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

learn more… | top users | synonyms (1)

1
vote
0answers
18 views

What impact does a higher $R^2$ have on the precision of the CI?

I attended a seminar today at which the presenter mentioned that a higher $R^2$ would, all other things being equal, produce a narrower confidence interval. Is that true, and if so then why? Google ...
2
votes
1answer
22 views

Matching in a linear DiD setup

I am trying to determine (if any) the effect of an economics shock on a number of outcomes. In order to do this, I use the usual difference in differences setup, i.e. estimate a model of the form: $Y ...
0
votes
0answers
24 views

finding confidence interval for any type of regression

I am working on a project where I need to estimate some real-valued values given some features in an online fashion. I evaluate various online learning methods. My estimations has to be bounded by a ...
0
votes
0answers
13 views

The effect of covariate measurement error on coefficients in regression with dummy variables

I am trying to understand if I should be more concerned with covariate measurement error in linear models including dummy variables, than with all continuous predictors. Say I have a simple linear ...
0
votes
0answers
20 views

Purpose of fitting parallel lines

I'm a student. I have a dataset of a set of observations split across three factors/categories. I'm being asked to fit three regression-lines with identical slope (but different intercepts, ...
0
votes
0answers
3 views

sklearn.linear_model.RandomizedLogisticRegression : Handle Categorical Value [migrated]

I want to use RandomizedLogisticRegression for selecting variable for my data set. But the problem is that, One of the feature in my data set is Gender. So it's ...
0
votes
1answer
17 views

Regression Slope and a Bilateral Test

Well, I observe that the standard statistical software package tests regression coefficients if they are statiscally different from zero, that is, not specifically higher nor lower than zero -- a ...
0
votes
0answers
13 views

How to use the bootstat function to bootstrp without replacement? [migrated]

I have a simple MATLAB code to perform a bootstrap, but I need to do this without replacement. How do I write the bootstat function such that there is NO ...
0
votes
0answers
22 views

Comparing r^2 values?

Short version: I have two values of r^2, one a control group (.713) and one for an experimental (.527), and I would like to quantitatively compare the difference in how well/poorly the points in each ...
0
votes
1answer
31 views

Degrees of Freedom Calculation in Linear Mixed Model

How are degrees of freedom calculated for a linear mixed effects model? I was recently running a linear mixed effects model that contained 1 independent variable with 3 levels (1,2, and 3). The random ...
1
vote
1answer
9 views

K-fold cross validation for hierarchical data sets

I'm currently working on a data set that contains a hierarchical data structure (i.e., GPS locations nested within individual animals). Does anyone know how to write R code for this type of ...
0
votes
0answers
9 views

standard error and liklihood for nested parameter estimations

Pursuant to this question, I am attempting to fit the model $$runoff = \beta_0 + \beta_1(antecedent + C*precip)$$ with a breakpoint in $antecedent+C*precip$ I would like to estimate $C$ and the ...
1
vote
1answer
27 views

Time Series Comparison - Correlation and Regression Model

I am trying to see if and how the news for affects the financial markets. I have a time-series for both of them. Should I standardise the series? I have a monthly return on prices from the Dow and a ...
2
votes
5answers
60 views

How to control for categorical variable in regression?

I'm trying to analyze two negatively-correlated variables, A and B (where A is the ...
0
votes
0answers
28 views

First linear regression - interpreting results to guide next step?

This is my first time attempting to build a linear regression model and I am not sure what to do next given the results I have. I have a data set with 24 predictors and 1 response and there are 999 ...
1
vote
1answer
17 views

Linear regression confidence intervals variance assumption in practice

An assumption for linear regression confidence intervals is that the variance is the same for the dependent variable for whatever of the independent variable. If in practice the variance is ...
0
votes
0answers
6 views

Continuous low truncated response in regression

I can't find a clear answer on how to model a regression with a low bounded response. The tipical case is with response variables that can take only positive results. Poisson and negative binomial ...
0
votes
0answers
2 views

Gains Factors in Multinomial Regression Individual-level Forecast

I'm conducting a multinomial regression forecast of academic program enrollment based on individual-level data (i.e. data on students and those who are applying or otherwise indicating interest in ...
3
votes
0answers
25 views

Biased estimator for regression achieving better results than unbiased one in Error In Variables Model

I am working on some syntatic data for Error In Variable model for some research. Currently I have a single independent variable, and I am assuming I know the variance for the true value of the ...
0
votes
0answers
13 views

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 ...
1
vote
0answers
22 views

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 ...
0
votes
0answers
10 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 ...
0
votes
0answers
9 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. ...
0
votes
0answers
16 views

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 ...
0
votes
0answers
25 views

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 ...
1
vote
1answer
29 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.
1
vote
0answers
15 views

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) ...
4
votes
2answers
41 views

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 ...
0
votes
0answers
27 views

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?
0
votes
0answers
14 views

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 ...
0
votes
0answers
15 views

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 ...
4
votes
1answer
127 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 ...
0
votes
0answers
18 views

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 ...
2
votes
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?
1
vote
1answer
24 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 ...
0
votes
0answers
15 views

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 ...
0
votes
0answers
16 views

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 ...
0
votes
0answers
12 views

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" - ...
0
votes
1answer
40 views

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 ...
1
vote
2answers
47 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 ...
0
votes
0answers
24 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 ...
5
votes
1answer
37 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 ...
2
votes
1answer
44 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. ...
0
votes
1answer
38 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 ...
2
votes
0answers
40 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 ...
1
vote
0answers
48 views

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.
6
votes
2answers
226 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?
0
votes
0answers
11 views

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 ...
1
vote
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 ...
0
votes
0answers
16 views

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: ...