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

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Logistic regression : non exclusive predictors

I am doing a logistic regression . My outcome is a categorical (yes/ no) pain after surgery. The predictors i wish to model for includes the type of anaesthesia , among other predictors. The problem ...
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17 views

Linear regression with estimates of error in predictor

I have data with two different kinds of measurements at the same set of $S$ sites. One of these (call it $X$) returns m estimates at each site, which are not necessarily independent of one another. So ...
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30 views

Testing correlation and the t-statistic used in Simple Linear Regression

Given $H_0$ : $\rho=0$ and $H_A$ : $\rho\neq0$, we use the test statistic $t_{n-2}$ , which is $\frac{r\sqrt{n-2}}{1-r^2}$. I have to show that $\frac{r\sqrt{n-2}}{1-r^2}$ equals ...
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54 views

Standard error of regression coefficient without raw data

After searching here: Perform simple regression without raw data I am still curious about this. Is it possible to derive the standard error of a regression coefficient from summary data alone? ...
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12 views

How do I report the weights of the most influential features for logistic regression?

I am currently using logistic regression to compute the probability of some event. I randomly split my training/test data and perform cross-validation on the training data, getting a "best model" for ...
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17 views

Significance codes in linear model with factors

I am setting up a linear model in R and need help understanding the significance codes when one of my independent variables is a factor - i.e., dummy variable for each possible value For a scalar ...
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8 views

Breusch-Pagan Test in Weighted Least Squares

When fitting an ordinary linear regression model, we can conduct a test for heteroscedasticity by applying the Breusch-Pagan Test. We fit the linear model, calculate the residuals, and then regress ...
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3answers
40 views

Why is it the case that when we try to fit an OLS model to a system with more variables than observations, that the residuals are zero?

I am trying to fit an OLS model to some data, where the number of variables $k$ is greater than the number of observations, $N$. In this case, it is obvious that we will have a infinite amount of ...
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21 views

Co-linear covariate in regression what if take one out?

I understand that there are a few ways to deal with correlated or co-linear covariates ( PLS or AIC, and Lasso) But my problem here is: If you have 2 covariates x1 and x2 that are correlated(you ...
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73 views

Bayesian Linear Regression

I have the following question concerning Bayesian linear regression on my machine learning assignment: Consider $f = w^Tx$, where $p(w) ∼ N(w | 0, Σ)$. Show that $p(f | x)$ is Gaussian. I ...
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question about using R to find cofindence interval for the subpopulation [on hold]

I am stucking on several statistic questions. I have no idea which direction should i go. Please help! The variables are x=height of father and y = height of corresponding son. The unit is centimetre ...
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53 views

Causal links with omitted variables

So I have a fairly basic setup, with $X\rightarrow Y$. However, I've run across a potential third variable, Z, that is probably correlated with both X and Y. However, the causality for Z is unusual ...
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How to build the A MAtrix for a A-Model(Amat) after a REDUCED VAR in R

i have a doubt in how to build the A MAtrix(Amat) for Estimating a SVAR model in R: I estimated a reduced VAR with the GDP, interest rate and inflation variables . With the economic theory and the ...
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58 views
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23 views

Prior for the coefficients of a linear regression model

I have a linear regression model $\bf Y=\bf{X}\bf{\beta}+\epsilon$. I want to assign a prior on $\bf\beta$ in order to derive the posterior predictive model $p(y_{predictive}|\bf{y},\bf{X},\beta)$. ...
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1answer
53 views

Bayesian linear regression question

I am doing a problem on Bayesian regression but I'm having a lot of trouble with it. Here is the question: Consider $f=w^Tx$, $p(w)\sim N(w|0,\Sigma)$. Show that $p(f|x)$ is Gaussian. Find the mean ...
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23 views

What to do with this non-normally distributed and near-categorical data?

I have a dependent variable (monetary transfers made by 'dictators' in an experimental dictator game) that is non-normally distributed, and which also appears to be multi-modal (it was collected as a ...
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17 views

How can the relative importance of a categorical variable in a linear regression model be determined?

A simple example can be seen here:http://www.ats.ucla.edu/stat/spss/output/reg_spss.htm Gender is a dummy coded variable. I completely understand how to interpret this variable. I cannot use the ...
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8 views

Linear Regression and error calculation with streaming data [on hold]

How to calculate error when perform linear regression if data is in streaming pattern ?
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19 views

Obtaining adjusted proportions with logistic regression

Can I obtain adjusted proportions of a binary variable by using logistic regression? I have a binary variable (normal/abnormal), which I'd like to obtain adjusted prevalence for (i.e the proportion ...
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14 views

Linear regression with faster decrease in coefficient error/variance?

Suppose we have set of variables $Y$ and $X$, which know are related by a linear relation $y_i=\alpha x_i +\beta$, and important for us is to find $\alpha$ and $\beta$ and the error in estimating ...
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12 views

Metrics for regression error

Suppose I undertake a least squares regression on some data. I end up with a function such as $\hat{f}(x,y,\ldots)=\hat{\beta_0}+\hat{\beta_1}\cdot x+\hat{\beta_2}\cdot y+\hat{\beta_3}\cdot ...
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23 views

How to create an index to compare regression lines

Suppose I have the actual and fitted values of two regression lines. Each regression line is modeling the sales of some good. The fitted and actual values of one of the regression lines is much ...
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21 views

Non parametric clustering by discretizing continuous IV

My final target is to develop a predictive model for a rate (fraction) DV. The DV showed bimodality and I have no variable that separates the two modes. Hence I created an IV using two observed IVs ...
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9 views

Repeated measures ANOVA with custom standard errors

I want to see if there's a difference in size of some cells after several measurements, compared to a baseline (0.5, 1, 3, 6 hours versus 0 hours). I have 3 samples. Each measurement is actually a ...
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1answer
35 views

How is the Akaike Information Criteron applied for model with large number of predictors?

I am reading a paper (details not very relevant) which assumes that the market cost $C$ of a trade is related to $N$ predictors $X_1,\dots,X_N$ (page 25) through a linear relationship $$C = \beta_0 + ...
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8 views

Input normalization effect on Polynomial regression

Here there is a good explanation proving that column normalization does not affect linear regression. But I need to know if this is the same in polynomial regression as well. Thanks in advance,
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43 views

Can I do panel regression if all my covariates are time-invariant?

We have a data set where our outcome of interest varies over 10 years, but the explanatory variable of interest and all of the potential confounders are time-invariant. I am quite certain that a panel ...
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20 views

Inferring on an unknown number of function approximation

I want to ask whether a procedure to do the following job exists (or whether it makes sense for it to exist). First, assume we have $k$ functions $f_1,...f_k$ that have the same domain and range. ...
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test of correlation among binary variables prior to running logistic regression analysis

I am running a logistic regression analysis with binary variables on SPSS: dependent variable: preterm birth (Y/N) independent variables: hypertension (Y/N), diabetes (Y/N), C section (Y/N), ...
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6 views

troubleshooting R BayesFactor package - error in regressionBF [on hold]

I want to use regressionBF to run all subsets regression. Here is my code: fitness.bf = regressionBF(VO2 ~ ., data=fitnessdata) and here is the error it spits out when I try and run the code: Error ...
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prediction with string similarity

Assume we have an input of an email and we want to predict if it is spam or not spam. Without being a statistician, i would think one of the predictors takes the subject of the input email and ...
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21 views

Estimation of a system

Suppose we have a system that essentially evolves as follows: ...
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67 views

What criteria tell us that the prediction of a model is reliable

What criteria can be used to tell whether the prediction of a model will be more reliable than other specifications. Background: We have data with $N$ computers. However, prices available only ...
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28 views

Find distribution of Bus arrival time

I am currently working on a problem in my research which can be modeled into the following question: Let's say I have a rich dataset with values for the variable $A$ which is equal to ...
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10 views

Machine learning/random forests with noisy response data

Machine learning techniques like random forests seem to assume that the responses in the training set are known perfectly. Specifically for regression applications, it seems one needs to account for ...
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1answer
28 views

What is the connection between regularization and the method of lagrange multipliers ?

To prevent overfitting people people add a regularization term (proportional to the squared sum of the parameters of the model) with a regularization parameter $\lambda$ to the cost function of linear ...
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1answer
27 views

Gaussian Process: Using partitions of a Cholesky decomposition solution for conditional deduction

If I define a GP over observed values, $y$, of a sensor reading over time, $t$, as (for simplicity assuming discrete time series e.g lets say readings after every 5 mins) : $y=f(t)+\epsilon$ where ...
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Adjusting for mediating variables (everyone does it!)

I have always learned that when doing multivariable regression, one should not adjust for mediating variables between the independent and dependent variables of interest. I have seen in the academic ...
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1answer
24 views

What to make of countervailing spatial regression coefficients?

I am running regressions across a country's counties (N about 300). I divide the country in two regions A and B to control for potential unobservables. My explanatory variable varies at the county ...
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1answer
41 views

Finding significant predictors of psychiatric readmissions

The set of data I am working contains nearly 17,000 independent spells (each spell consists of a number of hospital episodes) each belonging to a unique patient ID. I have spent a very long time ...
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147 views

Do you see trends in my residual plots?

Do you see trends in my residual plots? These residuals plot show the standardized residuals against fitted values, origin period, calendar period, and development period. The patterns in any ...
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1answer
35 views

Principal Component Regression with an additional factor

I am looking to tease out the significance and contribution of a particular variable to 2 different continuous responses. I have 7 continuous variables I know to be influential on the two responses ...
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8 views

Does it make sense to report equally-fit, more complex, model, if it fits better a theory?

I have two (logistic) regression models for which the deviance is not significantly distinct (p = 0.7). One of them has education, gender and age explaining variable Y. In the other, I have added a ...
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31 views

Regression with Measurement Errors in X and Y

I am trying to find the equation of a line that best fits my data. However, I have errors on the X and Y data points. Here is what my data points look like: ...
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1answer
49 views

S-curve in residuals plot: a problem?

I am doing some linear regression and am predicting a absolutely non-normal dependent variable (for context: we are forecasting the amount of units sold for a shop). Therefore, we have transformed the ...
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Estimate Probability of being in a time interval

​You arrive at a bus stop in an unfamiliar part of town. Assume that buses arrive at the stop with an unknown (to you) distribution and wait in the bus stop for a few ​minutes. The wait time ...
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Regression with multiple observations of the independent variable for each dependent observation

Let's say I have data where y is measured at some higher level than x. For instance y represents a State level outcome over time and x represents a county level data. (Additionally, the data is such ...
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Prediction based on multiple time series - Python

I have 3 predictors and 1 variable that represents ground truth. They all are linked time series. My purpose is with the 3 predictors to try to forecast the ground truth data. For example : ...
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Prediction on multiple regression - Python

I have 3 list of value and 1 ground truth data. They all belongs to the same time series. My purpose is with 3 list try to forecast the ground truth data. For example : ...