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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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Including % Race/Ethnicity in Regression Model

I have been examining high school graduation rates, and wanted to include race/ethnicity as a control. The only data available is % of students identifying as one of 7 race/ethnicity categories. My ...
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0answers
24 views

Whitney-Mann and multiple regression

Is it appropriate to use Whitney-Mann (to compare the groups) and multiple Regression model (to analyse each factor) within the same data? (parametric and non-parametric test on the same data)
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1answer
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Coefficient of multiple correlation for multiple linear regression with degree > 2 and interaction terms

I want to calculate the Coefficient of Multiple Correlation $R^2$ for a multiple linear regression with polynomial features of degree >= 2 (with interaction terms). Let's say I want to obtain the ...
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2answers
27 views

What OLS assumptions need to hold when fitting a linear model with machine learning?

I often hear that machine-learning algorithms are not restricted to the assumptions behind the Ordinary Least Squares (OLS) estimator for linear models, e.g.: The conditional mean should be zero. ...
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How to interpret regression coefficients when each predictor variable contains different categories

Overview: I have conducted two types of statistical analysis using both linear regression and multiple regression. Overall, there were two observation periods, and the idea is to gauge if the rate ...
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0answers
28 views

Why is it that Support Vector Regression isn't as popular as SVM?

I know that SVM and SVR have different purposes in that the former is used for classification while the latter is used for regression, despite the similarity in the concepts between the two. However, ...
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1answer
33 views

Difference between least squares and chi-squared

What is the difference between least squares and reduced chi-squared? For me they look nearly exactly the same, with the difference, that in chi-squared everything is divided by the variance. But ...
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28 views

Problem doing Machine Learning in this dataset

I have a problem to obtain good performances with a dataset. I have to predict the flow of visitors in my city given the distance of origin of tourists and the number of inhabitants of their city of ...
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7 views

two part regression confidence interval

I am trying to predict amounts of data that goes through a few stages, let's call them stages A,B,C. Now I have data on the amounts from A to B and from B to C, so i created two regression models for ...
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1answer
18 views

likelihood for regression purposes

Which role plays likelihood estimation in regression analysis? I've seen it here, but I can't figure out when I exactly have to use it. Is it, when I know which function or model I want to apply (...
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1answer
36 views

Posterior density for a linear regression model

Given a classical linear regression model $$y = X\beta + \varepsilon,$$ $$\varepsilon\sim N(0,\sigma^2I_n),$$ the posterior density is proportional to the product of the likelihood and the selected ...
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23 views

What are the uses of panel data regression?

I have a dataset with Cross-section and Time-Series Data. After doing some reading I came accross a concept named as "Panel data analysis/regression". However, I am still not clear how and why we use "...
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2answers
54 views

Choosing the optimal theta / dispersion parameter for negative binomial regression (glm / glm.nb) in R

I am applying a negative binomial regression to my data in R. For this, I use the package MASS and have two different ways to calculate it: ...
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7 views

Is it appropriate/possible for me to use PLS regression for my problem?

Im currently writing a dissertation on the effect of cultural dimensions on technology acceptance. In order to collect data I have used a questionnaire with questions relating to technology acceptance,...
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1answer
23 views

OLS regression for not normally distributed data?

In a college course I try to measure the abnormal returns (the returns that are below or over the returns of the market) of a companies stock after a specific event based on linear OLS regression. ...
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0answers
13 views

Can the variables for a regression be replaced? [duplicate]

Does it play a role which of both variables X, Y is determined on the other one? I think in case of a straight linear curve it should provide the same results(?) but what about a quadratic curve: y(x)...
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How to automatically identify degree of multiple independent variables of Polynomial Regression in R

In the dataset, there are 8 independent variable and 1 dependent variable. I want to use polynomial regression to find the relationship between independent variables and the dependent variable. The ...
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0answers
11 views

What should I use to analyse the relationship between independent and dependent variable, correlation or regression?

i'm anuradha I am going to test the relationship between inventory management and financial performance with four independent variables and two dependent variables. Inventory turnover, inventory to ...
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0answers
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Whats the most appropriate test: Anova, multiple regression, or linear regression? Confused!

Overview As part of a group practical activity, we collected phenological data from deciduous oaks trees which were pooled into a large database (see the data frame below). Parameters Measured ...
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2answers
64 views

Why does the OLS-intercept not just “de-mean” the residuals of the same model without intercept?

The answer here explains, why the residuals of an OLS-regression have mean zero if an intercept is included. Problem: Intuitively, i would assume that including an intercept just "de-means" the ...
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0answers
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How to deal with correlated regressors in a multiple regression model?

i currently try to estimate the effect of different task parameters (IV) on neuronal activation (DV). Some predictors in my design matrix(trials x features) are moderately correlated (r~=.3) and I ...
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0answers
25 views

Model failed to converge in lme4::glmer() when the a factor is centered or releveled

I'm running a mixed-effects model using glmer() function. The modeling works well with R's default dummy coding. But if I center or relevel a factor of 2 levels, the model failed to converge. I am ...
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0answers
16 views

About linear regression and polynomial functions [duplicate]

Can I apply linear regression to any function to the x's. For example: Polynomials with one variable: y = w_0 + w_1 * x + w_2 * x2 + ... + w_D * xD Polynomials with multiple variables (let's say ...
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Plotting correlation coefficient against regression coefficient

I have samples taken in different places with yearly data for temperature and a measure of photosynthetic activity/biomass (NDVI). So for example for each sample I have ... Year 2000 2001 2003 ...
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27 views

Independence vs. conditional mean independence

Independence, i. e. cov(g(e),f(x))=0, can be considered as a stronger assumption than conditional mean independence, i. e. cov(e,f(x))=0. Does independence, i. e. E{f(x)g(e)}=E{f(x)}E{g(e)} imply ...
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Presenting regression model

I have a problem with writing the regression model in my thesis. For example below; Fixed effects model with year,town and industry dummies. 1) How should I write the dummies in the regression model?...
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1answer
28 views

Significance levels of the estimated logistic regression coefficients for artificially generated data sets

I'm trying to simulate 2 data sets for testing some variable selection methods for logistic regression models. The initial step is to fit the logistic regression for all candidate predictors. But ...
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1answer
20 views

Predicting risk factors

i'm trying to find predictive risk factors i already found out that young age at diagnosis is a risk factor ( binary logistic regression) But now i want to know the exact age when the risk is highest....
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1answer
14 views

Paired variables?

In our research on apprenticeships in early modern times, we have a series of observations (cases), each with several variables. Each observation is unique because of its combination of the variables "...
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1answer
24 views

combining gridsearch with regression : how to estimate variance of residuals

I'm trying to do measurements based on image processing : first I do some image processing to find detect the pixels that changed between the background and my image, and then I perform a linear ...
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3answers
35 views

Comparing two logit models

Folks, Would appreciate any advice on the following topic. My question I would like to answer is the following. What are the determinants of being a First Time Buyer of a House? Based on a survey ...
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1answer
41 views

Is it possible to apply a monotonicity constraint on a Gaussian process regression fit?

Below is a code using scikit-learn where I simply apply Gaussian process regression (GPR) on a set of observed data to produce an expected fit. I know physically that this curve should be ...
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5answers
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Why Normality assumption in linear regression

My question is very simple: why we choose normal as the distribution that error term follows in the assumption of linear regression? Why we don't choose others like uniform, t or whatever?
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How are R2 and adjusted R2 mathematically related to the idea of explained variance?

I am trying to understand in what sense, $R^2$ and $R_{adj}^2$ represent the "explained variance." I can't find any similar question that explores the connection in mathematical detail. My current ...
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How to Estimate Treatment Effects using Heckman two steps (Heckit)?

I need a help on how to find a treatment effects using Heckman two steps method (Heckit), I need to find ATE (Average treatment Effects), TT (Treatment on treated) and MTE. I tried to do a simulation ...
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0answers
30 views

Help with circular-circular regression

I am looking for assistance in using the lm.circular function of the circular package. I am unsure of A) why the package authors ...
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0answers
17 views

Intuition Behind One Vs. All Linear Least Squares Classification

I understand that in the one vs. all classification approach, we form $k$ discriminants, one for each of the $k$ classes and that $(w_k - w_j)^Tx + (b_k - b_j) = 0$ is the hyperplane decision boundary ...
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37 views

Hierarchical bayesian model: should I account for lack independence?

I am working with vegetation surveys that were conducted in several river networks. See the attached image that shows one of the those river basins/networks. I am interested in analyzing how the ...
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3answers
59 views

Why should I check for collinearity in a linear regression?

The Gauss-Markov Assumptions: MLR.1: Linearity in parameters. MLR.2: Random sampling. MLR.3: No perfect multicollinearity. MLR.4: Zero conditional mean Hence, why should I check for high (but not ...
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0answers
16 views

Difference between regression p-values and t-test on residuals?

I'm interested in testing if a particular (binary) feature is significant in explaining a target variable, after other factors have been considered. Naively, I can do this by including the test ...
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0answers
18 views

How to obtain regression coefficients of a multiple linear regression model from simple linear regression models? [duplicate]

Suppose I have a multiple linear regression model $$ Y=\beta_0+\beta_1X_1+\cdots+\beta_pX_p+\epsilon$$ How can I obtain the regression coefficients $\hat{\beta_i}$ by fitting just a series of simple ...
2
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1answer
41 views

Machine learning for inequalities

This is a very general question about machine learning. Two of the most standard problems in ML are classification and regression. E.g. if we have pictures of buildings, we can classify them as two-...
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0answers
14 views

Supervised machine learning for dimensionality reduction of control variables in logistic regression

Is it a valid approach to use the predictions of a supervised machine learning (ML) algorithm as a form of dimensionality reduction of control variables in the context of logistic regression? ...
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1answer
9 views

Scoring procedure for survey responses

I have a project to create a scoring procedure for respondents answering multi choice psychological questions based on their behavioural metrics. So the responses can be treated as categorical ...
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0answers
7 views

What type of regression (and Stata approach) to use with 1 to 10 scale independent variables?

I'm looking to use regression analysis to compare the impact of certain training programs on whether a candidate won or lost. The independent variables (training programs) are number scores from ...
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2answers
26 views

Relaxed Lasso Logistic Regression: Estimating second penalty parameter

I'm trying a relaxed lasso logistic regression by first using sklearn's cross validation to find an optimal penalty parameter (C = 1/lambda). Then, I use that parameter to fit statsmodel's logit model ...
2
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1answer
20 views

What are the 'implications for diagnostics of the standard linear regression assumptions' of the results $\sum_{i}e_{i}=0$ and $\sum_{i}e_{i}x_{i}=0$?

I've been doing some work where I had to prove, for standard linear regression, the results $\sum_{i}e_{i}=0$ and $\sum_{i}e_{i}x_{i}=0$. I did not find this to be a problem, but I have also been ...
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2answers
32 views

Linear Regression - holding predictor fixed at its mean

I am trying to create a linear model to predict House Price ($y$). The predictors in the dataset are Area (continuous) & Location (factor: West, Midwest, South, Northeast). I am asked to assess ...
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2answers
53 views

I could not fit a linear mixed effects model with time as a random effect, but the time effect should influence the observed results

I have data which has an independent variable (temperature), dependent variable (gasflux) and an additional time variable as given below. ...
2
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0answers
28 views

Combining multiple observation weights for classification

Let's say you have multiple sources of observation weights for a dataset. For example, you have a $[0,1]$ weight coming from the label's certainty ($w_c$) and another one coming from its recency ($w_t$...