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

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Quantifying Independent variables for Multiple linear regression

I have 3 independent variables (factors) that prompts banks to close.one variable is financial pressure, 2 variable is drop in donor charity and 3rd is for profit conversions. Now how do I quantify ...
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24 views

How to find correlation for multiple dependent varisbles

I have a 7x7 matrix of dependent variables (so 49 dependent variables). My independent variable is time. I am doing a physics project in which I am supposed to get a matrix function (every element of ...
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Compare two lm() which are subsets of each other

I'm trying to compare two linear models, one calculated with full dataset and one calculated on a subset of the same data. The reason why I need/want to do that is, I suspect a part of the data to ...
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16 views

Predicted Change in Score = 3 + .25*Female [on hold]

I saw this on your website. Where can I find more about this subject? Here is the url where I found it. How would I calculate the expected change? Based on the info below, how would I calculate the ...
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Regression models to only predict integers (instead of floating point numbers)?

I have a dataset that consists of about 50 different attributes. One of these attributes I want to predict, using the other attributes as features. The values of the attribute that I want to predict ...
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17 views

Do we need gradient descent to find the coefficients of a linear regression model

I was trying to learn machine learning using the coursera material Andrew Ng uses gradient descent algorithm to find the coefficients of the linear regression model that will minimize the error ...
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20 views

spurious regression/co-integration

I have two I(1) time series and I regressed one against the other and found that it had low to moderate R-squared but my DW statistic is about 0.015. I know the literature says this is the case of ...
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1answer
17 views

Obtaining regression model B-values in an OLS model in R

This is probably a fairly silly question, but I have the following regression model: ...
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19 views

Do confidence levels' ranges have a high correlation with prediction error

I've created four linear regression models each with different variables. I looked at the error rate: (actual-prediction)/actual and also on the confidence levels (90%). I've noticed that there is ...
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AR(1) error, add lag or Cochrane-Orcutt?

If I have a simple regression model and the residuals are autocorrelated, what is the difference between a) simply adding the lagged dependent variable to the list of regressors and running OLS b) ...
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Q: How to correctly include a categorial control variable in a regression model (in R)?

I'm trying to fit a (logistic) regression model to predict the successful funding of crowdfunding ventures (0/1) based on a series of IV with different level of measurement. One of these IVs is a ...
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1answer
25 views

Survival regression variance estimates

I would like help understanding why a survival regression with no censored data-points does not give the same variance estimates as a linear model (see code below). I think it must be something to do ...
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14 views

Robust Generalised Least Square

Here generalized least square refers to the situation where $Y_1, \dots, Y_n$ are NOT $i.i.d.$. Instead there is a known variance-covariance structure among them. But the $X_1, \dots, X_n$ are fixed ...
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Comparison of distributions

I measured the velocity of particles depending on some biophysical conditions (summarized data from two DoE plans) for about 100 samples. The goal was to identify the most important parameters ...
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8 views

Crude Analysis and adjusted analysis? [duplicate]

What is the difference between Crude Analysis and adjusted analysis? and what does this sentence mean? "analysis were done in 2 phases, both unadjusted and adjusted for covariates described earlier." ...
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1answer
27 views

Interpretation of log(1 + var) transformed predictor

Interpretation of log transformed predictor neatly explains how to interpret a log transformed predictor in OLS. Does the interpretation change if there are 0s in ...
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1answer
24 views

Regression imputation of missing data

Suppose a two-way experiment with interaction. Is it correct to estimate the missing values by OLS, input those values in the data (fill the blanks) and now perform a polynomial (or any kind of) ...
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1answer
277 views

Understanding QR Decomposition

I've got a worked example (in R), that I'm trying to understand further. I'm using Limma to create a linear model and I'm trying to understand what's happening step by step in the fold change ...
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52 views

Bayesian regression full conditional distribution

I have a problem with the derivation of the full conditional distribution of the regression coefficients in a simple Bayesian regression. The source of the following equations is: Lynch (2007). ...
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1answer
24 views

Alternatives to Chi-Squared for Single Categorical Outcome and Single Categorical Predictor w/counts for factors [R]

I am from an applied background, where X2 and G-tests are the default ways to analyze count data (default as in, until today, I had no idea there were other ways, as I was only taught these methods). ...
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How to incorporate characteristics of the object of outcome variables as predictors in model

I'm working with survey data for a social science research topic. The particulars aren't super important, so I'll use a simplified version to make it easier to understand what I'm trying to do. I say ...
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1answer
31 views

Time dependence and cumulative logit regression question

I'm looking to do some research with the GSS (the General Social Survey; a survey that asks over a 1000 people every year various questions and collects their demographic information as well). I'd ...
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1answer
29 views

Adaboost for numeric dataset

I have been trying to fit Adaboost to work with continuous valued data set and the more I read the more I keep getting confused. I have read about the multiclass Adaboost with log(K-1) addition to ...
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1answer
27 views

Sample size calculation, linear regression

I have just had my viva and my sample size calculation was criticised as it was based on r2. I was told to base the sample size on the minimal magnitude of association. My outcome variable is HbA1c, a ...
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1answer
35 views

Regression imputation of missing data based on OLS effects

Let's say we have a two-way with interaction experiment with missing data. Being the dataset: ...
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Interpretation of coefficients in linear regression: first difference of logs

what is the correct interpretation of coefficients in time series regression when using first differencing on logs of the DV and in certain IVs. FD(lnY)=c+beta1*ln(X)+beta2*FD(lnZ) The log ...
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50 views

Predicting male and female incidence rates over time

I have time series data for any number of time points for both males and females. Would it make sense to have sex as the entity in the panel approach? I have data on the annual rates at which a ...
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What are the differences between Ridge regression using R's glmnet and Python's scikit-learn?

I am going through the LAB section §6.6 on Ridge Regression/Lasso in the book 'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013). More ...
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GP regression - Matern kernel gradient issue

I'm trying to use a Matern 5/2 kernel for GP regression, so my kernel function is $ K(x,x')\triangleq\theta_0(1+\sqrt{5r(x,x')}+5/3r)\exp(-\sqrt{5r}), $ where $r(x,x')\triangleq\sum_{d=1}^D ...
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51 views

What property of logistic regression is useful for modeling user behavior? [on hold]

I want to know that what property or attributes of logistic regression make it to useful for modeling user behavior.
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Hierarchical Bayesian Regression with an Indicator Variable, one group has all zeros for the IV Variable

I'm attempting to form a Bayesian Hierarchical Regression Model and one of my regressors is for an indicator variable. My hierarchy structure has separate group-level regressors related across-groups ...
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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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27 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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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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14 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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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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16 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
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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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Interpreting the Intrinsic Scatter (dispersion)

I have some data with uncertainties on both the independent and the dependent parameter. I did a Hierarchical Bayesian Model to study whether there is a correlation between the dependent and the ...
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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
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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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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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2answers
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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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18 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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19 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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11 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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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 ...