Questions tagged [regression]
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
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What is R^2 if SSE = SST = 0
I want to acknowledge that this is a trivial edge case. As far as I know, the only case where this will occour is when fitting an n degree polynomial(with a constant term) to constant data. For ...
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Are splines overfitting the data?
My problem: I recently met a statistician that informed me that splines are only useful for exploring data and are subjected to overfitting, thus not useful in prediction. He preferred exploring with ...
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Estimating price elasticities of demand from four step transport model
My question is specific and concerns transport modeling. I am trying to find a way how to estimate price elasticities of travel demand from a 4-step transport model. With this model it is possible to ...
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Logistic regression model; too many independent variables? (listening duration of music genres)
I'm building a logistical regression model to predict the gender of users based on listening duration of music genres. My main worry is that I have over 40 different genre's and I'm concerned that ...
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L1 & L2 double role in Regularization and Cost functions?
I'm confused about the way L1 & L2 pop-up in what seem different roles in the same play:
Regularization - penalty for the cost function, L1 as Lasso & L2 as Ridge
Cost/Loss Function - L1 as ...
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Is the iid assumption in Linear Regression necessary?
In linear or logistic regression, we have the following (adapted from Foundations of machine learning.):
As in all supervised learning problems, the learner $\mathcal{A}$ receives a labeled sample ...
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Online learning with random action set?
Suppose I have an online bayesian linear regression problem for which I can updated the posterior distribution of parameters. Using this posterior, I want to make a point forecast by sampling from it. ...
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What does negative R-squared mean?
Let's say I have some data, and then I fit the data with a model (a non-linear regression). Then I calculate the R-squared ($R^2$).
When R-squared is negative, what does that mean? Does that mean my ...
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Regression model: Y = aX1 + b(X2)^2 [closed]
I received an exercise with requirement of finding a regression model of Y = aX1 + b(X2)^2 for the given data tabe.
Thanks in advanced!
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linear regression can it be used for one group?
Can i study correlation and linear regression for only one group that answered my questionnaire?
the two variables of my research are "gamification" and "motivation" but how can i ...
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function for multiple and multivariate regression in Python [closed]
I have two matrices, lets say Feature_1 and Feature_2. They have the same size, ...
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need some advice regarding finding appropriate statistical method. (weighted, Bayesian, penalized) logistic regression
My study aims to determine the causal relationship between a subset of predictors (consisting of 13 variables) and one binary response variable. We have unbalanced data because the number of samples ...
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Does R-squared help assess statistical significance?
I have a R-squared of 0.4787. I know it indicates the model does not fit very well with the observations, but that is what I got so far using R. My question is: does R-squared help to access ...
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How to interpret Jensen Alpha statistical significance?
When you regress portfolio excess returns against relative benchmark excess return you get a model in which the beta (slope) could be interpreted as the one you get from the CAPM, that is systemic ...
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Derivation of first-order derivatives (gradient vector) for multivariate ordinal regression cutpoints log-posterior
Throughout this post:
$t$ is an index for the $t$-th outcome variable, and there are a total of $T$ outcomes
$n$ is an index for the $n$-th individual, and there are a total of $N$ individuals,
$C_{...
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Polynomial fit: removing outliers
I want to fit a scatter plot with a polynomial, and find the correlation between two variables.
1) How can I define and remove outliers from data points?
(in the figure the outliers on the right ...
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Can't fix non-normality and heteroskedasticity
I am attempting, via linear regression, to model a dataset.I've tried various transformations on the response/ and predictors, as well as WLS but the assumptions are not met. I'm looking for the ...
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How to interpret regression negative interaction coefficient
How do I interpret a negative interaction coefficient in multiple regression, with negative coefficients for main effects? 2) When I run the model without the interaction, the relationship is positive....
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Why do my GaussianProcessRegressor prediction results converge to 0?
I am using sklearn GaussianProcessRegressor to predict a time series.
The kernel I use is this: ...
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How to interpret multivariate logistic regression?
I am a beginner researcher trying to learn how to interpret advanced statistical results. I am working on a (beginner) project to determine the correlation between rural students with student loan ...
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Multi-nominal logistic regression [closed]
i’m not very good with stats but I need it for my university degree, I can’t work out what the results show. The test is whether sound or people affect the behaviour of chimpanzees in a zoo
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What mean and variance should we use for inferring results using linear regression? [duplicate]
While training a linear regression model, it is advised to standardize the input features using the mean and std deviation of the train input features.
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How to correctly set up my mixed-effect model?
I have data on days in which the greening of trees happen across America in 2015. This includes meteorological and topography data etc. I want to predict the day of greening happens through a linear ...
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Can somebody offer suggestions rooted in mathematical theory as to how many data points I require for a regression line to accurately model an object? [closed]
Essentially for a math paper I have to apply regression to model the outline of an object. In order to do so I go on GeoGebra and use the "plot point" function to plot data points along the ...
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What is the algebra showing the logistic and log loss to be equivalent?
This question discusses two equivalent ways to express the canonical loss function for a logistic regression, depending on if you code the categories as $\{0,1\}$ or $\{-1,+1\}$. In the following, let ...
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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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Performing multiple linear regression on SPSS when there are many predictor variables
I have a question about performing multiple linear regression preferably on SPSS 27.
I had 3 dependent variables on the teacher's intention to remain on the job. I got a mean of the scores (measured ...
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How do I fit an equation to this butterfly curve?
I have data which I plotted to visualize. I cant seem to fit any sort of sensible curve to this plot and I wondered how do I approach this task?
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does serial correlation have something to do with endogeneity?
I'm a beginner of econometrics, and I've construed that endogeneity is caused by omitted variable bias, measurement error, and reverse causality, and it makes OLS estimator be biased.
And also I've ...
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Regression model where output is a probability
I am trying to fit a model, and have the suspicion that what I am doing is not quite right. The data tracks what proportion of people made a decision, and what factors were active when they made their ...
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Can you combine a categorical variable with a numeric variable?
I have multivariate(?) time series data where I am trying to model coral populations over time. Measurements were taken at discrete timepoints for specific individuals within a population, and I am ...
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How was applied the formula of linear regression in this case?
I am following a course in Coursera about Introduction to Statistics and I got lost in the part of the explanation of the linear regression. I know is a simple subject, but I do not get it how the ...
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How to calculate and visualize the increasing spread of datapoints (heteroscedasticity) in scatterplot? [closed]
Background: I am a biochemist and an experiment I am working on is producing heteroscedastic data. More specifically, duplicates of the experiment produce very similar data for high values, but very ...
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Residuals pattern in Cox proportional hazards model
I do a survival regression on some time-to-event data (vehicle breakdowns) with some covariates (essentially the age of the vehicle and some boolean variables for vehicle type). I must admit that I am ...
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Why isn't the residual standard error referred to as RMSE?
As I understand it, in the specific context of linear regression, the R output "residual standard error" is an estimate of $\sigma$, the standard deviation of the distribution of the ...
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How to compare the random forest performance using R-Squared?
I've trained a random forests for a regression problem. Now, I want to check if the model is not overfitted. I have tuned the parameters and then compared the R-Squared of Train and Test dataset as ...
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What interpretation do REML/fREML values provide in generalized additive models (GAMs)?
I'm continuing my slow trudge through Simon Wood's book on generalized additive models (GAMs), and it has given me some new useful insights. However, I am still confused after reading through Chapter ...
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minimizer weighted linear regression
In a regression problem, with $y=X\theta+\epsilon$ and $X$ is an $n$ by $p$ matrix
the ‘weighted least squares estimate is the minimizer $\theta^{*}$ of $f(\theta)=\sum_{i=1}^{n}\omega_{i}(y_i-x_i^{'}\...
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Measuring the Impact of Recurring Events
What models can I use to measure the impact of a recurring event? I know that event analysis can gauge the impact of a one-shot event--for example, the impact of an economic policy (which only ...
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What is the difference between these two mixed model specifications?
I've been running several mixed linear models in R. I use the lmer function from lmerTest. I also ran the same analyses (or so I thought) in JASP. JASP uses R ...
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Linear regression with variables which are possibly dependent to each other
I am working on building a linear regression model with one response variable and multiple explanatory variables. For the explanatory variables, I suspect that one numerical variable and one factor ...
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Analyse clustered items from different scales?
I am a non stat person so pls be kind in your reply! What sort of statistic can I use to compare how the items of 3 different Likert scales covariate? My respondents sample is 150 ppl. Each respondent ...
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Multiplying coefficients of logistic regression to get per 10 unit increase?
I'm working on a project in R where I'm looking at California's census tract-level demographic data in an explanatory logistic regression model. I have 6 demographic variables of interest and am ...
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GLM with uncertain features
I am trying to model total goals in soccer matches. I ultimately want to predict various odds for a whole future season in advance (so I need a parametric model I can extract quantiles from given its ...
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Lasso Regression - Finding multiple candidate models
I have 20 predictors and I am attempting to find several candidate models to then test.
I am using the LassoCV library, my following code provides me with the alpha and co-efficients of a model.
<...
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Why does a neural network perform poorly in case of small loss?
Background
I'm building a convolutional neural network (CNN) to predict the response factor (continuous variable) of organic molecules. As input, I use 86x86 onehot-encoded matrices that represent the ...
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How to calculate influence of variables at ROW LEVEL?
There are several algorithms which give relative importance of variables at OVERALL Model level.
But the most influencing variable might not be the reason why a particular row might get higher or ...
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Interpreting coefficients of beta regression
I have implemented a beta regression and am a little confused on how I should interpret the coefficients of my model. For context, both my independent variables and dependent variable are expressed in ...
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How do you deal with imbalanced data when you're doing regression?
To describe my problem. I'm predicting the price of an item depending on some text and other features in an ad. The training data contains a bunch of cheap items, some medium price items and few ...
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Standardized dependent variable, effect size interpretation
I have the following model:
RT ~ condition + (1|participant)
RT is a continuous variable. Condition has three levels and is coded using Helmert contrasts.
I ...
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