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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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Can't find much online about “Linear Regression Estimators” — Looking for help making sense of notes on the topic

I have recently been lectured on how to implement linear regression estimators for a project I have going - I was walked through it works but I couldn't make sense of what was going on. See below for ...
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1answer
49 views

Lmer set up for repeated measurements?

I have 139 subjects (ID), with measurements taken at two time points (Time1, Time2), at 148 brain regions, a dependent measure called volume, and a covariate called thickness. Each subject has 148 ...
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One sample T-test with covariates lmer? [duplicate]

I have 139 subjects (ID), with measurements taken at two time points (Time1, Time2), at 148 brain regions, a dependent measure called volume, and a covariate called thickness. Each subject has 148 ...
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10 views

Should I test for sphericity and calculate statistical power before running and modelling any forecasting model?

I'm trying to model a regression for one dependent and five independent macroeconomic variables. I'm new to statistics but I've read lots of text books and articles about SEM, cointegration, ols, URT, ...
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1answer
29 views

How can I do a multiple regression with data collected annually over 5 years?

I'm trying to find out how which of six governance indicators has the most significance on FDI inflows into a region of 16 countries. I have 3 control variables, and the data has been collected ...
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interpretation of treatment effect from linear mix model when Y is log-transformed in longitudinal analysis

In a clinical study, 100 patients are evenly divided into two treatment group, trtA and trtB. For each patient, a biomarker is measured at 5 different visit timepoints. Y is the measured biomarker ...
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24 views

Parameters for boosted regression trees with small samples [closed]

I have a small dataset (n = 72 rows) for which I am attempting to use boosted regression trees for an exploratory analysis, i.e. to identify which of the 23 possible predictors are "important" and ...
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2answers
106 views

How to study auto-correlation of time series when shocks are present?

The time series I want to model has several shocks due to law changes. Basically, I do not have a lot of data that isn't impacted by these shocks/shifts/pulses. Now, I want to study the ACF and PACF ...
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34 views

regression analysis with both slope and value data

I have measured data $\{x_i, y_i, y'_i\}$, to which I would like to fit a polynomial $y=a x^2 + bx + c$ and $y' = 2ax + b$. It occurs to me that the regression problem of fitting $y=a x^2 + bx +c$ to ...
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1answer
39 views

Analysis of one group of subjects (data provided)

I have one group ($n = 39$) of subjects pre- and post-tested on a continuous variable. I also have a gender variable coded $0$ and $1$. I was wondering how I can analyze this data so to detect any ...
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1answer
38 views

recommended approach for building an ANOVA model

Despite reading several online references, including the full Wikipedia article on "ANOVA", I'm still confused at the recommended process taken to build the most statistically significant linear or ...
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1answer
22 views

Predict Based on Prediction?

I am working on a binary classification task with a pretty straightforward input set of numeric features. One of these features is particularly good, but it cannot be used in real life because it's a ...
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27 views

Scatterplot bounded by parabola

I have frequency data for several variables. Each type can take on one of two variants: ...
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1answer
44 views

Likelihood function for linear regression

For linear regression, the likelihood function can be found with: However if your data points are multi-dimensional such that x,...
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50 views

Solved : L1 norm approximation for the regression ||Ax-b||_1, using CVXOPT solver [closed]

I'm trying to solve a $\ell_1$ approximation regression problem wherein I'm creating a sketch of the matrix $A$ $(n \times d)$, where $n\gg d$. $b$ = A$\beta$ + $\in$. where $\in$ is coming from $\...
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1answer
16 views

What value of alpha should I choose regularization

What value of alpha should I choose in glmnet? Should I use one which minimizes the cross-validation error, one which is one standard deviation above or below the one which gives the best error (like ...
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23 views

Trouble replicating sklearn logistic regression outputs

I am trying to create my own logistic regression classifier using scipy.optimize but I am having trouble getting close to the output of sklearn's built in logistic regression function. I am testing it ...
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7 views

Log-linear BIAS adjustment

I have a loglinear model of: $log(\mu(S_{ij|gij}))=\alpha_0+\alpha_1g_{ij}$ where gij is distance, and Sij is connectivity There is a bias in the distribution of count values for the outcome ...
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12 views

How to build a recommendation engine using a regression algorithm?

I'm working on a recommendation problem in which I want to recommend the best setting/configuration for a machine, i.e. what wheel material results in longer run life for a machine (machine can be ...
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29 views

Log likelihood function for (neural networks) regression

My question is about how we calculate the loglikelihood function for regression when you have multiple standard deviations instead of a single standard deviation. For a standard linear regression (...
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0answers
21 views

When/why to use the normal approximation inside a vertical strip?

The Statistics book (Freedman, Piscani, Purves) has a section about using the normal curve inside a vertical strip. The introductory description is: Often, it is possible to use the normal ...
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1answer
34 views

How well gradient boosting can predict outside training values domain?

It has been said(link , link) that gradient boosting can predict values that fall outside of training domain for $Y$ in a regression problem. I intuitively sense that there is a distinction between ...
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1answer
69 views

Using logistic regression scores for inference

I'm training the logistic regression for binary classification on a labeled data set. Now I'm using the same entries and predict their scores using the model. For example, I have an entry with label ...
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16 views

Statistical adjustment for regression

I already checked out the answer to this: enter link description here It is not a duplicate and that did not answer my question. I wanted to try to ask a different question regarding a similar ...
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2answers
60 views

Variability of Regression Coefficients

What does it mean when your coefficients in linear regression change, after you removed some variables? I already checked the assumptions of linear regression. They seem to hold.
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25 views

how to compare regression results

I'm building a regression model but the metric I care about most is a bit different from tradition measures. That is, I want to see if actual(test_instance1) > actual (test_instance2), what is the ...
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14 views

big difference between r squared in training and test data

I'm building a random forrest regression tree and use cross_validate function from scikit-learn with cv=3 I'm getting a huge ...
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0answers
17 views

Can I use regression output to feed my clustering algorithm?

I'm working on this example: e-commerce dataset with purchases made by users as well as minimal demographic data like age and gender. The age field is rarely filled. So I want to use regression to ...
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58 views

Negative values for OLS variance

I am currently writing some code which performs regression and have noticed that when I calculate variance of $c\hat{\beta}$ I am sometimes on some datasets getting negative values. The variance is ...
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17 views

What is the benefit of a single input single output neural network?

What would be the difference of a neural network with a single input and a single output with e.g. linear activation in contrast to a e.g. linear regression? In my understanding its mathematically ...
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34 views

Testing equality of two coefficients of two separate regressions in R [duplicate]

I run the following regressions: Y_1 ~ c_1 + b_1*X Y_2 ~ c_2 + b_2*X and I get two estimates for b_1 and b_2. What is the appropiate statistic to test if ...
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15 views

significance in linear regression with constraints

I have a problem which is similar to linear regression, but differs in two main points: 1) the number of regressors is equal to the number of observations and 2) I have constraints on the regressors. ...
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1answer
29 views

What's the difference between using $y=ax^{b}$ and $y=ax^{b} + c$ as regression function?

I saw Excel use $y=ax^{b}$ and $ y= ae^{bx} $, why not use $y=ax^{b} + c$ and $ y= ae^{bx} + c $,aren't the latter should be more appropriate? If the former is ok, then why use $y = ax + b$ instead ...
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1answer
23 views

What is an overall procedure for cointegration test?

I'm working on a set of macroeconomic variables form 1992M01 to 201407. They are PPI, CPI, industrial production, stock price index and exchange rate. I know that I should run a cointegration test for ...
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1answer
23 views

Correct Type of Statistical/Machine Learning Analysis For Inflow

I want to predict the number of people joining (inflow e.g. 4000, 5000, 6000 etc) online subscription. The dependent variable is ‘inflow in the first 4 weeks for a certain content title’ as this is ...
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9 views

R^square for a pre-determined linear regression

I would like to produce the R^square goodness-of-fit statistics for a predictive model. I have the base data (10, 000 number of x-values) which are the true values given by an analytic/deterministic ...
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2answers
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Comparing two regression model (Beta regression and linear regression)

I was informed that beta regression was more preferred to be applied to proportion data instead of linear regression. I know I can use various ways to compare the goodness of fit of two models, such ...
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12 views

Heteroskedasticity testing

Im estimating the carhart 4 factor model. Im testing for heteroskedasticity to see whether i need to use adjusted standard errors, but i am finding conflicted results. All but one test (ARCH) are ...
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0answers
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Why mean in Gaussian Process is not so important? [duplicate]

Source of my doubt is the section 2.7 of GPML book by Rasmussen, an screenshot of the book is attached below. Much of my confusion is clarified by this discussion. If mean of GP is not estimated and ...
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1answer
27 views

Can I use a first difference variable as dependent variable in a panel regression even if it contains both positive and negative values?

Can I still use a first difference variable as the outcome variable to run a panel (say, diff-in-diff) regression? For example, my dependent variable is defined as $Y_{i,t} = M_{i,t} - M_{i,t-1} - P_{...
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+50

Accounting for errors in independent variable through Gaussian process regression

In Gaussian process regression (GPR), one applies a kernel (i.e. covariance function) to describe the similarity between observed and predicted data in the domain. The diagonal of the covariance ...
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2answers
49 views

Neural networks for regression vs. more classical regression methods?

I am interested in learning about when one would use neural networks for a regression problem over a more classical regression method such as least squares. Is it mostly related to the complexity of ...
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1answer
94 views

Peanut butter jars full of river mud and bacteria?

I'm an environmental scientist looking into dynamics of bacteria growth in river bed sediments. I collected lots of data, and used regression for most of the comparisons, but one (the most important) ...
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1answer
43 views

changing the coding system from helmert coding to difference coding changes regression results?

EDIT: I think I have mistaken the names of the coding systems, so I changed it (in bold). The content has not changed at all, though, so I would still appreciate any answer. END EDIT I'm running ...
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1answer
59 views

glm returns NA as coefficient for logistic regression

I am fitting a logistic regression for the response variable- 0 or 1. There are 15 explanatory variables- 10 are continuous and 5 are categorical with 3 levels each. I checked collinearity among the ...
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1answer
20 views

How to evaluate neural network regression model

I have some data with 2963 observations and 7 variables. I want to use regression and train this data using neural network then evaluate the regression model. I've tried splitting the data into ...
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0answers
17 views

What is the training algorithm for sklearn's Bayesian Ridge Regression?

I read the sklearn's code to train Bayesian Ridge Regression, but can not understand the algorithm. I think it is EM, but don't know where the update equations for lambda_ and alpha_ come from. Any ...
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17 views

Is it necessary to keep the regression model linear for checking the Granger-Causality relationships between the variables of a data-set?

For checking the Granger's Causality between two variables of a data-set, lets say to check X granger-causes Y, we create two regression models, a restricted model(containing the lagged values of ...
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1answer
42 views

Residual Sum of Squares degrees of freedom intuition [duplicate]

Let RSS = Residual sum of squares $ = \sum (y_i - \hat{y}_i)^2$. Without proof, $\frac{RSS}{\sigma^2} \sim \chi^2_{n-2}$. I do not quite understand why the DoF is $n-2.$ Could someone explain?
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1answer
25 views

Log Inverse Regression Model

Let's say that we have the following regression model: $$\ln Y=\beta_0+\beta_1\frac{1}{X}+\epsilon$$ How should I interpret the value of the estimate of $\beta_1$? I am interested into an ...