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

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3 views

Cox model with a predictor variable within only one stratum

I'm investigating survival in a cancer patients cohort. The Cox model I'm using is stratified by stage and adjusted for several variables. I would like to add one variable RT (Radiotherapy) which is ...
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0answers
11 views

Algorithms for everyone

Something I've allways wanted to see is a concise run-through of different machine learning algorithms, all on one page: With their pros and cons, what situations they work in best and when they don'...
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0answers
4 views

Shows negative value in reliability test

How to test reliability in nominal data? MY dependent and independent variables are both in ordinal data. I have to find out the contribution of independent variables in dependent variables. is it ...
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0answers
7 views

confusion with frisch lovell type situation

Suppose I am trying to run a regression as follows: $y= a_1+ b_1*x_1 +b_2*x_2 + e_1$ To study the relation between $y$ and $x_2$ net of their correlation with $x_1$, I run the following model first: ...
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1answer
8 views

Regression on subset of observations. Is this valid?

I have a dataset that compiles voting results for transportation referendums. Each observation is a city that has held a referendum. I am interested in the community factors contributing to support so ...
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0answers
5 views

Intuition: What is the difference between linear factor models and regular linear regression?

So, I have a very vexing theoretical question that I hope some experienced econometricians can help me with. Being in finance, I have recently been exposed to linear factor models, which are models ...
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0answers
11 views

Independent variables in truncated Weibull distribution

I am relative new to statistics and currently working on a problem about crash rate analysis. It is appears to be a zero-inflated scenario, then I decide to use the hurdle model. The first part will ...
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0answers
15 views

Methods to demonstrate time series causality?

I am working with a team to understand the relationship between two processes that are hypothesized to have a relationship but which occur simultaneously. The hypothesized relationship is that action ...
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1answer
32 views

How to interpret the importance for a regression coeffcient in Bayesian regression from its posterior density?

I am trying to interpret the regression coefficients of a covariate in a Bayesian linear regression problem. More specifically, I am trying to determine if the regression coefficient have an important ...
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0answers
18 views

Divergence of regularized gradient descent [on hold]

I am applying a regularized gradient descent algorithm on a dataset for linear regression. Since there are too many features, I am programming using the matrix notations. Following expression is being ...
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0answers
28 views

Finding the worst mobile phone by data mining

we have about 50000 models of mobile phone in database. the size of data is about 5 million. we want to find the mobile phones that have the least call success rate ( the numbers of successful call ...
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0answers
6 views

balance_classes in H2O, but for regression?

I am training with deep learning for regression in H2O for R. My dataset is unbalanced (ie. not evenly distributed). There has been discussion on whether unbalanced datasets are an issue or not, with ...
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0answers
10 views

Difference in differences model with time-varying continuous treatment

I am trying to estimate a non-standard DID model. There are two time-periods, pre and post treatment. All units are treated in period 2 yet some more intensely than others. Suppose this variance in ...
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0answers
8 views

Regression of cointegrated variables, serial correlation

I have a response variable $Y$ and a series of predictor variables $X_1, X_2, ... X_n$. All are $I(1)$. I found that $Y and X_1, X_2, ... X_n$ are cointegrated. To estimate coeffcients of $Y ~ X_1,...
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1answer
41 views

Simple Linear Regression: how does $\Sigma\hat{u_i}^2/\sigma^2$ follow chi squared distribution with df (n-2)?

My question is, as far as i am aware, 1. the residuals($\hat{u_i}$) are not independent of one another 2. the variance of ith residual is $\sigma\{(1-1/n-(X_i-\overline{X})/\Sigma(X_i-\overline{X})^2\}...
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0answers
24 views

How to input a continuous distribution to a neural network

I have simulated the relative frequency of a stochastic process by creating a very small grid say $1000$ by $1000$. The graph looks like this Now I am trying to setup a regression model by ...
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0answers
18 views

Can I use Survival Analyses if there are gaps?

I am interested to learn if it is possible to use a survival analyses approach when, in the middle of the study, there "gaps." Specifics: does oiling a motor increase the "time until death" or ...
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0answers
23 views

How to fight imbalanced data in regression task? [on hold]

Suppose a reggression task, where solution space is [0..1]. But our dataset has more examples of solutions closer to zero, than to one. I am training a neural network. It is biased to predict numbers ...
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0answers
14 views

Finding when an external effect appears in time series using regression analysis

I have the 'seen' data (post views, PV) of different social media channels over a period of time and I want to see whether the effect of an external factor (EF, for instance, internet accessibility) ...
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0answers
17 views

How to chose covariates to adjust for in a regression analysis

I am running a regression analysis where my primary interest is to see if the outcome differs by the group (treatment vs. control). However, I have some 80 other clinical and socio-demographic ...
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0answers
38 views

XGBRegression - how penalize negative resid more than positive?

I'm using a XGBoost for building a regression model. The model is predicting price (continuous value). ...
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2answers
96 views

Why is linear regression overestimating small values and underestimating big values?

I am trying to predict age from a couple of variables using linear regression, but when I plot predicted age against real age, I can see that small values are significantly overestimated and big ...
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0answers
26 views

Which test should I use? Help!

I'm new here in the group, so I do not know if I'm putting my doubt in the correct place... :) I am conducting my master's thesis, and I came across a problem in the analysis of my results... In my ...
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0answers
15 views

Why after WLS I still get the same result of homoscedasticity tests?

I generated such a sequence x <- seq(1:64) y <- 101 + x + rnorm(64, x, 3*x^(2/3)) df <- data.frame(x, y) then I did the regression and checked for ...
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1answer
55 views

Interpretation of coefficients from glm Gamma

I am attempting to fit a model to a dataset with frequency (Hz) is the dependent variable. Using a generalized linear model based on a gamma distribution seems appropriate since the values of the ...
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0answers
12 views

Finding relationship between Package Holiday prices and Holidays per month

I have a dataframe that shows the Package Holiday prices (as an index) and Holidays per month. This is an example of how the dataframe looks like. You have the: 1) Dates which are the index, in ...
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1answer
15 views

Cook's Distance result: Does not make much sense

I am performing regression on the financial data (dependent variable is the MSCI AC World Financials and independent variables are the MSCI North America Financials, MSCI Europe Financials, MSCI ...
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0answers
26 views

How to get a regression line where y predicts x? [migrated]

So what the title says. I have two variables and looking to plot both their regression lines on one plot: lm(y ~ x) where x predicts y lm(x ~ y) where y predicts x I figured out how to do the first ...
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0answers
18 views

Looking for a freely accessible package to select model for regression [on hold]

I work in an engineering field. I have been given a supervised learning problem, for a given set of inputs I have to find the best prediction for a single output. We have software (TableCurve 3D) for ...
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0answers
27 views

Regression coefficient estimate bounded as a function of the error covariance

Consider the linear regression model with one non-stochastic predictor: $Y = x \beta + \varepsilon$, where $Y \in \mathbb R^n$, $x \in \mathbb R^n$, $\beta \in \mathbb R$, and $\varepsilon \sim \...
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0answers
7 views

Is it sufficient to normalize the input Data to perform a multiple Output Regression where the labels have different magnitudes?

I am trying to simplify a complex mathematical model in a certain range by performing a regression with a neural Network. I am using a hidden layer with a 'tanh' activation function to normalize my ...
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0answers
12 views

Interpreting odds ratios generated from polr model with both factors and ordinal predictors (Using R) - Note, reposted as user not guest)

I'm working with a large data set of students who have responded to a survey regarding physical activity levels and a number of other measures. I have several models I want to look at to consider the ...
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0answers
4 views

How to incorporate Fama and French three-factor returns in cross-sectional multiple regression model?

I have a follow -up question about the use of Fama and French three-factor model returns as control variable in a cross-sectional multiple regression: https://quant.stackexchange.com/questions/35016/...
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0answers
8 views

Regression similar to BWW-ANOVA

I've run a 2*2*2 Mixed ANOVA with one between subjects (Group: HighScore/LowScore) and 2 Within Subjects factors (Factor1 and Factor2) All Factors have 2 levels. I'd now like to run something ...
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0answers
16 views

Methods of analysing gender disparity amongst bonus awards

I've been asked to do some analysis of bonus awards made in a firm of accountants to determine whether gender is a factor, and if so, how much of a factor. The company is medium sized: about 300 ...
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5answers
2k views

What is the need of assumptions in linear regression?

In linear regression, we make the following assumptions The mean of the response, $E(Y_i)$, at each set of values of the predictors, $(x_{1i}, x_{2i},…)$, is a Linear function of the predictors. The ...
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0answers
31 views

Calculation of the coverage of variable selection methods

I'm having a problem with understanding the coverage calculation for some variable selection method. Suppose that we want to estimate the coverage for some regression coefficient $\beta_1$ of ...
5
votes
1answer
80 views

How to account for multiple measurements of same person in either two-group comparision or regression?

I am running analysis on clinical data collected from patients which are correlated either by time (longitudinally) or more commonly different measurements of the same person at same time (eg. ...
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0answers
20 views

How can I use equations of several averages of the same data derived by Excel cross-plots? [on hold]

I'm conducting a research in which a large set of data were used (picture 1). To enhance the correlation coefficient and learn about the trend, I grouped the initial large set into averages of fixed ...
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0answers
7 views

Exogenous Switching Treatment Regression

Can anyone explain what this term means? I was reading the paper "What Determines Gender Inequality in Household Food Security in Kenya? Application of Exogenous Switching Treatment Regression" https:/...
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0answers
15 views

How to understand a before-after effect in a longitudinal medical dataset

I am after some suggestions on what statistical analysis I can perform to show a before-and-after effect in a longitudinal electronic healthcare record (EHR). I have N number of EHRs, of varying sizes/...
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0answers
149 views

Model for predicting chance of winning in variable count of opponents

I have dataset with horse racing results including bookie odds - converted to percentage chance of winning. Data are stored in relation tables. The basic entity relation is described on image. Each ...
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1answer
30 views

What's the definition of “Dynamic Regression Models”?

I am trying to learn about Dynamic Regression models. However, the sources on the topic is (relatively) few compared to other TS topics, and so I cannot really get a grasp of where to start. I really ...
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1answer
25 views

Is dummy coding required when spacing is meaningful (number of days)?

I apologise if this has been asked before. If so, please point me the right way. However, I have had a look and cannot find an appropriate answer. I am attempting to fit cumulative logit models ...
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0answers
6 views

Converting standardized regression coefficients to metric coefficients

I'm trying to create a multiple regression model for some basketball-related data and am struggling with interpreting the results of the standardized model practically. I've got four team metrics -- ...
1
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0answers
30 views

Bayesian linear regression on complex : how to use the prior laws and more

My model is as follows : With $y\in\mathbb{C}^{40},A\in\mathbb{C}^{40\times10},x\in\mathbb{C}^{10},b\in\mathbb{C}^{40}$ : $$y=Ax+b$$ $y$ and $A$ are known and I have a normal prior law on the module ...
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0answers
13 views

Mixed models for zero-inflated count data in R?

I have a dataset containing scores on a measure of uncommon experiences. The scores are derived as a count of the number of items rated as present divided by the number of items that were answered by ...
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1answer
24 views

Does it make sense to use a dropout layer in a neural network for a regression to predict an absolute Error?

I am working on a regression problem where I try to predict an Error with a NN with as little calculation steps as possible. Currently I have an input layer consisting of 21 Neurons and a Dense Output ...
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0answers
11 views

Cluster points in regression

I am trying to cluster data for a regression problem and wonder if I am way off in my approach or if there is something in it. Problem: make a model of impact of variable L1 and L2 in Output. Output ...