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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11 views

How to estimate dependency or in-dependency between variables [on hold]

Is it possible to estimate "the rate/degree (or something similar)" if there is dependency between 4 different types of variables that are categorical. If the 5th variable is a outcome variable(the ...
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24 views

Appropriate number of explanatory variables in redundancy analysis (RDA)

This question comes from a reviewer's comment on a manuscript I recently submitted. I analyzed a multivariate data set (6 response variables, 21 observations for each) using redundancy analysis (RDA) ...
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38 views

Confidence in Small Data Set [closed]

I have 300 rows. Target (Performance) is between -1.2 and 1.8. 50 sentiment features are between 0 and 1. Other features are age, salary, absence days. I plotted regression against each of the ...
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1answer
43 views

Algorithm: multi label classification

I am a biologist and I have an algorithm question, I asked on stack exchange but was suggested to come here. Also, I have really tried to explain my problem using simple toy data; note that in real ...
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0answers
9 views

Variable Importance (Linear Models) for dummy encoded predictors

I've built a model using glmnet under the caret package. I have dummy encoded predictors (4 levels for 1 variable) and want to estimate the variable importance of each variable (using varImp) . Since ...
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1answer
42 views

Reasons NOT to use standardised data in multivariate analysis

Question: Can you give any reasons/examples when it is more appropriate NOT to standardise continuous metric independent variables when performing multivariate analysis? Background: I am an undergrad ...
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14 views

Paired test with 2 dependent variables

What test should I use to find out what effect an intervention has on two dependent variables? I have a test group of clinically depressed that will undergo a sports program. The program is expected ...
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5 views

Finding right lags for several independent variables in linear regression

I have gone through several other Stats.stackexchange posts such as these experiment lags, Cross-correlation function suggestion,VAR model approach and lagged dependent variable approach. One of the ...
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0answers
28 views

Treatment of categorical data in R [closed]

I work most of my time with categorical data (predictors and outcome), I usually do a trees in SPSS to make groups and rank which groups are more predominant to buy / not buy. But now I'm into R, and ...
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36 views

predictor skewed but normality of errors

In linear regression (linearity assumption had been checked), what is the effect if distribution of predictor is skewed but errors are normally distributed? Is there a risk for estimation of ...
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6 views

Difference Beta on b-path depending on independent variable (all else being equal)

I hope you can help me with 2 questions: 1. Difference in B-path value I have run a simple mediation with one independent variable on two independent variables with a mediator and a covariate. Here ...
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1answer
16 views

Using centroids to find predictive cluster features

I clustered some data (rows: text documents, columns: word frequencies) using the KMeans implementation in Scikit Learn. This, like most other centroid-based clustering algorithms, returns a centroid ...
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1answer
50 views

Main Drawbacks of stepwise regression

People typically prefer the Lasso or other methods to stepwise regression. What are the main problems in stepwise regression which makes it unreliable specifically the problems with forward selection ...
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33 views

detecting the unknown canary [closed]

There is a metaphor "canary in a coal mine". The miners would keep the birds as an early warning system - if the bird suddenly died they would know something toxic was around and they could get out ...
2
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1answer
89 views

Alternative methods for logistic regression

Usually the condition of the validity of a logistic regression is to have 10 events per predictor. In our model the binary outcome variable (1 if Healthy aging ; 0 otherwise) has a frequency of ...
2
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1answer
29 views

Is it OK to use an original variable & another variable constructed from it in a regression model if there is no multicollinearity?

I'm doing binary logistic regression. I want to predict the chance of being in an advanced class. There no multicollinearity among my variables. I have three predictors: If you passed the test or ...
2
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1answer
55 views

How do I test for independence with non-exclusive categorical variables?

Introduction I have a categorical contingency table with many rows and a binary outcome, which I count: ...
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1answer
37 views

How to enter IV in logistic Regression for testing significance

for my thesis I need to build a logistic regression model and test the significance of several indicators on a certain outcome, that is testing if the independent variables has a significant effect on ...
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30 views

Factor loading of predictor on outcome variable

I know the basic and some more advanced statistics. I want to use multiple lineair regression to see whether the standardized questionnaire PsyCap, leader adaptivity and employability culture can ...
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1answer
28 views

How do I treat a fairly strong correlation of a predictor to the output variable?

As one of the preliminary steps in my data analysis project, I am looking at a correlation table I made of my potential predictors and one output variable. There is no significant information overlap ...
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0answers
14 views

logistic regression: comparing odds ratios for two different predictors

I have performed a logistic regression analysis using the following variables: DV: Substance use (coded 1 = yes, 0 = no) IV1: Sports (coded 1 = participant plays sports, 0 = participant does not ...
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25 views

Determining Dependent and Independent Variable

I have this set of data with two variables (A & B) link here I would like to determine whether change (decrease/increase) in A will drive change in B, or the opposite (change in B is driving ...
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3answers
668 views

Do we really need to include “all relevant predictors?”

A basic assumption of using regression models for inference is that "all relevant predictors" have been included in the prediction equation. The rationale is that failure to include an important real-...
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23 views

regarding probability

From a survey we found that number of house holds having two wheeler are 4527, number of house hold having 4 wheeler are 3325 and the total number of house hold in that locality are 11728. Now I need ...
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19 views

Type of variable of my parameter

I have just begun some research into insect biting rates but I am fairly new to statistics and I was hoping for some quick guidance! I am measuring insect biting rate at different, even time intervals ...
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20 views

Statistical Test for Unequal Sample Sizes and Non-Independent Samples

My research involves using a data set from a clinical trial and splitting the patients based on two factors: violence (violent or not-violent) and treatment status (pre-treatment or post-treatment). ...
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19 views

Why don't we use testing sets in Estimation Theory

In estimation theory, researchers fit probability distributions to data. To evaluate whether this is a good distribution: why don't researchers hold out an independent test setand then test the ...
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35 views

Logistic regression: Why does significance change based on the specification of the categorical control variables

I want to run a logistic regression with the purpose of assessing whether working hours (scale variable) influence the probability of someone being clinically depressed (dichotomous variable). I have ...
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1answer
80 views

Do I throw away a variable that is not statistically significant?

I am running various models in R for sake of prediction. If I run a model and a specific variable is showing itself to be insignificant (say, at the alpha=0.05 level), would I want to simply discard ...
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2answers
43 views

Maximum number of predictors in regression

Are there any rules about the maximum number of predictors in OLS regression given the number of observations? I was argued that I should reduce the number of predictors (20) because I have 80 ...
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1answer
24 views

Categorical Variable in Training Set does not capture all cases

I am training a predictive model on a training data set, which includes zipcode as one of the predictors. Since zipcode is nominal, I treat it as categorical variable and try to dummify it. The ...
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0answers
47 views

handling missing input variable for machine learning

In building feature-sets for a machine learning algorithm, I'm facing a situation where the input variable - which is a numeric variable, may or may not appear. What I mean is, the data set I'm ...
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2answers
51 views

Three IVs, one DV. What test to use?

I've gotten myself into a pickle with my experimental design. I'm looking at how the temperature at which an organism dies is affected by: - the population it came from (e.g. Pop1, Pop2, Pop3) - the ...
2
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2answers
71 views

How to reduce the final set of significant variables from logistic model?

I have built a logistic model, which has 40 significant predictors, p value<0.0001. I want to reduce them to say about 10 variables, so that it can be presented to business. How do i go about doing ...
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58 views

Examining country level variables while controlling for country with dummies meaningful?

This is my first question and I hope I am posing it correctly (and hope you may have an answer): I have a sample that captures the performance of different firms from multiple (home) countries that ...
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56 views

dependent and independent risk factor

when a certain association appears only when we adjusted for certain potential confounders. In this case, can we say that this association is independent of this confounder. in my case, I'm studying ...
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1answer
130 views

Is Random Forest the only algorithm to measure the importance of input variables …?

I have three time series say (Stock price open, Stock price high, Stock price low) and one output (Stock price close) and I need to know which of the 3 inputs has a greater effect on my output. R's ...
4
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1answer
131 views

How to predict & plot quasipoisson GLM in R

I have a set of complex survey data with sampling weights. I am using the svyglm() function from the survey package in R to ...
4
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3answers
287 views

How to handle ordinal categorical variable as independent variable

I am using a logit model. My dependent variable is binary. However I have an independent variable which is categorical and contains the responses: ...
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1answer
55 views

Exclude not important predictors from dataset or leave them all?

I have a large dataset with 6 predictors, with a goal to predict bank loan interest, based on year income, time at work, loan amount, credit balance, credit utilization rate, etc. I use python with ...
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69 views

Combine multiple independent variables into one variable in a GLM/GAM/GAMLSS model

In the R package gamlss there is a function centiles that according to the documentation is ...
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0answers
42 views

Should I use GEE, Multilevel or Cox regression?

I am interested in the relationship between the number of hours worked per week and the chances of getting an illness. My dataset consists of 47 subjects, the predictor variable hours worked per week ...
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1answer
26 views

An independent variable in the logistic regression has 2.2%, 2.1% and 95.7% distribution [closed]

I have one independent variable in the logistic regression with a 2.2%, 2.1% and 95.7% distribution (three categories IV). My DV has good distribution (68% and 32%). How would this IV affect my ...
0
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1answer
41 views

Is feature complementarity different from feature interaction?

I am writing a conference paper in which I have a sentence like "...complementary/interactive features...". This sentence ...
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1answer
52 views

In regression, what is the limit of independent variables?

After having taken the Coursera Data Science specialization, I am faced with my first "practical" problem which I plan on solving with some sort of regression. This is my first real world, business-...
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21 views

Active independent variable

In a study, there are two independent variables - A and B. We only manipulate variable B. Do we count A as an independent variable in our analysis, even though it has not been manipulated?
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74 views

Testing to Compare for “Impact” of Independent Variable on Dependent Variable

How can I perform a statistical test to judge impact of an independent variable on a dependent variable given multiple regression output? In the example below, how would I test if gender or work ...
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1answer
17 views

Evaluating a pretreatment predictor with repeated measures ordinal data

I am currently attempting to study whether particular baseline factors predict pre/post changes in relation to a substance use intervention. However, a few things have me stumped. Our substance use ...
1
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1answer
59 views

Positive and negative impact of predictors on responses in data mining models

My question is an extension to the question asked here. How does one identify the parity of predictor/feature/variable impact on response/outcome in a data mining model. Is there a standard procedure ...
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2answers
56 views

How “bad” is it to use an independent variable as part of a proxy dependent variable in forecasting?

I am fitting a logistic regression model in order to forecast the occurrence of an event. Let the event I am interested in forecasting $Y$ be whether or not someone purchases stock in a given company ...