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

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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
16 views

Want to predict the top 100 students out of 1000 students - what model to use?

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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9 views

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
28 views

How to create and improve a logistic regression model in R

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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16 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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8 views

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
37 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
20 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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17 views

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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18 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
47 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
15 views

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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1answer
51 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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10 views

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
23 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
69 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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9 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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2answers
205 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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27 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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32 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
48 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
75 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
46 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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9 views

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 ...
2
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1answer
29 views

Difficulties obtaining valid predictions when using interactions

I examine long term trends (2003 to 2014) for a continuous dependent variable. I want to predict the mean each year in relation to income category. Income is arranged in quintiles, from 1 (poorest) to ...
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1answer
36 views

Weight shrinking in linear regression by L2 regularization

Quoting Prof. Bengio from his Deep Learning text (http://www.iro.umontreal.ca/~bengioy/dlbook/regularization.html), $ w = (X^{T}X + \alpha I)^{-1}X^{T}y $ We can see L2 regularization causes ...
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38 views

If removing another variable makes another variable insignificant should it be removed?

This is a logistic regression used for the goal of prediction. Originally a model had ten variables. Two variables were removed using a clustering procedure. Then one variable was removed due to ...
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1answer
22 views

Separated regression analysis vs control the covariate

I am conducting a data analysis on an Epidemiology cross-sectional study. Suppose the outcome variable is an binary variable for health status (1=health, 0= unhealth). And the exposue is infection at ...
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48 views

How to know which model is appropriate for my data?

I study regional planning. there is a theory that says population density (D) is changing by distance to CBD (center of city). And the model for any city is different. And I have population density ...
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15 views

Transforming independent variables in R [duplicate]

Is there a way through which we can determine how can we transform our independent variables to increase linearity. I am aware of boxcoz function but it provides information on transforming dependent ...
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2answers
115 views

Logistic regression or T test?

A group of persons answers one question. The answer can be "yes" or "no". The researcher wants to know whether age is associated with the type of answer. The association was assessed by doing a ...
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17 views

How to write an equation for a spline regression

I have this basic regression model that I would like to make into a spline with a knot at -1. How would I write it? Is it possible for me to write it as a single equation where I subtract 1? Here's ...
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Self-designed objective for multiple linear regression

A multiple linear regression is to use several predictor variables to predict the outcome of a response variable, like the following relationship: ...
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1answer
23 views

Interpretation of coefficient in log-linear model with share predictor

There are several questions on the interpretation of coefficients in log-linear models such as Interpreting regression coefficients of log(y+1) transformed responses Log linear model interpretation - ...
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Lagged Part-time Employment Share as Instrument

I am going to run a regression of unemployment on part-time employment share, but as there will be reverse causality I am going to use an IV. I only have the part-time share for 24 years (with 1 ...
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23 views

Urgent help required [on hold]

For the study ,i have 3 independent variables and 1 dependent variable. After performing a correlation analysis, variable 1:r=.58 and p=.000 variable 2: r=.41 and p=.000 variable 3: r=.37 and p=.000 ...
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32 views

Does feature size affect polynomial regression?

(I'm still trying to learn all this, sorry for any wrong terms or mistakes I might have made in this question) By feature size, I mean the value of the numbers. For example, let's say I have input ...
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39 views

Can I create an Index score using factor scores as weights?

I am creating an Overall Customer Satisfaction Index score based off of 4 factors that comprise satisfaction for callers to a call center: A representatives concern for your needs; ease of navigating ...
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5 views

Adding variables to conjoint

Could you please help me with conjoint analysis. I would like to conduct choice based conjoint. I have created attributes and levels of the product. My dependent variable is willingness to pay. So ...
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20 views

Using regression fit to compare two groups at a time point

I have estimated a regression model in R with two groups, included as factors. Their regression fits are visually quite different, but I would like to statistically test if they are different on a ...
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6 views

Binomial Logistic regression to Compare results of multiple methods to a known value

I have a "known value" (recorded by a field observer) which I want to compare several methods of data collection with to test for significant differences between methods. Methods: 2 points along a ...
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1answer
42 views

How to use linear regression for heavily skewed purchase data?

I am trying to use multiple linear regression to predict the amount that a particular user will spend in a particular time frame on a particular site. Part of the problem is that there are very few ...
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1answer
14 views

Solving “n” equations with 3 unknowns

I'm new to R and I'm trying to solve a system of equations. I have about 380 equations where i have 3 unknowns per equation. I can use three equations and solve by using "solve()" and it works great. ...
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1answer
32 views

set slope to 1 in linear regression in r [closed]

In R, lm(y~x) will get the linear regression in terms of y=a+bx, giving the estimates of both slope and intercept. Now I want to set the slope to 1, and do the regression, which means the form of the ...