Questions tagged [regression]

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

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A regressor performs better under a certain regime — how to condition that regressor to make regression better?

I ran a regression $Y \sim X_1 + X_2 + .. X_n$. I find out what one regressor , $X_1$'s performance depends on another variable $t$ (not in the regression). So basically if I bucket by $t$, within ...
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prediction model for a cross sectional data for ONE YEAR

I have a set of cross sectional database in which are the scores of 30000 students in different subjects plus their sex and type of school, it looks like this (13 explanatory variables) All these ...
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Mutiple regression analysis [closed]

Can anyone help with the multiple regression analysis and interpretation of these data set? Score in the final exam is the dependent variable while the others are the independent variables. Thank you!...
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Estimating WTA (or WTP) from a logit model

I am a beginner at writing and estimating models and will appreciate the community's help. I have some data on whether individuals accepted or rejected some tasks that they were assigned. Here's a ...
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Unbalanced residuals in mortality prediction (aquaculture) [closed]

*I'm working on predicting mortality in aquaculture (fish farming), when the better the score the more the mean of the predictions deviate from the target mean. The distribution of the target data ...
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Which is the best way to analyze a pooled cross sectional data? [closed]

I'm currently estimating the effect of the type of sovereign debt restructuring have on economic growth. My dependent variable is cumulative growth rate of gdp per capita for 5 years. I've given an ...
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Where does this formula for the effect of the mediator on the outcome comes from?

I am reading the paper Caron, P.-O., & Valois, P. 2018. A computational description of simple mediation analysis. The Quantitative Methods for Psychology, 14(2): 147–158. There there is the ...
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1answer
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Neural network using Orange

I'm using Orange Data Mining in a regression analysis applying Artificial Neural Network (ANN). Some works suggest defining the number of neurons in the input layer as the number of variables. The ...
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Finding the coefficient of determination from a regression line?

Suppose you are given the following estimated model from a sample of size 1217: $\hat{y} = 1.177663 + 0.0910103x$ and the standard errors of the coefficients are $0.0865446$ and $0.0065643$ ...
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what is the relationship between the two standard errors

I have two variables $X$ and $Y$. Consider that there is one sample with 1000 observations, we can get the standard error of coefficient by this equation: $se(\hat\beta) = \sqrt{\sigma^2(X^TX)^{-1}}$. ...
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Seeing fair correlation at some points and zero correlation at many points between dependent and independent variable ; how to go about treating this

I have a dataset with and dependent and independent variable have an odd style of correlation
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1answer
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Normalizing/Scaling a dataset does not have any effect on r2 score?

I have this question on my mind for some time now, but unable to find some thorough explanation around this. While working on the Boston housing data set, scaling the data has no effect on the output. ...
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Is using percentile data for linear regression a valid approach?

With this article from the CDC, I'm creating a log-log plot of all percentiles for the weight of men aged 25-34 (page 32) against all percentiles for the height of men aged 25-34 (page 33). So the ...
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Maximum likelihood estimation of a gamma distribution mixed with linear regression in R

I have this excercise in R and I don't know where to start. The income dataset was collected with intervals instead of the actual number. The following table shows every interval in the dataset. $y_i$...
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Is there a test to find if it is a matter of Poisson or simple regression?

I want to measure the percentage of visits to transit stations during the pandemic (using Google Mobility Reports), according to other variables (% of vaccinated people, % of visits to the workplace ...
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Establishing relationship between two variables with low sample size(5)

My question is - Is elevation range of plants linked to intraspecific trait variability. I want to explore the relationship between the coefficient of variance (CV) of a certain plant functional ...
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Would a machine learning algorithm run faster on a pre-clustered dataset than its non-clustered equivalent? [closed]

Suppose we want to perform regression/classification on some arbitrary dataset. I read somewhere (can't recall where) that clustering is sometimes used as a preprocessing step before further learning ...
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Ordinal predictor treated as continuous in multiple linear regression: testing deviation from linearity with SPSS

I am running a multiple linear regression with a continuous DV and a number of independent variables, one of which is ordinal (three-levels). I am trying to follow David J Pasta’s instructions to ...
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How much importance should I give to R2 and p-value when analyzing a trend?

I am working on a small dataset, with data points regarding 21 geographic regions of my country. The dataset is composed of numeric variables only, so I've performed some linear regression to verify ...
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Random effects : Number of coefficients more then individuals

I'm trying to estimate a random effects model in R using the plm function. my data consists of financial and environmental variables for 6 banks from between the year 2012 and 2020. when i run the <...
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Simple linear regression model without intercept

I am a new beginner at linear regression. So here is my question: given that we have simple linear regression without the intercept: $$y_{i}=\beta x_{i}+\varepsilon_{i}$$ the question assumes that $\...
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34 views

Confidence Interval for least squares estimator

There was a paper by Yasin-Abbasi-Yadkori https://arxiv.org/pdf/1102.2670.pdf titled Online Least Squares Estimation with Self-Normalized Processes. I am trying to give a brief context before asking ...
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ANOVA for road accident data

I have a dataset showing the number of road accidents, persons killed and injured classified according to weather conditions(Sunny, Rainy, Foggy, Hail, Others) for 36 states. It kind of looks like the ...
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Calculating the n needed to discern a certain proportion effect in a linear regression

As title says, I was wondering how one would go about calculating the n needed to discern a certain proportion effect in a linear regression and additionally calculating the discernable effect given ...
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Difference-in-differences model with a matched control group

I need to run a difference-in-differences (DiD) model, but I'm not sure how to construct a formula for this. The problem is that the timing of events affecting the treatment group is not uniform, like ...
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Independent Variable has same value

i'm doing a regression using EViews, but one of eight independent variable has the same values for all sample and shows near singular matrix error, my supervisor said that it still could be done as ...
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Exclude NA values only and not entire rows in a lm in R?

If I have a dataset that looks like the following, looking at species richness of spiders in different habitats of a garden. ...
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1answer
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Full dataset with few repeated measures

I am dealing with a dataset with the majority of entries with one value per individual but, with three cases with 2 repeated measures each. My first approach would be to pursue linear mixed models, to ...
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1answer
29 views

Categorical variables in Linear Regression

I learned that in order to use categorical variables in Linear Regression models, I have to convert them to several binary dummy variables. Binary dummy variables can either be 0 or 1, so they aren't ...
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Which type of correlation is used when IV has four categories?

I am working on parenting styles (IV) that is a categorical variable with 4 categories. My DVs are personality traits and quality of life. I have created dummy variables (IV) for regression analysis. ...
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1answer
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Zero-inflated regression model for only independent variables?

My independent variable has a huge proportion of zeros in the data, and I've been thinking of using a zero-inflated Poisson regression to account for this. However, this method is only specified for ...
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How are regression parameter confidences found *for each parameter*?

I strongly feel that this question has been answered or explained somewhere, however, I'm struggling to find the correct terminology and resources to learn about this, and I believe this post may have ...
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Interpreting Hausman test results (FE vs RE)

I am deciding between a panel regression with fixed effects or random effects. The outcome variable here is labor force participation rate "lfp_rate", and one independent variable is poverty ...
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When should I use a dummy variable?

I have data for neighbourhoods with median income. I also have a standard low-income cutoff. (Just using regression analysis) I feel it would lead to a simpler and cleaner result if I just use the ...
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Definition of Linear Regression

What's the meaning of indexes in the definition of populational regression? $$Y_i=\beta_0+\beta_1X_i+e_i$$ When we have a sample the indexes make sense to me because we have each $x_i$ associate with ...
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Using different tiers of a single performance metric as discrete independent variables in a multivariable logistic regression

The TL;DR of the dataset is this: The data set I'm working with is a set of votes. Experts in a field vote for outcome A or outcome B and I'm running a logistic regression to get a feel for how the ...
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Are prediction results of linear regression inaccurate if data is not IID? [closed]

As I understand minimizing the sum-of-squares error function is equivalent to maximizing the likelihood function and in fact the sum-of-squares error function stems directly from the derivation of ...
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Assumptions of weighted least squares regression

I have built a weighted least squares regression model and was about to interpret the results. But before doing that, I wanted to check for assumptions first. However, I couldn't find the assumptions ...
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1answer
20 views

Example of response variables that are/ are not statistically independent

I have read that in regression there is no need for the response variable (independent variable) to be statistically independent. The source is https://en.wikipedia.org/wiki/...
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Understanding the proof for consistency of the OLS estimator

In my econometrics lecture we discussed the consistency of the OLS estimator ($\hat{\beta} \overset{P}{\rightarrow}\beta)$ and I don't understand why it holds that $(X'X)^{-1}=\Big(\frac{X'X}{N}\Big)^{...
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1answer
22 views

Simplifying the conditional expectation $ E[X|Y]=(-a +Y-E[e|Y])/b$ to find the slope of the Reverse Regression line

I have a pretty basic question about conditional expectation that is stumping me. Consider the real-valued random variables $Y$, $X$ and $e$, where $E[e] = 0$ and $X$ and $e$ are independent. Assuming ...
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1answer
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Causality comes form experiment manipulation or statistical calculation?

It is known that "Regression can only imply correlation but not causality" But whether a research can draw their variables to causality relationship is somewhat determined by whether the ...
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Any issues with conducting stratafied train/test splitting based on the distribution of a categorical predictor?

I am building a xgboost regressor for a dataset that includes a categorical feature with a very large number of levels (on average, each level has an observation frequency of only about .2%). However, ...
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In linear regression, is Bartlett's test for homoscedasticity used improperly?

Bartlett's test is used to test homoscedasticity between two or more independent normal populations (with unknown mean). The actual formula is not important for my question. The important thing is ...
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1answer
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Plugging in Regression Coefficients back into Cox PH Model

I am trying to plug the regression coefficients back into the cox ph model to manually find the partial hazard of the model, but am getting mixed results. I am using the lifelines python library for ...
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Using variance of y variable as weights for weighted linear regression when both x and y variables contain negative values?

I am currently dealing with a weighted linear regression problem in the context of an instrument calibration in analytical chemistry. Let's assume I have a response variable y and a predictor variable ...
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1answer
18 views

Regression with percentile independent variable

I am running a simple regression with average standardized exam scores at the school-level, and weekly household income at the neighborhood-level where the school is located. The two variables look as ...
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1answer
80 views

Multiple regression in SEM when the covariance between predictors is fixed to zero

I did a multiple regression with $x_1$ and $x_2$ predicting $y$, and found that I could recreate in SEM software (lavaan or AMOS) the same results I got doing things the regular way in R with the <...
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Can I double my dataset by including opposites?

The data set I'm working with is a set of votes. Experts in a field vote for outcome A or outcome B and I'm running a logistic regression to get a feel for how the intensity of the majority vote ...
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How does ordinal regression compare to quantile regression?

I am familiar with ordinal regression and quantile regression at a high level, but would like a deeper understanding of the two beginning on how they differ. Can someone compare and contrast the two, ...

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