Questions tagged [regression]

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

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34 views

What is the role of an intercept in an I-Spline basis?

I have the following data to which I want to fit a monotone non-decreasing spline. ...
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6 views

Heterogeneity in residuals

I am very new in here, but I will try my best to create a good question. First of all I am doing some regression on Fama and French 3 factor model and an asset. Thus I am doing a OLS regression, using ...
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9 views

How to interpret a significant effect present in a subset data but absent in the full data

My model has 3 predictors: Group (A vs. B) Condition (baseline vs. treatment) Memory (a continuous variable) The model gives an interaction between Condition and Group. I then tested the simple ...
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2answers
14k views

Does cross-validation on simple or multiple linear regression make sense?

Does it make sense to apply train-test split or k-fold cross-validation to a simple linear regression model or multiple linear regression model? I'm really confused about this because I saw this ...
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8 views

How to interpret gamma deviance?

My target variable, sales per day, is skewed to the right ( many values are closer to 0 than to the maximum value of 4000). Running the model in Data Robot, the metric recommended was Gamma Deviance. ...
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3 views

Interpreting markedly large Logodds in logistic regression?

I am running a logistic regression in R and for some reason, some of my outcome variables o a ridiculously high log odds (ie -14), which corresponds to a p value of 0.9. But this does not make sense ...
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8 views

Regression of differences in differences proof

Is there anyone familiar with regression of differences in differences and could help to understand this? Could someone in normal human words explain to me how I should read and understand $Yi$? I ...
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1answer
13 views

Multiple linear regression with very large cost

So, I'm trying to enter the Data Science world but struggling with a very simpel exercise. I'm using a dataset to get personal medical costs from a bunch of personal info. The database look like this: ...
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1answer
50 views

Is there any paper about applications of Deep Metric Learning on regression problem?

I'm trying to solve a problem in the field of transfer learning, more specifically, domain adaption where both the source domain and target domain are labeled. Basically it's to predict the ...
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1answer
351 views

What is Factorial Design in the context of Linear regression

I have been trying to understand the concept behind factorial design and its importance in connection to linear regression. I will be glad if somebody can give a clear and tone down explanation in ...
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1answer
15 views

Robustness check for cross-sectional data by merging data sets and creating year dummy variable

I am currently working on the effects of maternal education on child mortality with cross-sectional data. I got data sets for 2008, 2010 and 2014. I am thinking of doing a robustness checks and I ...
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14 views

Different regression models in one research project?

I'm working on a research project on alcohol behaviours where I am looking at 2 outcomes: 1) number of drinks; 2) preference (5-point Likert scale, ordinal). There are two things I'm confused about: A)...
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2answers
2k views

Least-square fit with uneven distribution of data

I'd like to perform least-squares fit to data which is unevenly distributed on the x-axis. For example, if I was to bin the data, it would be something like x = 0~5: 10 data points x = 5~10: 20 ...
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1answer
501 views

Cumulative hazard in the setting of Cox regression of repeated events

Cox regression is commonly extended to estimate repeated events processes (for a quick review see [ 1 ] and [ 2 ]). In Clark et al's first article in their excellent review series of survival ...
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4 views

Probability Graph Model (PGM) and Linear Model (LM)

I was learning PGM recently and wondering if all linear models can be put into the framework of PGM. We know that the linear model is the most important tool in statistical analysis. It can be ...
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11 views

Difference between Chow test and F test

I have the following model: Yi=Beta0+Beta1Xi+Beta2Di+Beta3(Di*Xi)+Epsilon(i) where Di is a dummy variable I want to understand if it is desirable do keep the variable Di inside the model (if it has an ...
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15 views

Predicting a value based solely on Correlation Coefficient

Let me set the stage. We are dealing with two variables; $A$ and $B$. We can easily obtain $A(x)$ for a specific data point $x$. $B(x)$, on the other hand, is very difficult to know. We know Pearson'...
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1answer
65 views

Reference for doing linear regression with mean absolute deviation?

I am looking for a resource that goes over how to derive the coefficients for a linear regression model while minimizing the mean absolute deviation. I am hoping for both a mathematical and ...
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56 views

Techniques for comparing regression models

I am looking for a technique to deal with a simple problem. Assume I have two groups: men and women. For each group, I have a measure of average happiness for each day. For example, men are happy 10.2 ...
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20 views

Least Absolute Values Regression

I have the data: x <- c(0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0) y <- c(2.06, 2.12, 2.32, 2.02, 2.76, 3.04, 2.83, 3.15, 3.36, 3.68, 3.96) I ...
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1answer
228 views

Stationarity and Ergodicity - links

In time series analysis stationarity and ergodicity have different definitions and meanings: https://en.wikipedia.org/wiki/Stationary_process https://en.wikipedia.org/wiki/Ergodic_process Essentially ...
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2answers
61 views

In a regression problem, having a variable highly correlated with our target messes up the optimization of the parameters?

In a discussion with a colleague, she told me that if a variable X_i in our design matrix (X) is highly correlated with our variable of interest (target, y, etc), it will make the regression unsovable ...
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11 views

Choice of statistical testing - under five mortality rate

I am not sure if this question or similar one has been asked before but I have checked and to the best of my knowledge couldn't find a suitable answer. Here is my confusion: I have a dataset with ...
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16 views

Different length scale per dimension in TensorFlow

I would like to know how to use a different length scale in a kernel for each dimension of the input. For instance, take an input of dimension 4, I would like to use 4 different length scales where ...
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1answer
26 views

Estimating survival curves from Cox regression results

I can understand that it is possible to estimate survival curves directly from the results of a Cox regression. The way it can be done, mathematically, is furthermore very nicely explained in this ...
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27 views

Confusion about coefficients of logistic regression model produced by glm(family = binomial) in R

I have a response variable that is binary ( 1 - recovered, 0 - dead) and I will use two predictor variables as an example. One is continuous (age) and one is binary ( condition x: ( 1 - patient has it,...
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19 views

What does nan mean in Logistic Regression output

I made my first model using Logistic Regression in Python. I'm using some dummy variables as predictors, but it resulted all nan value in the summary, except coefficient. Is this meant to be? If not, ...
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6 views

What is the theoretical difference between fiting linear regression models to many individual cases, and fitting a panel regression model?

What is the difference in say, fitting distinct regression models to 100 different individuals, each with their own time series and relevant data and then taking the average of the coefficients, vs. ...
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7 views

Compare regression estimates between trial types and subjects

Hej everyone, I recently read a paper where 20 subjects choose between two gambles in two contexts (1 and 2) repeatedly. Each gamble was defined by a probability of winning and a magnitude that could ...
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14 views

Dummy variables and chow test: which model to chose

I have this model: Yi=Beta0+Beta1X1+Beta2Di+Beta3(Di*Xi)+Ei (where Di is Dummy) Now, with the Chow Test I want to understand if it is desirable do estimate two ...
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1answer
28 views

Is there any example of 2-step predictive model?(i.e. classification model coupled with regression models for each subclass)

I have a large dataset with 10,000+ individuals and many many biological features (>5000). And I want to use these features to build a linear model (e.g. elastic net) to predict their clinical ...
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6 views

Question regarding explained variation of individual regressors

If you regressed a dependent variable on multiple independent variables, would it be possible to figure out how much each regressor is explaining the variance of the dependent variable? Or can you ...
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1answer
2k views

Alternative to Cross validation

I have implemented different cross validation algorithms, like CV and GCV, and they are working perfectly. I have to run it on an arm processor, and the time of calculation is very high. Since the ...
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1answer
39 views

How to scale interactions in regression (quantitative*qualitative)

Supose I have two variables in a model, and their interaction, like this: lmer(response~x1+x2+x1*x2+(1|time), data=db) If x1 have a very big scale (like a city ...
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10 views

How to adjust for confounders in multinomial logistic regression models in R?

I want to test the effect of diet intake (protein, prot, and carbohydrate, carb) on disease occurrence using Rstudio. Confounding variables are age, sex, and another variable y. ...
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1answer
16 views

Best way of setting up dummy variables in multiple regression model?

I have a regression model in which the independent variable correspends to a signal over time. If at a certain point in time a specific event occured, I introduced a dummy variable into the model ...
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9 views

Negative binomial assumptions and errors

I'm conducting a negative binomial regression in SPSS using the GLM menu and I'm receiving the following error message: Warnings: The Hessian matrix is singular. Some convergence criteria are not ...
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28 views

How do I denote a linear regression with AR - correlation?

We can fit linear models and add correlation coeffitients. In the example, x is the dependent variable, which was measured during a time series: ...
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1answer
30 views

Covariance of $x_i$ and $\hat{\varepsilon}$ without exogeneity of $\varepsilon$

What is the $Cov(x_i,\hat{\varepsilon})$ if $E(\varepsilon_i|x_i)\neq0$ in a univariate regression model? Can the expression be simplified further beyond $\sum(\hat{\varepsilon}_i-\bar{\hat{\...
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2answers
47 views

Bias variance tradeoff of ridge regression with independent but non identically distributed error?

I am trying to figure out how the solution for ridge regression changes when the error term is independent but NOT identically distributed such as $\mathbb\epsilon = \mathcal{N}(0, \Sigma)$ rather ...
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1answer
27 views

Strange, symmetric suppression between 3 IVs in multiple regression?

I have encountered an interesting phenomenon while working on some data derived from a(n admittedly badly constructed) questionnaire by a colleague, and I have no idea what to make of it ...
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3 views

randomised controlled trial in regression

I am a bit confused by the assumption of randomness in regression. Taking the effects of maternal education on child mortality, is the random part referring to 1. randomly selecting respondents, or 2. ...
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2answers
414 views

What does Penalize a learning algorithm mean in Machine Learning?

I am new to Machine Learning and have taken Andrew Ng's course on Machine Learning. In one of the Logistic regression videos for binary classification for the error case where predicted value through ...
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1answer
14 views

How can i quantify the infuence of the independent variables over the dependent variables in linear regression with more variables than observations?

I have a dataset that has 1216 columns and 104 observations. I want to somehow quantify numerically, how much each of the columns influence a change (drop or raise) in the value of the target variable,...
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9 views

Can someone tell me whether my data meets the assumption of linearity, normality and homoscedasticity (multiple linear regression assumptions)? [duplicate]

I have a feeling it meets the assumption of normality. However, I dont think it meets the assumption of homoscedasticity. If it doesn't meet this assumption I am not sure what to do. I do not also ...
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17 views

interpreting multiple logistic regression p-value when variable is not normal

I know there has been a similar question posted before Why do my p-values differ between logistic regression output, chi-squared test, and the confidence interval for the OR? but im still not sure ...
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2answers
367 views

Linear model with both additive and multiplicative effects

In linear regression, the independent variables have an additive effect on the response (level-level regression): $y=\beta_0+\beta_1x+\epsilon$ In a log-level regression, the independent variables ...
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13 views

Using the Hat Matrix to detect influential observations in logistic regression

I'm currently running residual diagnostics for a logistic regression model, aiming to identify possible influential record could influence the parameters estimate. I wonder about if it is possible to ...
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2answers
5k views

Difference Endogeneity and Multicollinearity in Logistic Regression

I am right now working with logistic regression and test my model over and over again. However, I am still not sure about the terminologies endogeneity and multicollinearity. For my under-standing, ...

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