of redundancy analysis (for example using the redun() function of the Hmisc package in R) in finding variables to be included for a regression ...
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17 views

Relative importance of predictors in the final model

A common question that frequently comes up, while presenting the findings of a predictive model to a Business audience (with non-statistical background) is - Which variable/predictor is most important ...
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25 views

Determining relative contribution of independent variables in predicting dependent variables in regresssion

I am running a multiple regression in which the dependent variable and both predictor variables are continuous, numeric and positive. lm(DV ~ PV1 + PV2, mydata) ...
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35 views

What kind of independent variables can I use for multiple regression?

I'm very new to statistics. My assignment requires me to use one statistical method taught during lesson so I only have a choice between multiple regression, logistics regression and MANOVA. My ...
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8 views

Variable selection algorithm based on meta study

Suppose I would like to conduct a survey and want to know which variables to include. Literature review and theoretical guidance both help. What I would like to know is whether there is a way to do a ...
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13 views

Optimal Predictor under symmetric loss [self-study]

I am seeking to prove that, under symmetric squared error loss $C(e)=e^2$ where $e$ is the forecast error h periods ahead $e=y_{t+h}-\hat y_{t+h}$, the optimal predictor $\hat y_{t+h}$ = ...
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15 views

What are valid ways of analysing predictors for a response variable that changes with time?

I have a cohort of similar patients who are likely to get a certain disease over time. I am trying to find out how some continuous health markers (e.g. weight) at time 0 are related to their disease ...
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44 views

How to treat variable in logistic regression?

I have a variable I do not know how I should handle my logistic regression. The variable is the number of registered students each semester. If I plot it against my binary outcome, I get the following ...
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20 views

Quantitative and categorial predictor in one model

This is what I would like to know, due to some logical problem behind! I have a model as: Crown radius = Diameter at breast height + Location DBH is quantitative, like 30cm, 40cm... Location is ...
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38 views

Comparing predictors based on ROC AUC and cross-validation error

I am analysing how well some continuous variables (e.g. weight, height) predict the occurrence of a given disease after surgery. I have computed the area under the curve of the receiver-operator ...
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10 views

Screening/Filter Method for classification problem

I have a data set with 100 variables. And the output is binary (case/control). What kind of method would be a good choice for screening variables at the beginning stage.
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12 views

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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32 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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32 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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61 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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55 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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159 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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69 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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34 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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35 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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22 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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29 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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16 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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21 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
40 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
129 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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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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137 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
25 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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36 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
39 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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28 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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51 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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134 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
108 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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65 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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28 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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89 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
130 views

graphical representation of fixed effects from lmer

I have run a lmer model in R: ...
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86 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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216 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 ...