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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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Moderated regression analysis: standardizing predictors

for my fourth year project I did a series of moderated regression analyses (7 predictors + 12 interaction terms). I was instructed to standardised my predictors (prior to computing the terms) to ...
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Bayes Factor Question

I am beginner in Bayesian Theorem, I would appreciate it, Someone help me to solve the below problem. My question: I have two models where M1 ~ Normal_n(X.β,σ^2.I_n) and M2 ~ Normal(X_1.β_1,σ^2.I_n)....
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Linear Regression on Boston Housing Price? [duplicate]

As far my knowledge, Linear Regression assumes that data or columns are normally distributed and doesn’t have multicollinearity amongs the features, But when I apply Shapiro test, it shows that none ...
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DFBeta for Binary Logistic Regression - Pregibon 1981

I am attempting to code the calculations for the DFBeta-like diagnostic for binary logistic regression proposed by Pregibon (1981): However, I am wrestling with fully understanding the matrix V in ...
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Serial correlation structures with two nested random intercepts

This is a Gaussian model of spatial correlation: \begin{align} \boldsymbol y &\sim N(\mu, \boldsymbol V), \text{ where} \nonumber \\ \mu_i &= \ldots \text{ (depends on fixed effects of the ...
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Determine the effect of a binary variable?

Consider that a site A has ten thousand web pages. For each of the web pages, the server keeps the number of people visited each of those pages (plus some additional characteristics, such as ...
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In a gamma regression, how can i interpret coefficients?

My question is pretty simple, i have done a bayesian gamma regression with an inverse link, so: $\eta_i$=$\beta_0+\beta_1x_{i1}+\dots+\beta_px_{ip}$ < using an inverse link, mu is the ...
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Difference between OLS and GLS linear Regression in data with heteroscedasticity

I have a cross sectional model which displays heteroscedasticity. I've tried a GLS regression and an OLS with heteroscedasticity-consistent standard errors linear regression. They give significantly ...
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Linear Regression Approach For Trends

I'm working on a project with genetics but I think my problem is applicable to general statistics. I want to test frequency (Minor Allele Frequency) of a SNP/variant across 5 age categories to see ...
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using anova to compare two linear models of unequal sample size: error [migrated]

Here is an example of what I want to do, with a randomly generated number set: a<-runif(100, min =0.5, max=1); b<-runif(100, min = 0, max =1); c<-factor(rep(c("High", "Low", "High", "High")...
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Robustness checks vs. BMA

I have a simple question. Let's say I have a model with 13 IVs and I am using a BMA for analysis. At the end, I would like to add few other variables for robustness checks. However, isn't BMA by ...
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How to use Lindeman-Merenda-Gold in python [on hold]

I need to calculate Lindeman-Merenda-Gold (LMG) scores, for my regression analysis. I've found that is is available under relaimpo package in R-Language. Unfortunatelly, I don't have any experience ...
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multivariate linear regression without b_0 [duplicate]

I created a multivariate regression following the scheme $$y = \beta_0 + \sum^n_{i=1}\beta_i*x_i$$ and got an average deviation ofaround 5%. When I tried the regression without the $\beta_0$ I got a ...
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How to get consistency in neural network and eliminate possibility of NaN values?

I'm using a neural network(Keras,LSTM) for time series regression. Whenever I run the network, I get different outputs for the prediction. This is presumably due to the randomised weight ...
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207 views

Bayesian inverse modeling with non-identifiable parameters?

If I have a physical model \begin{equation} y = \frac{1}{\beta_0} (\beta_1 x_1 + \beta_2 x_2) \end{equation} and want to estimate coefficients $\beta_0$, $\beta_1$, and $\beta_2$ from given data ...
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Random Regression in R

I am a student. I have some knowledge of mixed regression models. I would like to implement Random Regression in R. I found "Random Regression Models" by Schaeffer (http://animalbiosciences.uoguelph....
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Component Plus Residual Plot: How do they work?

How does component plus residual plot work? You create these plots by plotting: But how do they work? The books says that you want to get rid of the influences from other predictors. A linear ...
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AUC/R^2 score strictly lower on partitioned dataset than total dataset

Suppose I've fit a logistic regression to my data and I calculate the AUC. I then partition my dataset according to one of the explanatory variables (say those rows where the variable value is greater/...
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Instrumental variable analysis: ivreg (R) vs. naive estimation

Short version of my question: Is it true that the naive, 2-step instrumental variable approach overestimates the standard errors (I expected an underestimation)? Long version: I am working with an ...
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1answer
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Time series: group and then forecast, or forecast and then group

Let's say we want to forecast revenue by month for the next 12 months, and we have daily revenue data for the last 3 years. We could then group this data by month, train our model using revenue by ...
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Building and interpretation of regression models [on hold]

Coming from the world of classification I struggle to understand how to properly build and interpret regression models. Some tutorials only build models on the training data to make conclusions. ...
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Should I build a different model for each subset

I have a dataset which has categorical variable class and it has around 10 classes in it. I am trying to solve a regression problem I am not understanding whether I should build a model on entire ...
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PCA interpreting coefficients

down vote favorite had a question on something I thought was pretty basic, but just not getting this. I have three variables, and three principal components rateprev1 rateprev2 rateprev3 0.03831 0....
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What will be the Lower and Upper Confidence of Simple Regression

The model is $y_i\sim N(\beta_0+\beta x_i, \sigma^2).$ If $\sum x_i=192,$ $\sum y_i=258,$ $\sum x^2_i = 5280,$ $\sum y^2_i=9002,$ $\sum x_i y_i=6864$ Then How do we plot the fitted value of $y$ ...
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Interpretation of fitted model with 'auto.arima'

I'm a little bit confused about the fitted results given by auto.arima when I was trying to fit a regression model with ARIMA error. The example is ...
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Autocorrelation: Multiple observations at lag 0

I have data recorded over 100 days. For each day there are ~5-10 observations. How can you check whether residuals of one day are correlated at lag 0? More precisely: Are residuals of the same day ...
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Finding Impact of IV on NPS of response to a survey [duplicate]

I have a dataset like this. Where the first column is the age of usage of product of a user (in days) and second column is the response of the user to the typical NPS survey question: Using these ...
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1answer
24 views

How to fix and understand linearity

The model I have run is a simple multiple linear regression. The model looks like a great fit, but R is telling me otherwise. My question is 3 fold. 1) How do we estimate linearity (not visually) 2) ...
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Introducing random slopes in models with nested random effects

I'm trying to see how latency to emerge (response variable) is varies with time (trials). Individuals (ID) are nested within colonies. The nesting is such that individuals 1-20 belong to colony 1, 21-...
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Pyspark Linear Regression : implementation and details [on hold]

The development team in our group is trying to deploy the prediction part of linear regression by storing the coefficients and intercept from the model fit. So I was looking for materials on: How the ...
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1answer
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Can I combine several ordinal questions to create an independent variable? [duplicate]

I am currently doing a research and recently encountered a few statistical concerns. I am basing this question on the following post. My question is quite similar to the above-mentioned one but has ...
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Should I try to estimate logistic regression by F1 maximization, rather than Liklihood?

Assume I have a dataset of covariates $x_i$ and binary outcomes $y_i \in \{0,1\}$. I want to predict outcome for unknown a unknown $y_k$ given $y$. Quite common is to do this with logistic regression,...
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1answer
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lmer multilevel regression with intercept constraint [on hold]

I regularly have this problem: I want to fit a multilevel regression, with constraint. I don't know how to do that. I usualy end up using lavaan, as it allows to ...
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M-estimator (S- and MM-) for regression models

Let's assume simple regression model $y_{i}=\beta_{0}+\beta_{1}x_{i}+\varepsilon_{i}$. It is obvious that the interpretation of the $\beta_{1}$ parameter is given by $\frac{\partial E(y_{i}|x_{i})}{\...
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When can Autocorrelation of Residuals be ignored?

One assumption of OLS regression is that residuals are idependent, so that there is no autocorrelation. When I checked the assumption, I noticed that autocorrelation is present. Now here are two ...
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1answer
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Linear regression - Can I log transform dependent variable and one of the independent ones and keep the rest not transformed? [duplicate]

I have model where my dependent variable is Total money spend and then I have independent variable Income and some other ...
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Lagged Dependent Variables (are they in or are they out) Vilasuso (2001, Jounral of Econometrics)

I am really struggling with whether to include lagged dependent variables or not. I have read the logic (on this website) that a lagged dependent variable should include if its current value is ...
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1answer
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PROC GENMOD Negative Binomial doesn't predict zeros

I am using PROC GENMOD with time series data, I have tried to work with Negative Binomial, Poisson, GEE and Zero Inflated Poisson, but in each case when I score my validation dataset, I am getting ...
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Can I average out a constant (intercept) in OLS regression?

I have a OLS regression in the form: $$Y_t=\alpha +\beta X_{t-1}+\varepsilon_{t}$$ Can I average out the constant during the OLS estimation/derivation and report, $$y_t=\beta X_{t-1}+\varepsilon_{...
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complementary and substititive effects, what analysis to use?

i regressing a dependent variable (return on investment) on 3 environmental strategies. It was suggested to me to look for complementary and substitutive effects. A hypothesis looks like this: "...
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1answer
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Data level at which the Regression should be run

So I am new to regression and I have a basic doubt: Let's say I have 100 unique products(product id) which have a lot of other features that contribute in calculating the product_price(dependent ...
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Determine the effect of each variable and interaction in regression analysis

I am studying the effect of two variables x1 and x2 on response variable y. Interaction term ...
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33 views

What should be the Variance in X to get a statistically significant relationship with Y?

My goal is to understand the effect of a promotion campaign on sales of a product. My hypothesis is that there isn't enough variation in my campaign variable to run regression. I've ~200 geographical ...
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37 views

How cost function for simple linear regression behaves under different settings with batch gradient descent?

In the linear regression problem, using a simple linear model with 1 variable & with 2 model parameters, performing batch Gradient Descent(GD) & assuming I am using Mean Square error as my ...
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1answer
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Do I need to adjust for confounding when the confounder is not causal?

Suppose I have a model like $$y =\alpha + x_1\beta $$ and that there exists another variable, $x_2$, that is correlated with both $y$ and $x_1$. However, changing $x_1$ will cause changes in $x_2$ ...
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What algorithm(s) should I use for a regression training and a classification prediction?

I am trying to work on a project on MALDI-TOF MS dataset. The dataset contains mass-spectrometry data of pure samples (1 bacterium species) and mixed samples (mixtures in known proportions of 2 ...
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How much Autocorrelation is acceptable in Regression Analysis?

One assumption of regression analysis is independence of residuals. I checked this assumption and found small autocorrelation (see figure). One remedy would be to incroporate dummy variables for the ...
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1answer
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How should I treat categorical variables for the purpose of the “One in 10 rule”?

Hope a basic question like this is alright! To avoid overfitting, we try to maintain enough cases for the least common event per explanatory variable; people usually recommend at least 10. How should ...
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Finding effect of a variable on NPS of a survey responses

I have a survey responses data. It has various columns, but one of the column is the age of the product usage by the user. The other column is the response given by the user on a 0-10 point scale as ...
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33 views

Bootcov in R’s rms package not working when cluster variable included in regression model as fixed effect

I'm trying to use bootcov in Frank Harrell’s rms package in R to get standard errors that account for state-level clustering (...