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Questions tagged [regression]

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

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2answers
60 views

if there's no noise, is overfitting possible?

Just what the title says. In every model we try to approximate a function $$Y = f(X) + \epsilon$$ Assume we that $Var(\epsilon)=0 \forall X$. However, the train/test set correspond to different sets ...
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1answer
30 views

Gaussian Processes, basic question about how the prior is computed

I'm approaching the topic of GP, and I have a question regarding how functions are sampled. On my textbook is stated that to represent a distribution over a function (the prior): we only need to ...
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0answers
35 views

Which one of these is correct for linear regression?

Only one of these is supposed to be the correct one for simple linear regression. Which pair of plots would you say has constant variance and normal distribution? I feel like none of them have both ...
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0answers
24 views

GAM residuals , GAM check

I am doing my GAM regression analysis in R and by using the gam.check() function from the ...
2
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0answers
14 views

How do you interpret “explained” coefficients in Blinder-Oaxaca decomposition with considerable negative values?

For illustrative purposes, consider the example given on p. 473 of Jann (2008). However, instead of the difference and coefficients noted, let's assume the difference and coefficients were the ...
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0answers
14 views

Logistic Regression(multi class) accuracy too small

I try to solve a problem with 3 features and 6 classes(label). The training dataset is 700 rows * 3 columns. I use one-Vs-all method, but I do not why the prediction accuracy is too small, just 24%. ...
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1answer
19 views

Should I adjust for family-wise error when doing many logistic regressions and t tests

In my biomarker analysis, there are 100 biomarkers. Each biomarker is measured at 4 different time points. I'm looking for any biomarker that is associated with outcome variable at any timepoints. ...
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1answer
22 views

Multinomial Logistic Regression: small groups

I am implementing a Multinomial Logistic Regression, but I am encountering the possible issue of having very small groups when I create a frequency table of the dependent variable Y and one of the ...
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1answer
21 views

How to obtain the inverse of the variance covariance matrix of GLS (Random Effects Model)

In the standard GLS set up how do you find the inverse of the variance covariance matrix? $$y _ { i t } = \beta _ { 0 } + x _ { i t } ^ { \prime } \beta + \alpha _ { i } + u _ { i t } \hspace{35pt} u ...
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0answers
36 views

How to model time series and to find corelation between times of events

I have data set that track daily activity of user. User repeat action one or more times during a day. This actions are independent and I want to determine if there is relation between times of events. ...
1
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1answer
49 views

Linear Model (on X or in $\beta$?)

I'm well aware that when we use the expression "linear model" we are actually making reference to models that are linear on the parameters $\beta$. And because of that any polynomial regression will ...
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1answer
20 views

Does a panel regression model make sense for my data?

This might be a bit of a newbish question, but I recently picked up a forecasting project at my job, and I'm trying to figure out whether it makes sense to run a panel regression like a Fixed Effects ...
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0answers
26 views

Linear regression Interaction Quadratic terms

In linear regression, if I have dependent variable Y and independent variables A and B, where B has a quadratic relationship with Y, and I'm looking for a possible interaction between A and B, I would ...
1
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1answer
29 views

Linear regresson with four values as input and two values as output

I have the following problem for a personal project of mine: I am solving a system of two differential equations that has 4 changing parameters. The output are two vectors of numbers. Let's say I am ...
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0answers
40 views

Regression model bias [closed]

I know in the case of regression analysis, when we are studying the relationship between the dependent and independent variable, we are also checking for bias. If the error term is correlated with ...
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1answer
51 views

Curved regression lines

I had already asked a similar question here, but I'm experiencing the same problem for a different data-set and for a different family of mixed models. My response variable is a binary outcome of ...
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0answers
17 views

Regression on a change between a variable before and after?

In my dissertation I am using a paired sample t test to see if there is a statistically significant difference between consumers' intention to purchase from and attitudes towards purchasing from a ...
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0answers
33 views

Linear project when X includes a constant

In Hamilton's text I ran across the following statement: if $X$ includes a constant then the linear projection of $aY+b$ is $aP(Y\mid X)+b$ where $P(Y\mid X)$ is the linear projection of $y$ on $x$. I’...
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0answers
20 views

Time Series of Wound Healing

I am struggling with how to approach my dataset for a meaningful analysis and need help. I am looking at wound healing of sharks over time and have hit a wall with analysis. My data consist of ...
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1answer
12 views

Amount of data needed for a statistically relevant regression analysis

Trying to determine if a logarithmic regression analysis is valid. With good R2 values, does it matter that there are ony 7 data points?
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0answers
18 views

How to find proper weights for summation of 3 dependent variables

I have time series for 3 variables, say energies from 3 sources (e1, e2, e3). I have checked their dependency using the calculation of the correlation of each pair and found that their dependency is ...
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0answers
17 views

What is an intuitive way of explaining how to set Hyperparameters for Regularization? [closed]

What is an intuitive way of explaining how to set Hyperparameters for Regularization? How would these hyperparameters change for L1 or L2 Regularization? In Python I have seen np.logspace() used to ...
3
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1answer
59 views

Do GEE and GLM estimate the same coefficients?

In a GLM, the likelihood equations depend on the assumed distribution only through the mean and the variance. The likelihood equations are $$\sum_i^n (\frac{\partial \mu_i}{\partial \eta_i}) \frac{...
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1answer
18 views

Logistic regression F value

I have a question that I’ve been struggling finding the correct answer to. I wonder if you can help? I am performing a logistic regression with multiple variables,only one variable is statistically ...
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0answers
25 views

What is the meaning of correlations between regression coefficients?

I understand that coefficient correlations can arise in the presence of correlated covariates, essentially indicating that our inference for those parameters is coming from information that cannot be ...
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0answers
18 views

With omitted variables is OLS estimator still the best linear predictor?

Suppose the true model is $$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \epsilon$$ where $x_1$ and $x_2$ are correlated and $\epsilon$ is white noise. I omit variable $x_2$ and apply OLS to estimate $...
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1answer
25 views

Regression Analysis for Customer satisfaction [closed]

I want to see how Customer satisfaction is related to the following variables (survey metrics): Average response time, Drop off Rate, Click through rate, Number of Active users etc, Number of surveys ...
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0answers
23 views

How should clustering be accounted for in logistic regression, when there are very few clusters?

I have survey data from 1000 patients. This is a convenience venue-based sample. In a specific city, at 9 hospitals that happen to have a psychosocial program, patients can opt into the program if ...
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1answer
16 views

Automated variable selection with unique variables

I have a dataset which contains areas covered by different landuse variables such as agriculture, forest, grassland etc for different spatial scales. The spatial scales that I have used are 30 m ...
1
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4answers
212 views

Regression with Lots of Categorical Variables

I'm facing a regression task with many categorical and few numeric features. I encoded them into dummies and removed the first dummy column for each feature. I am not getting very good R2 at all. I ...
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0answers
14 views

Confused about hyperparameter selection for elastic net regularization using glmnet

I am following the glmnet tutorial here and confused about the statement: We see that lasso (alpha=1) does about the best here. We also see that the range of ...
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0answers
20 views

Stata Logit model - sample size and clustering question

I would like to check if there is a relationship between mothers' fertility and grandparental childcare. I am using a cross-national survey dataset. I restricted the sample to 6 countries of interest ...
0
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1answer
26 views

Is it normal for the intercept to have a 1600 Variance Inflation Factor (VIF)?

I'm using Python's module to calculate the VIF for my variables to be used in a binary logistic regression. I'm completely following this post to do this: https://etav.github.io/python/...
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0answers
13 views

question about the logit model for credit risk

i have this question in one of the past exams . Discuss which model you would choose to calculate the probability of default of corporate firms and give a rationale for including OR excluding the RE/...
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0answers
30 views

Parameters for regression linear model for two variables? [duplicate]

I made regression for two variables, one is independent (time of first pizza) and dependent (second is number of pizza that day). Trying to determine if there is connection between time of first pizza ...
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2answers
89 views

Regression as mutual information minimization

I am trying to see if mutual information can be used as an objective function in a generalized formulation of the linear regression without the normal distribution assumption for the residual error. ...
2
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1answer
72 views

regression coefficient in the poisson model [closed]

When we are dealing with count variables we are told not to log transform our data but to instead use a poisson regression. I was wondering.. when it comes Poisson regression, the common formulae is :...
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0answers
7 views

On dichotomizing a continuous variable and how it affects MSE of OLS

Recently I have learned about the practice of dichotomizing a continuous independent variable (or maybe even discretize it into more than 2 categories), and then run a predictive model (e.g. multiple ...
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0answers
28 views

How to do a linear regression if the independant variable will have different sample sizes?

I have time based data that is stochastically fluctuating in lower time resolutions. Moving to higher time frames, I expect to have non-stochastic behavior (that could e.g. be trend driven). I would ...
0
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1answer
17 views

Catboost Regression. Function Extrapolation

I'm new at ML and have a problem with catboost. So, I want to predict function value (For example cos | sin etc.). I went over everything but my prediction is always straight line Is it possible and ...
1
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1answer
65 views

What type of regression should be used in predicting Click Through Rate?

I'm looking for a model to predict CTR (click-through-rate) I have the following data: For each ad I know the number of impressions, clicks and some other attributes (which are mainly dummy variables)...
1
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1answer
29 views

Finding the appropriate polynomial fit in Python

Is there a function or library in Python to automatically compute the best polynomial fit for a set of data points? I am not really interested in the ML use case of generalizing to a set of new data, ...
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0answers
23 views

Fitting multiple polynomial regression

I hope someone could advise to interpret and report outputs of the multiple polynomial regression fit. I am trying to do a simple sensitivity analysis of an empirical threshold-based ecological model ...
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0answers
17 views

Regression of multiple combinations of states

My problem is solving a variation of the Potts model. Where the equation in question is $\Sigma(\sigma) = \Sigma(h_i(\sigma_i)) + \Sigma(j_{ij}(\sigma_i, \sigma_j))$. I am currently working out the ...
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0answers
11 views

Difference between multivariable linear regression and polynomial regression? [duplicate]

Is one is a special case of another.Give detailed explaination.
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1answer
48 views

1D CNN for time series regression without pooling layers?

I am working on a prognostics task, where I predict the Remaining Useful Life of some equipment (i.e.: time steps remaining until failure). In order to do that, I use multivariate time series sensor ...
1
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1answer
27 views

Model Quasipoisson interpretation and validation

I am currently doing my Master thesis with evaluating my results in R. I am stuck on my analysis of my glm with quasipoisson. I am analysing influencing variables on the dormouse abundance in 2 types ...
1
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2answers
34 views

how (logistic regression, random forest) deal with input zero values?

I am new in ML. In my dataset there are 11 of 21 features that have some zero values. what is the impact of having zero values as input when using logistic regression or random forest to train my ...
7
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4answers
311 views

Is the average of betas from Y ~ X and X ~ Y valid?

I am interested in the relationship between two time series variables: $Y$ and $X$. The two variables are related to each other, and it's not clear from theory which one causes the other. Given ...
0
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1answer
27 views

Mean Absolute Error in Random Forest Regression

I am new to the whole ML scene and am trying to resolve the Allstate Kaggle challenge to get a better feeling for the Random Forest Regression technique. The challenge is evaluated based on the MAE ...