Questions tagged [regression]

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

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12 views

Plotting predicted common OR and 95% CI from an ordinal logistic regression model

I fitted an ordinal logistic regression model with a dependent variable of four ordered responses with one continuous predictor variable and 6 confounders, using the polr package in R. It looks like ...
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1answer
44 views

Is the sum of 3 bits a linearly separable task?

In other words can a linear classifier learn to correctly assign a class (label 0 to 3) for an input of 3 bits? Intuitively this cannot work, since the half-adder circuit contains an XOR block, which ...
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23 views

How regression coefficients change when shifting the scale of the response variable

I am running a regression with y: a 7 point index ranging from -3 to 3, x: binary indicator (0,1) of second wave of data collection. When I fit this regression, I get the following equation: ...
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18 views

Testing the ratio of SLR coefficients

Consider applying simple linear regression (SLR) to the data $\{(X_{i},Y_{i})\}_{i=1}^{n}$. Furthermore, let's assume that the errors are conditionally normal, i.e $$Y_{i} = \beta_{0} + \beta_{1}X+e_{...
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2answers
100 views

Orthogonality of columns of the augmented design matrix for ridge regression

In the question: How to derive the ridge regression solution? there is a solution by whuber, which describes how the columns of the augmented matrix approach pairwise orthogonality as the ...
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10 views

Is polynomial regression doomed to only work for data set with tiny amount of features?

Consider curve fitting for a dataset $D = \{(x_i, y_i)\}$, $i = 1, \ldots, N$, $x_i \in \mathbb{R}^f$, $y_i \in \mathbb{R}$. Here, $f \in \mathbb{N}$ is the number of features associated with the data....
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9 views

Can I adjust the predictand in a linear regression model?

Attached is my predicted linear regression model. The dark blue line is the ensemble. I can fairly predict the training period (1984-2015) and so is the validation (2016-2020) but not the magnitude. ...
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11 views

Controlling for a variable almost the same as my moderator in interaction regression - colonial data question

I'm unsure of if i'm avoiding intermediate variable as controls in my interaction model, since the controls are fixed at the same time as the variable I'm adding in my interaction term. My model is as ...
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18 views

Research question - avoiding intermediate variable as control in interaction - colonial data

Hi, I'm unsure of if I'm avoiding intermediate variable as controls in my interaction model, since the controls are fixed at the same time as the variable I'm adding in my interaction term. My model ...
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9 views

Interpreting : significant categorical variable but all dummies are non-significant

I have run a binary logistic regression in SPSS. One of the covariates has 6 categories, last one serving as referent group. Results show that the variable is significant, but neither of the dummies ...
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1answer
34 views

Do we have almost sure equality in linear regression?

If we have a linear regression model given by: $Y =\beta_0 + \beta_1X + \epsilon$. Is this equality in the almost sure sense or only in distribution? I couldn't find anything on this question on the ...
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1answer
50 views

Why is a t statistic used for standardized regression coefficients?

I understand the sampling distribution of unstandardized linear regression coefficients is normal, and therefore a t distribution can be used to determine p values for given coefficient and standard ...
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1answer
154 views

How can the CLT fix OLS regression residuals that are not normally distributed?

I often hear that when the residuals depart from normality, the central limit theorem can be used to fix things. I do not quite understand how this works, since the central limit theorem is a ...
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1answer
48 views

Consistency of slope given by SLR through the origin

I'm reading a post about the consistency of coefficients of SLR models: Consistency of estimators in simple linear regression Now I'm wondering whether there will be some similar conditions for SLR ...
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23 views

Regressing a WN variable on a random walk

I probably have a silly question, but what would the residuals look like if you regress a white noise variable on a random walk? I mainly refer to the order of integration of the residuals. Would it ...
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1answer
37 views

How to use logistic regression with original values from a dependent variable that is a ratio?

I have a dependent variable that is a ratio, i.e. it takes the values between 0 and 1. Some 30% of values are 1s. The dependent variable measures the distribution of funds, i.e. it is calculated just ...
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1answer
31 views

How to interpret mean of this estimated AR(1) process

I estimated an AR(1) process, my data looks like this: Making usual unit root test, they suggest that an estimated AR(1) from this data is stationary. Estimating the AR(1) over this data, these are ...
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1answer
53 views

What is the sampling distribution of the standard error of regression coefficients to get an accurate histogram?

I'm trying to fit the pdf on the variance of regression coefficients. I understand how to plot the regression coefficient estimates, the noncentral t-distribution, but I am always off for when I try ...
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1answer
22 views

Poisson model without exponential relationship between Y and X

Im having a hard time understanding how to fit a count model for data that i dont think is exponentially related. $$E[Y|X] = e^{\beta_0 + \beta_1x}$$ Is the typical relationship when it comes to ...
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1answer
98 views

Can we use fractional regression for a dependent variable that is made of continuous numerator and denominator?

I have a dependent variable that is a ratio, which takes values between 0 and 1. Some 30% of values are 1s. The dependent variable measures the distribution of funds and is calculated as amount of ...
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0answers
7 views

AdaBoost Regression Prediction Inequality

I have created an AdaBoost regression model from scratch in Python but ran into a rare occurrence where a prediction can not be made for an observation in the testing data. I have followed section 3 (...
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1answer
32 views

Train vs Test performance

Hey I am building logistic model. My train data has 4000 observations and my test set has 1000 observations. What suprised me is fact that for train set I get AUC 0.9 but on my test set I get AUC 0.92....
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15 views

What type of regression I need for two dependent variables: (1) binary and (2) more than two levels?

I would like to test if a series of continuous IV predict two categorical variables (income level and gender). As you see, the DV are two, one of them is binary (gender) and the other has several ...
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1answer
151 views

How can I use a control variable that is non-normal in linear regression with other variables that are normal?

I am doing a moderation analysis. I have a predictor, a moderator and an outcome variable, all of which are normally distributed data. I very much need to add in a control variable. Otherwise my ...
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28 views

How to test the difference between dependent partial correlations in the presence of autocorrelation?

Background and what I am trying to do: I have a single dependent variable $Y$, and $K$ independent variables of interest ($X_1, X_2... X_k$). I also have a single independent variable of non-interest, ...
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2answers
22 views

Regression/classification models and dummy variables

I want to build regression model (linear and logit) but one of my independent variables is categorical variable with levels "Gym", "School", "Hospital", "Others"...
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1answer
27 views

How to derive variance from a regression model with only μ as the estimator (i.e. no coefficients)?

I am having trouble deriving the variance of this regression model given below: So far, I have managed to derive the OLS estimate but I just cannot wrap my head around how the variance is derived here:...
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1answer
15 views

Interpreting regression results with one log transformed independent variable

I have a following OLS regression model log(y) = a + b*x_1 + c*x_2 + d*log(x_3) + error I am now wondering what is the interpretation of the coefficient ...
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20 views

Correlation in the Bivariate probit regression?

Could you explain to me how to interpret the coefficients Rho and athrho in the bivariate probit regression. In my case the athRho is significantly different from zero (0.399), does this mean that the ...
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1answer
21 views

How can I express a logistic regression equation

I see a lot of examples of linear regression like this: y = a1x1 + a2x2 + a3x3 + a4x4 + (a3*a5)x5 + (a4a5)*x6. But I would like to write something similar for a logistic regression. I am not ...
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21 views

lavaan factor scores to feed other models

Let's suppose I have an outcome variable that is not continuous. For example, it could be an ordered categorical variable or a nominal (unordered) categorical variable. This implies that I would need ...
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1answer
22 views

Significant but low interaction coefficient value

I've adjusted a linear model: $Y = \beta_{0} + \beta_{1}X_{1}+\beta_{2}X_{2}+\beta_{3}X_{3}+\beta_{4}X_{1}X_{2}+\beta_{5}X_{2}X_{3}+\beta_{4}X_{1}X_{3}$. All coefficients are significant (p<2e-15) ...
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1answer
24 views

How to work with continuous overdispered data?

I have troubles with analysis of my data. I analyze cross-sectional data about users activities and spendings from mobile game . I have paying and non-paying users, I need to explain what independent ...
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1answer
29 views

What is the marginal effect when there is interaction in a regression model?

I have data which has experience(in months), genderMale, and exp*genderMale (interaction). My question is: What's the marginal ...
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29 views

Should i use ordinal logistic regression or multinomial logistic?

I want to do an analysis on word search performance. Word search performance is my dependent variable. A word puzzle was assigned and the correct number of words is the score, for example 5 correct ...
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1answer
19 views

Model individual differences by id or by numerical variables?

I want to understand the effect of IV on my DVs (each subject has multiple measurements). I realize that my default analysis approach is to use the subject id as the random variable and throw the IVs ...
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32 views

Interpreting coefficient when both dependent and independent variables are percentages (historical share of serfs across European Russian provinces) [closed]

I'm a bit confused about the correct interpretation of my coefficient. I have data for the historical share of serfs across European Russian provinces, and the historical share of urban population ...
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1answer
17 views

Why we divide the variables on the previous year

This is my first post here, and I hope to find an answer to my question. I have found a paper that examines the relationship between earnings management (absolute value of the abnormal working capital ...
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48 views

How to correct conditional Poisson standard errors for over-dispersion

I want to estimate a conditional negative binomial model, which an economist might call a negative binomial model with individual fixed effects. I use Python and statsmodels, which has a conditional ...
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9 views

Dealing with outliers when calculating body condition indexes

I am seeking advice on how to deal with outliers when trying to calculate residual body condition indexes (by using the residuals from a regression of body mass with body length). I am using a body ...
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1answer
59 views

Naming a problem with multiple regression curves

I've got a "naming" problem: I've got some individuals (say, a hundred), and for each individual I record 10 points to obtain a regression curve. I want to test if those regression curves ...
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15 views

How to collapse models with different intervals of a key variable?

We have some clinical variables on patients under a disease partially characterized by a key clinical variable. To study how this disease behaves in different levels of the key clinical variable, we ...
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4 views

DFBeta Measures for Subsets of Variables

I need to calculate the DFBETA statistics for a logistic regression. The number of columns in the $X$ matrix is $3000$ and the number of records is in the millions. I've been using R's ...
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32 views

Are their feature selection always go by linear model?

A generalized linear model maps a linear transformation of features to some response through monotonic function, does GLM feature selection always go by analyzing the coefs of this linear ...
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26 views

Difference between a multivariate normal regression and multiple regressions with shared random effect

Let's $Y_1$, $Y_2$ be two random variables representing two outcomes and $X$ a covariate. I want two regress $Y=(Y_1,Y_2)$ on X, but by taking into account the potential correlation between $Y_1$ and $...
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2answers
124 views

Estimated linear model: notation, properties of residuals

Let us say we have a linear model $$ Y=\beta_0+\beta_1X+\varepsilon, \quad \varepsilon|X\sim i.i.N(0,\sigma^2). $$ Suppose estimation yields $(\hat\beta_0,\hat\beta_1,\hat\sigma^2)=(5,2,9)$. Question ...
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24 views

What is the sampling distributions underlying a moderated (interaction) effect?

I'm trying to derive the underlying sampling distribution of moderated effects (product of two continuous random variables), to compute its statistical power. At this point, I'm mostly interested in ...
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23 views

Centering continuous variables in a multilevel logistic regression model

We are fitting a two-level logistic regression model with continuous variables in Level 1 and no variables in Level 2, except the groups. We considered only a random intercept in the model, but it is ...
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15 views

Why I get same predictions values for diferent input data?

I am newbie in the neural network world and actually I build my first neural network but for some reason when I use de trained model to predict ,giving by myselft the data I want it to predict it, ...
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1answer
26 views

Can I use linear regression when one group has positive correlation and another negative of X with Y?

I am wondering if I can use regression of the type Y=a+b1X1D1+b2X2D1+b3X3D1+... As a result, I am planning to use dummy group variable for each of X's. The assumption of the linear regression tells ...

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