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

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Multivariate quantile regression

I have a multivariate linear model: $\mathbf{Y} = \mathbf{X}\mathbf{B} + \mathbf{U}$ where the matrix $\mathbf{Y}$ represents stock returns, the design matrix is constituted by some explanatory ...
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2 views

Purpose of multiple models with fixed slope

I'm fairly ignorant when it comes to fitting models, and I'm having trouble grasping the point of fitting more than one line with the same slope. Is this generally used in a context with factors, ...
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Evaluating close calls with the Wilcon Sum Rank test two sided vs. one sided

I am testing to see if the means of two groups G and R are different. I cannot use a t-test because the data is not normal so I am using the wilcox sum rank test which seems like the non-parametric ...
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9 views

Does Frisch-Waugh require that variables be independent?

I'm trying to determine if I can use Frisch Waugh to find the effects of one variable from the residuals of a regression including everything but that variable. The following R simulation seems to ...
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8 views

Coefficient of determination in the presence of a certain measurement error

In page 138 of Green's Econometric Analysis, we consider a simplified type of measurement error that allows the usual OLS estimator to be consistent. In the picture below that model is described. ...
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Wilcoxon Rank Sum test null hypothesis

I am doing a Wilcoxon Rank Sum test in R to see if the means of two groups of data (the data is not normal) are statistically different. When I look at the details of the wilcox test ...
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multiple linear regression analysis with continuous and categorical data result interpretation

I have data from gene expression arrays and I have clinical data associated with the samples used. I am using gene expression (discrete), age at diagnosis (discrete) and ethnicity (categorical) to ...
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25 views

Linear probability model

Is there any advantage or any situation when the Linear probability model is superior than Logit model and Probit model, apart from its simplicity.
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Regression involving multiple measurements on known values (calibration)

Suppose I have two continuous variables Y and X and I want to predict a Y value given a specific X value. However, the dataset I have is composed of 15 particular Y values (that are known values) ...
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Variance of slope

I have a bunch of data that I fit a linear regression to, and now I need to find the variance of my slope. Is there an analytical way to get this? If an example is necessary, consider this my data in ...
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ols and multiple regression model wth two variables

My model is as follows: $y=b_1*X+b_2*\max(X,0)+u$ Will I have any problems with the variables $X$ and $\max(X,0)$, concerning any correlation issues? Can I just apply the classic OLS methodology?
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Regression on aggregate data

I am trying to model how salary increases across time for different categories of college professor, and to determine the nature of how the trajectory of these increases differ from each other. Was ...
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linear discriminative analysis for regression

LDA computes a projection matrix to maximize class conditional probability. Similar to this, is there any exisiting method or library for jointly learning latent space and minimizing the regression ...
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119 views

Different shapes of an ROC curve

What are the possible shapes of an ROC curve? Is it necessary for an ROC curve to be shaped like a normal distribution curve? Can we regard the following two curves as ROC with the area under the ...
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Normalized data and regression

Suppose I have eight subjects and measured performance in a time series (outcome measure is a distance measure). I assess learning effects across these time points by expressing the increase in ...
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1answer
13 views

What is the difference between linear perceptron regression and LS linear regression?

Recently, a project I'm involved in made use of a linear perceptron for multiple (21 predictor) regression. It used stochastic GD. How is this different from OLS linear regression?
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21 views

assuming independency between independent variables in multiple regression?

I heard that multiple regression assumes that the independent variables are correlated somehow. So when we convert the multiple regression into SEM diagram, we see covariance arrows are drawn ...
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K-fold cross validation and hierarchical data structure and lme4 package

I'm currently trying to locate R code to conduct a k-fold cross validation for a data set that contains a hierarchical data structure (i.e., GPS locations nested within individual animals). In ...
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Piecewise linear regression with SAS PHREG [on hold]

How to implement a piecewise linear regression model in PHREG procedure of SAS? For example with one knot at X=T: Finally i would like to include it in a Cox model: But the problem is S_1 has ...
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Is a significant predictor but low sensitivity in logistic regression a valid test result?

I used binary logistics regression (SPSS) to determine the relationship between ambient noise levels (a continuous variable on a logarithmic scale, dB) and a dichotomous dependent variable ("yes" - ...
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Errors vs measurement errors

I'm reading about how to fit a straight line with measurement errors in both coordinates ($x$ and $y$). Let the true unobserved variables be $x_{t,i}$ and $y_{t,i}$ and the observed variables be ...
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42 views

Statistical Significant vs Correlation

In my regression one of the control variables has high statistical significance. But when I check the correlation coefficient between this variable and the dependent variable the correlation is almost ...
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23 views

How to interpret the coefficients from Dirichlet Regression?

I have a response Y, which consists out of 5 response variables which are proportions and for each observation, add up to 1. I'm tying to regress these with a set of independent variables. As I ...
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1answer
35 views

a challenge with linear classification and distance to origin? [on hold]

I ran into a problem, when studying on linear classification. my prof. says: in a linear classification $y=w_0+w_1x_1+w_2x_2$ that depicted on following figure, distance of origin to decision ...
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43 views

Transforming TS for better fit

I'm trying to find transformation for my explanatory variable (outside temperature) to better explain heating power usage. I have data from one year here. ...
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33 views

Subset data in R [on hold]

I have several time series and want to regress the dependent variable on the explanatory variables. My question is: Because of structural breaks in my series I do not want to include all the ...
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39 views

drawing confidence interval graphs [migrated]

I've made regression model with 4 variables. And I have gotten the following regression equation $$ Y= 0.0761 - 0687X_1 - 3.46X_2 - 1.937 X_3$$ I calculated Confidence intervals for these four beta ...
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What is the difference between lm(log(y) ~ x) and glm(y ~ x, family = gaussian(link = “log”))? [duplicate]

Is all in the title. I would like to know if there is any difference in terms of coefficients, residuals, p-values, but also conceptually.
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222 views

Random walk estimation with AR(1)

When I estimate a random walk with an AR(1), the coefficient is very close to 1 but always less. What is the math reason that the coefficient is not greater than one?
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Log transformation on variable in percentage units

I was working through my STAT course and I got curious about this... I have seen lot of people use transformations on the variables in regression especially using logarithms, and usually the ...
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1answer
32 views

Closed form solution for t-stats and p-values in multiple regression

I am trying to build a spreadsheet that will perform multiple linear regressions on a number of data series using the closed-form solution. It was fairly straightforward to write the solution for the ...
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Repeated Observations Due to Pairings in Logistic Regression

I have data with repeated observations within a given year. Here's a snippet of the data: ...
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64 views

If $\operatorname{Var}\left(\epsilon_i\right) = h\left(X\right) \neq \sigma^2$, what can we know about $\operatorname{Var}\left(\hat{\beta}\right)$?

This question uses the derivations found here. The short version Consider a regression model. If the error variance is a known function of the data (rather than a constant), under what conditions ...
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19 views

Undergrad linear regression topic [on hold]

I have to prepare a mulitlinear regression research paper with cross-section data. I thought doing something on the credit ratings of sovereign debt (of one of the Big3 CRAs) or something similar. ...
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38 views

How to deal with factors with rare levels in cross-validation?

Suppose in a regression analysis in R, I have a factor type independent variable with 3 levels in my train dataset. But in the test data set that same factor variable has 5 levels. Therefore I can not ...
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48 views

Maximum likelihood method vs. least squares method

What is the main difference between maximum likelihood estimation (MLE) vs. least squares estimaton (LSE) ? Why can't we use MLE for predicting $y$ values in linear regression and vice versa? Any ...
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F test in random effects panel regression

I'm using random effects panel regression and I've 3 covariates not statistically significant and I want to test if the three parameters associated with those covariates are jointly equal to 0. Could ...
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How to test for interactions of continuous measure with two repeated-measures factors in R?

I am doing an items analysis of difficulty ratings of a large set of math problems which were constructed to represent the factorial combinations of two binary factors, feature1 and feature2. The ...
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1answer
38 views

2SLS probit vs LPM

I am using 2SLS to estimate the effect of education on the probability that one works. In the first stage I regress education on my instrument and the other exogenous control variables. The same ...
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Correction of data using a correlation

Suppose I have measured the outcome variable A using a (psychophysical) test that determines the ability of a subject to discriminate between two stimuli with a certain difference (the variable X). It ...
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1answer
25 views

Measuring impact of advertising on retail sales

I have a dataset of retail products which contains weekly sales for 12 different items in a single category. For each item, I have three dummy variables representing different types of advertising ...
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41 views

Difficulty plotting regression in R

I am trying to plot a regression using plot() and keep getting the error message "Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ". ...
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Multilevel models benefits vs. separate group models

What are the benefits of multilevel models vs. running a separate model for each group? My understanding is that MLM offer a method to effectively model interactions against all the base predictors. ...
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Assessing the need for random effects terms

During the model selection phase for mixed models, there are typically several possibilities to choose from; in fact, the number of possibilities is increasing in the number of covariates used. How ...
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When adjusting for X1, have we adjusted for X2, to the extent that X2 is related to X1?

I've just read Elizabeth Stuart's paper on matching methods (http://biostat.jhsph.edu/~estuart/Stuart10.StatSci.pdf), which I find very informative. She discusses propensity score methods and the ...
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Help with analyzing/planning a physical experiment — regression

This is a real experiment about to be performed Batches of samples will be prepared. A Striker will be used to see if a reaction will occur. For example: 20 drops might be performed, and number of ...
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I have data independent variable scoring 1 to 4 and a dependent variable number of people dependent variable,,,,,Which regression or model i prefer [on hold]

Access to water , Road , Sanitation, parks etc 1=25% have access 2=50% have access 3=75% have access 4= >75% have access Dependent Variable Number of people in a particular area... Which model or ...
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Analyzing ordered factor vs continuous variable

I have data of 50 students of a class as follows: build : an ordered factor with levels 'low', 'medium' and 'high' score : a continuous variable (values 1-9) ...
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what could be the best method to find best scenario based on revenue(statistical analysis) in python?

I have a data set on which I want to do some statistical analysis. The sample data set is in a csv file and of following nature: ...
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MSE of training set and validation set for linear regression

The wikipedia article on cross validation http://en.wikipedia.org/wiki/Cross-validation_(statistics) makes the claim that "under mild assumptions that the expected value of the MSE for the training ...