Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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Error Output - Wrong model type for regression

I am trying to constructs an Learning Vector Quantization (LVQ) model. ...
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24 views

Is polynomial regression restricted to linear models?

I'm wondering if polynomial regression extends to generalized linear models, so one could fit a model with a binomial, Poisson, gamma or other distributions? My question stems from a paper ...
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9 views

Univariate Linear regression [on hold]

I have only one predicator variable but I have it in 2 different times (week 18 and week 32) in a categorical form ( no, yes occasionally, yes mostly), and my outcome is a numerical variable. is it ...
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13 views

Combining confidence intervals from several regression point estimates

I have 13 point predictions from 13 independent linear regressions, each prediction with a 95% confidence interval. I want to sum the 13 predictions and calculate the 95%CI for the summed value. ...
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15 views

What information to include when reporting the results of simple linear regression in a scientific publication? [duplicate]

I am writing a scientific journal paper. In the paper, I describe one simple linear regression analysis (1 dependent variable and 1 independent variable). At the moment, I have included a scatter ...
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10 views

Looking for a method to statistically compare 2 3D structures

G’day Statistics Forum I’m having a little bit of trouble with the statistical analysis segment of my honors thesis proposal and was hoping one of you helpful folk might be able to offer some insight. ...
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1answer
13 views

Regression: can I run specific models or do I need to run a full factorial model?

I am running a GEE (Generalized Estimating Equations) Linear Scale Response regression. I have 4 IVs: Time pressure and Approach as as factors, and BIS and BAS sensitivity as covariates. My DV is ...
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10 views

R square change for 2-way interaction model

I want to test a regression model with neuroticism as focal predictor, agreeableness as moderator and RT variability as dependent measure (covariates: attentional control and mean RT). Previously, I ...
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1answer
12 views

One main effect and one interaction in R using multiple regression, is that possible? And why am I getting two interaction terms in output?

I have two factors that are fully crossed, the levels of the factor are each coded 0 and 1. I am running a regression testing for one main effect and one interaction. The following is my logistic ...
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Understanding ANOVA for comparison of models in R. Why is the result the same regardless of model order?

I'm trying to figure out why the anova function in R gives me the same results (for the p-value) regardless of the order of the models. ...
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5 views

How can I use Extreme Learning Machine in Rapidminer? [on hold]

I am working on a problem, I need to use 'Extreme Learning Machine.' But I don't know any programming language. I use Rapidminer. Is it possible to use the algorithm on Rapidminer?
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Is there something called “mean coding” (like dummy coding & effect coding) in regression models?

When we perform a regression analysis with categorical predictors, we can use (1, 0), called "dummy coding". The coefficients in this case represent the deviation of the groups' means from the mean of ...
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16 views

Regression model for Cumulative data in R

I am having a daily data for 3-4 months and another variable which is the cumulative sum. It starts with some value on the first day and it keeps on adding and at the end of 3 months, it would be sum ...
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1answer
13 views

Change in r squared due to clustering in multiple linear regression

Puny undergraduate stats student here. I am examining the effect of two regressors on a predictor. OLS on the raw data (approx 200k cases) yields next to no correlation in the following models: ...
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10 views

Can the inclusion of exogenous variables in an ARMAX control for non-stationarity?

I have a non-stationary time series. If I run an OLS regression, the residuals appear non-stationary but serially correlated. Can I then run an ARMAX model on this time series, since the inclusion of ...
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11 views

Experimental design - main effects and error bars [on hold]

I've conducted a full factorial design set of experiments, I have 3 factors, with 2 levels for each factor plus a central point to assess the existence of a curvature. I've Measured responses, and ...
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9 views

SPSS Binary logistic regression incoherent result after excluding outliers [duplicate]

I'm using binary logistic regression for my master thesis and after running the regression for a a few specific variables I get the following resutl: Then I created a filter to run the regression ...
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1answer
64 views

Using linear regression for count data - will this introduce bias?

Say I am fitting a model to Poisson count data, but I am only interested in estimating the mean of the count variable. I understand a ordinary linear regression is a good approximation when the ...
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19 views

Detrending a series that moves about zero

How can I avoid my de-trending model from blowing up? Do I need an additive model rather than a multiplicative model? If so, is there anything I would consequently need to take into account? Can I ...
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7 views

Which way of regression modelling is correct among below options, when the predictor is a product term? [duplicate]

I am modeling a response Y against a predictor X, which is a product of two variables X1 and X2. I am interested in the coefficient of X. Mathematically which way of modeling is correct?: Y ~ X Y ~ ...
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1answer
25 views

Want to predict the top 100 students out of 1000 students - what model to use? [on hold]

Currently, I'm looking at a model that uses logistic regression and then ranks the results based on probabilities from the logistic regression. Is there a better methodology?
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Comparing goodness of least-squares fits through origin

I was wondering how to measure the goodness of fit of a linear least squares regression constrained through the origin. I have been using r-squared for comparing unconstrained fits, but I understand ...
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1answer
59 views

Improving a logistic regression model in R [on hold]

I have to devise a model in R, capable of predicting the type of a disease, which is a categorical dependent variable (three possible values), through several continuous and categorical, some of the ...
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18 views

marginal likelihood in linear bayesian regression (in weight-space)

I want to tune the hyperparameters namely the target deviance $\sigma_y$ and weight deviance $\sigma_w$ in bayesian linear regression. The posterior distribution in level-1 inference which is ...
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loop ordinal regression statistical analysis and save the data R [on hold]

I am relatively new to R. The short version of the data looks ike this: ...
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1answer
49 views

Multivariate OLS - Partialling Out

I have bee wondering why in a multivariate OLS-Regression it is not possible for R² to decrease when increasing the number of explanatory variables. The Point is that for example in the model ...
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1answer
22 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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Forecasting with no seasonality

I have a set of data, let's say average weight of employees, captured every month over a period of 5 years (2010 - 2014). I cannot find a seasonality trend in the data over these years. Also, I have ...
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7 views

birnbaun saunders regression model [on hold]

i am running a birnbaun saunder (BS) regression model. My response variable is amount( in dollars) and my predictor variable is operational time ( in %). please which code will i use to generate it ...
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19 views

What is the best way to extract time-series shared by two variables?

I have one dependent and several independent variables. I want to extract the time-series of the independent variable that is shared with the dependent variable. In other words, I want to extract only ...
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21 views

linear regression with autoregressive errors ~ARMA(1,0)(2,1)[12]

I am fitting monthly data that are expected to be auto-regressive (streamflow), but I want to include other independent variables (in my case it is a multivariate regression, with about 4 variables). ...
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1answer
53 views

Combining principal component regression and stepwise regression

I want to use a combination of principal component analysis (PCA) and stepwise regression to develop a predictor model. I have 5 independent variables (which are correlated among each other to ...
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18 views

Overfitting of Regression with Robust Variances?

I performed regression with robust variances (after Stata 12.1 lnskew transformation). A question of overfitting has been raised. To summarise what I did: [1] Comparison of BrS (disgrp=2) vs ARVC ...
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1answer
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Mediator reverses direction of causal variable. How to interpret?

I'm running a mediation in SPSS as per Baron and Kenny's guidelines (using regression). X is a dichotomous variable; M and Y are continuous. Step 1) X-->Y (r = .07, p = .03) Step 2) X-->M (r = .45, ...
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72 views

Significance of regression coefficients and their equality

Suppose, we want to regress $y$ on $x_1$ and $x_2$, i.e. $$ y = \alpha + \beta_1 x_1 + \beta_2 x_2 + \varepsilon \hspace{1cm} (1)$$ Is it, in principle, possible that simultaneously: $\beta_1$ is ...
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Efficient Triangular Backsubstitution in R [migrated]

I am interested in solving the linear system of equations Ax=b where A is a lower-triangular matrix (n $\times$ n) and b is a (n $\times$ 1) vector where n $\approx$ 600k. I coded up backsubstitution ...
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2answers
25 views

Tests of heteroscedasticity in linear regression models

I am unfamiliar with the implementation used in the R package GVLMA. What are some basic tests of heteroscedasticity in linear regression models and how or where ...
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6 views

Convert coordinates to neighbor list for spatial analysis in R using INLA [on hold]

I am trying to do an analysis of two greyscale images taken a few seconds apart, where each pixel in the first image should be predictive of the pixel in the second image. In general pixels near each ...
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1answer
71 views

logistic regression in r with many predictors

I have been running logistic regression in R, and have been having an issue where as I include more predictors the z-scores and respective p-values approach 0 and 1 respectively. For example if have ...
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1answer
44 views

Assumtions behind simple linear regression model

If we are taking about simple linear regression model, that is, $y = X\beta + r$ where $y$ is a vector of size n x 1, $X$ a matrix of size n x p, $\beta$ the regression coefficient vector of size p x ...
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12 views

Using gbm to eliminate variables before glm

I have a classification problem I am attempting to model using logistic regression (via the glm package in R): ...
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218 views

nonlinear regression two equivalent models on paper, but different estimated parameters

I measured one response variable Y1 as a function of two measured independent variables X1 and X2 It is common practice in ...
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30 views

Next step after principal component analysis

I have a data set that consists of different characteristics of communities. What I want to do is to see how those characteristics influence each other. As in, for example I have the income and ...
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33 views

What kind of data types are best to work with prediction algorithms of R

Assuming the data is tidy and it has a mix of columns of type numeric, character and Factor. What is the data type that would give best results when using different prediction techniques in R? I am ...
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1answer
49 views

Why do we need to log transform independent variable in logistic regression

I am curious that since we don't have normality assumption of the independent variable in logistic regression, why do I see people using log transformation for independent variables in logistic ...
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1answer
76 views

Handling categorical predictors in logistic regression, linear regression and SVM

I want to know how I can handle categorical variables in logistic regression, linear regression and SVM. The categorical variable has four categories 1,2,3 and 4. However, it doesn't mean 4 is like 4 ...
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2answers
50 views

Logistic/Probit Regression if the response variable is not a probability

I am working on a model which involves predicting a ratio between 0 and 1 using a number of variables. The ratio in question cannot be thought of as a probability. I am wondering if a logistic ...
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18 views

Variable reduction techniques

I am researching variable reduction techniques for time series data. Atm I came up with expert judgement, Stepwise Regression (Forward), Stepwise Regression (Backward) and Granger Causality. Any ...
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Coefficient with df=0 in log-binomial model

I am using log-binomial modeling in SAS to model the PR of my outcome given exposure directly since the prevalence is >10% so the OR~PR approximation doesn't hold. Most of my models have converged ...