Questions tagged [regression]

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

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Good way to select reference and/or performing normalization of data points across experiments?

I have a set of objects S1 = {a1, b1, c1, d1, ..., z1} that perturbed a reference object q and were tested for effects in ...
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Mathematical rigorous approach to linear regression [duplicate]

I'd like a book or any PDF that teaches about linear regression in a complete way. That is, I want something that, for instance, doesn't use any random variable without stating its domain or the $\...
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How to specify a model with multiple treatment groups, measured twice, repeatedly across a time period (lme4)?

I have 30 animals (factor: animal_id, 30 levels) which have been treated with a drug or a vehicle (factor: treatment, 2 levels). ...
doesnotcompute's user avatar
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regression wtith t scores

I have performed a study in which I converted performance on a cognitive test to z statistics and from that derived t scores. We would like to examine the relationship between t-scores and various ...
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Splitting dataset by gender

I want to examine genderdifferences in three variables, and also an interaction effect. I am going to use ordinary least square regression, but i dont know if i should split the dataset by gender and ...
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Assessing and interpreting the results of a multinomial logistic regression in R

I run a multinomial logistic regression. The input data are given below: And the results are available here. ...
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Why the Sum of Squares Error in ANOVA has n-1 degrees of freedom?

There is a statement that got my attention recently, which is "ANOVA is just linear regression". I was watching this video that seemed to explain the relationship between the two topics. At ...
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Do autocorrelated residuals cause OLS coefficients to be biased?

I see different answers everywhere. Intuitively, I would think if residuals are autocorrelated then there is some information that you are not incorporating into your model and is a sign of a biased ...
user2330624's user avatar
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How to express Hotelling T² test as a Likelihood ratio between two multivariate linear models?

Is it possible, given that Hotelling's T² (or Hotelling-Lawley Trace for that matter) is just a generalization of Student's T, to reformulate the same testing procedure (to test if two vectors differ) ...
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Quantile Regression Detrending

Assume I have a time series, as the black one below. As shown, the quantile regression for 5%, 25%, 50%, 75%, and 95% quantiles show different slopes (in red). Even if not quite visible, the ratio ...
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How to specify mixed-effects using lme4 in r for a within-subjects experiment [closed]

Now I want to regress the overall quality of the ideas (DV) on the source interacting with a continuous variable. I was advised to use the lme4 package, but I am ...
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Which of these is the correct way to encode a type III ANOVA analysis?

I am trying to run a type III ANOVA analysis to look at how the three factors A, B and C affect the continuous variable X. Online I have found two ways of encoding the model which give different ...
Insect_biologist's user avatar
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Am I using emmeans correctly to look at pairwise differences after running a type III ANOVA model?

I am looking at how the weight of female individuals is influenced by the two factors A (5 levels) and B (2 levels) using a type III ANOVA as follows: ...
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Error in mixed-models. Which to detect? Collinearity? Singularity in backsolve at level 0, block 1

Firstly, I would like to admit that even though it is not the first time I am working with linear mixed models, the mathematical foundations escape me. I am running a linear mixed-effects model using ...
Javier Hernando's user avatar
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Testing Assumption of Linearity for Multinominal Logistic Regression

I have a nominal DV (0 through 3). IVs are all continuous. I'm trying to test the linearity assumption, however I can't run the logistic regression function in SPSS with the LN IV variables because of ...
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Ordinary Linear Regression with One Independent Variable

I am currently undertaking a project where I aim to explore the relationship between a single independent variable and a dependent variable. I have five questions that are answered on a 5-Point Likert ...
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Sample Size for Adaptive Lasso

Be gentle, I'm learning here. I have a fairly simple adaptive lasso regression that I'm trying to test for a minimum sample size. I used cross-validated mean squared error as the "score" of ...
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How do I interpret this QQ plot and residual vs fitted plot?

I have a model in R looking at infectious disease spread on social networks, and I am running into a problem where my data are clearly not normally-distributed when I try to run a linear regression ...
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Paired t-test or ANCOVA or linear mixed model?

Recently I encountered a situation where each subject has a continuous response variable at 3 time points ($t_0$, $t_1$, $t_2$). There are $3$ treatment groups ($A,B,C$), and some covariates. Consider ...
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Commodity Price Modeling with Time Series vs. Return

TLDR: Assuming that the current commodity prices are influenced by a variety of factors, including the prices from the previous period. Given this context, should I explore the relationship between ...
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3 votes
1 answer
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mixed effects model when within group variance cannot be estimated

What is a mixed effects model when the within group variance cannot be estimated. For example I have an outcome that is very rare to begin with. Less than 5% of children have this outcome. Then I have ...
Sundown Brownbear's user avatar
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Effect Size of CLMM

I am conducting an omnibus analysis of a CLMM model from the ordinal package. I passed the CLMM model to the function ...
Tavaro Evanis's user avatar
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1 answer
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Determining the magnitude of influence between two variables

I am trying to figure out the magnitude of influence one variable has over another, and how much it changes if we analyze it in the opposite direction as well. Essentially, I am trying to figure out ...
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Endogeneity problem in ols regression and causality

If in my model, the independent variables are uncorrelated with the error term, there is no endogeneity problem, and the residuals satisfy the OLS assumptions, can I say that this model identifies ...
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Does white noise guarantee that $X_{t-1}$ is uncorrelated with $u_t$?

I have a question about the properties of white noise in a time series context. Specifically, I want to know: If we assume that the error term $u_t$ in a time series model is white noise, does this ...
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Truncated response variable in boosted regression trees

I was thinking about the differences in approaches between parametric and non-parametric statistics in regression. I am working with a non-negative integer response $N\in\mathbb{N}_{0}$. Let's imagine ...
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I would like to test for isometric growth in fish using b value from linear regression against a b value of 3 using t test

I have data on length/weight for male fish and want to compare by $b$ value for slope of regression against 3 to test for isometric growth. I tried using hoCoef ...
amy's user avatar
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Fit from lm in R does not equal group means?

I define data such that: The outcome variable performance is a number between 0 and 1 ...
SimonSimon's user avatar
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186 views

Usefullness of Graphical Models in practice

Graphical Models uses that correlation 0 is equivalent to independence for multivariate normal distribution. Then we can make a graph where there is an edge between two nodes if the correlation is not ...
ScapeProf's user avatar
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Is there a way to do competing risk regression where you can specify entry and exit?

I would like to do competing risk regression with attained age as time scale. People enter the analyses at an age that is 5 years after their disease diagnosis and exit at the end of follow-up. For ...
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How to use propensity scores in real examples

I am trying to understand how to use propensity score matching in a real world example (e.g. case control study). Step 1: Based on what I understand, I think a Logistic Regression is first used to ...
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116 views

Sampling distribution of Coefficient of determination in general

I was studying the properties of multiple linear regression, and stumbled across the F statistics often used for the tests. We are testing for the regression coefficients (let's say there are $p$ ...
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Linear regression with smoothed time-series as independent and dependent variables

I'm pretty sure I'm misunderstanding something quite obvious here but I'm rather confused. I have multiple time-series that have been smoothed with a gaussian kernel. My goal is to regress the time-...
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How does plotting QQ plot on ggplot work?

I am new to r programming and have ran into an odd situation while plotting a QQ plot for studentised residuals with ggplot2. See code and plot below: ...
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Regression with one group having just zeros as input

I want to analyse the effect of the sports membership fee on the cancellation probability with a simple regression. When a person leaves a sports club (Cancel = 1), their membership fee is ...
Stojan's user avatar
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Comparing Deming/Orthogonal Regression to Null Hypothesis

I have some data of with the relationship Y=commonFactor+error1 and X=Alpha+Beta*commonFactor+error2 I want to test the hypothesis that Beta is non-zero, or that there is a significant relationship ...
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Explicit form of L2 regularization in sklearn.linear_model.LogisticRegressionCV [duplicate]

I am using LogisticRegressionCV of sklearn, and I would like to know the explicit form of the L2 regularization in Logistic Regression. In the official page of LogisticRegressionCV, it is written $Cs$ ...
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Linear Regression Excel Question [closed]

Let's say I am comparing the impact on features for a housing market. If I were to take the data for all houses in a region with a value between USD 200,000 and USD 500,000 and compare how individual ...
Curious User's user avatar
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70 views

Implication for a perfect fit in OLS regression

If $ \hat{\beta} = (X'X)^{-1}X'y $ with $ X $ being an $ n \times k $ matrix, then as I understand it, as long as $ k \leq n $, $ X'X $ is invertible (as long as all other OLS assumptions are ...
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How can you adjust for ceiling effects in predicted effectiveness?

I have a dataset from a trial of a new method to encourage people to vote. The dataset has three variables: cond: treatment vs control predictive_score: A value from 0 to 100 reflecting how likely ...
octern's user avatar
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Out of sample testing Geographically Weighted Regression

I have spatial data on property prices and I've fitted a GWR, calibrated on all points. I haven't seen cross-validation or out-of-sample testing approaches applied to many GWR models in literature, Is ...
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Lasso regression test MSE lower than train MSE

Im currently using Lasso to build a predictive model for numeric variable . Before scaling the features I split the data for train test and validation . I have a feature named 'year' and i wanted the ...
liza read's user avatar
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How might I go about analyzing the affect that the number of attempts of something has on the failure rate?

I have a dataset where I know the number of attempts and number of failures for a large group of individuals. I have calculated the failure rate of each and I suspect that as there are more attempts ...
peeezy's user avatar
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3 votes
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Do sample sizes in different groups have to be identical? (major imbalance)

Suppose there is a study where they want to test if stopping to eat meat vs continuing to eat meat prevents some disease from happening. The study lasts 3 years and they want to compare the rate of ...
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Dummy variables to indicate the week of year for linear regression? Is that a good idea [duplicate]

I have a problem with my data. I want to have a dummy variable to indicate the week number in the year as a predictor. When I look into it, it seems that I need to have 51 dummy variables, and my ...
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Variance Bias Tradeoff decomposition of Linear Regression with a twist

Normally, for a linear regression problem with fixed observations, we have the variance and bias tradeoff as: $$Var(Y) + Bias^2 (\hat{\beta_x}) + Var(\hat{\beta_x})$$. My question is what happens to ...
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Derivation for Variance of Regression Coefficients [duplicate]

I am trying to Derive the Estimated Variance of Regression Coefficients. I am struggling with the algebra. Here is the model: $$y = X\beta + \epsilon$$ $$\hat{\beta} = (X^TX)^{-1}X^Ty$$ First, we find ...
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1 vote
2 answers
72 views

Significant interaction when only one of the two main effects is significant? [duplicate]

I have run a mixed effect logistic regression model, and I have obtained a significant main effect of Factor A, while no significant main effect of Factor B. However, the interaction between Factor A ...
Katherine's user avatar
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A question about odds ratio: Gaussian vs binomial regression 5 [duplicate]

I am working with a binomial dependent variable (fail=1, not fail=0), and using ratios as independent variables to predict the outcome. My dataset is n=34, so it isn't. I'm using R. When I use the ...
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1 vote
1 answer
24 views

How to calculate R-squared after using clogit function in R

I am trying to calculate the R-squared value of a logistic regression model using the clogit function after multiple imputations with mice package in R. Here's the ...
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