Questions tagged [r-squared]

The coefficient of determination, usually symbolized by $R^2$, is the proportion of the total response variance explained by a regression model. Can also be used for various pseudo R-squared proposed, for instance for logistic regression (and other models.)

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How can I calculate the R-squared of a simple linear regression only with sample size, the coefficients and its standards deviations?

I have the following estimated simple linear regression, which was estimated from a sample of 1217 individuals: $\hat{y}=1.77663 + 0.0910103x$ where $\hat{\beta_0} = 1.77663$ and standard deviation of ...
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R2 for mixed-effects Conway-Maxwell Poisson using package glmmTMB

After running a mixed-effects Conway-Maxwell Poisson model using glmmTMB, I've printed the results using sjPlot's tab_model() but I don't know what R2 calculation is being used. Is it Nakagawa's R2 ...
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How to show VIF

The variance of the $j$th element of the OLS estimator is given by $$\operatorname{Var}\left(\hat{\beta}_{j}\right)=\sigma^{2}\left(X_{j}^{T} M_{-j} X_{j}\right)^{-1}$$ where $X_j$ is the column of ...
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$R^2$ explanation to commercial people

We have a representative digital panel that measures digital media consumption of panelist and then project it for the total population via weighting. Recently I ran a multiple regression on panel ...
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Is it correct to say one 'estimates' or 'measures' r-squared?

I am writing a report and am unsure about whether is correct to say one 'measures' r-squared or whether one 'estimates' it. I know the two words have two different semantic meanings, probably related ...
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$p$-value and $R^2$ of a Power Regression Model

I'm trying to perform a power regression model to fit my data. I used this script to find the angular coefficient and exponent of my power regression. ...
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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 $0.0065643$ ...
167 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 <...
154 views

Definition of partial R-squared?

I'm trying to understand the definition of Partial R-squared values in the context of a regression model. Does anyone have a layman's definition or an intuitive example that might help me better ...
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How to interpret variance explained and r-squared outcome from a multiple regression model in R?

I am running a regression model in R and I want to interpret the variance explained percentage, as well as, R-squared value. ...
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When and why should R squared be used instead of adjusted R squared?

The title says it all. In what situation R squared (non adjusted) is more useful and should be used instead of the adjusted one? Why?
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How to quantify and sum up the effect of each record of two time series on their correlation?

Let's say I have two one-day time series data. Each has 24 points (24 hours). ...
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Simple linear regression: R2 not equal to squared Pearson coefficient

The R2 of a simple linear regression model is the squared Pearson correlation coefficient (r) between the observations and the fitted values. Isn't the above in contradiction with the fact that the ...
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How to calculate the R Squared given limited information?

I am attempting to solve the following problem from my Statistics class: WALC=β_0 + β_1 TOTEXP + β_2 AGE + β_3 EDUC + β_4 SIZE + e. Suppose RSS=2032.73 and SD(WALC)=12.4537. N = 45. What is R Squared?...
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what does variation in X mean, linear regression and r squared

I was reading a document about the coefficient of determination and saw that r^2 shows how much of the variation in y is represented by the variation in x. What I couldn't understand was the part ...
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Nagelkerke pseudo R^2 interpretation in spatial regression

Could anyone explain to me the interpretation of Nagelkerke pseudo R^2 in spatial regression models? Definitions say it is computed using some conditonal probability, but I didn't quite get what they ...
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OLS- relation between R-squared an t-stat

I read here and in a comment by "Jeff" to the first answer here that $R^2= \frac{t^2}{t^2+df}$ I presume this is valid for simple linear regression only (with only one x variable, so not ...
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In a regression model, how to interpret a IC 95% for R-squared with negative values but with a significant effect of a predictor?

Imagine a regression with 3 predictors and one dependent variable with a sample size over 400. Only one predictor has a positive significant effect, p value equals to .02 aprox. R-squared is low (less ...
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Question regarding explained variation of individual regressors

If you regressed a dependent variable on multiple independent variables, would it be possible to figure out how much each regressor is explaining the variance of the dependent variable? Or can you ...
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What is the difference between correlation and explained variance?

Is there any meaningful difference between these two statements? X is highly correlated with Y X explains a lot of the variation of Y