What does it mean if my R2 value is 1?

R^2 = 1 means you have a perfect fit of your data from a simple linear model.
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What does an R2 value of 1 mean?

An R2=1 indicates perfect fit. That is, you've explained all of the variance that there is to explain. In ordinary least squares (OLS) regression (the most typical type), your coefficients are already optimized to maximize the degree of model fit (R2) for your variables and all linear transforms of your variables.
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Is a R2 value of 1 GOOD?

A value of 1 indicates that the response variable can be perfectly explained without error by the predictor variable. In practice, you will likely never see a value of 0 or 1 for R-squared.
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Is R2 close to 1 GOOD?

In fact, if R-squared is very close to 1, and the data consists of time series, this is usually a bad sign rather than a good one: there will often be significant time patterns in the errors, as in the example above.
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What R2 value is acceptable?

Since R2 value is adopted in various research discipline, there is no standard guideline to determine the level of predictive acceptance. Henseler (2009) proposed a rule of thumb for acceptable R2 with 0.75, 0.50, and 0.25 are described as substantial, moderate and weak respectively.
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R-squared, Clearly Explained!!!



What does an R2 value of 0.1 mean?

R-square value tells you how much variation is explained by your model. So 0.1 R-square means that your model explains 10% of variation within the data. The greater R-square the better the model.
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Can R2 of a regression be greater than 1?

Bottom line: R2 can be greater than 1.0 only when an invalid (or nonstandard) equation is used to compute R2 and when the chosen model (with constraints, if any) fits the data really poorly, worse than the fit of a horizontal line.
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What does a low r2 value mean?

A low R-squared value indicates that your independent variable is not explaining much in the variation of your dependent variable - regardless of the variable significance, this is letting you know that the identified independent variable, even though significant, is not accounting for much of the mean of your ...
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What does an r2 value of 0.99 mean?

Practically R-square value 0.90-0.93 or 0.99 both are considered very high and fall under the accepted range. However, in multiple regression, number of sample and predictor might unnecessarily increase the R-square value, thus an adjusted R-square is much valuable.
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What is a strong R-squared?

Any study that attempts to predict human behavior will tend to have R-squared values less than 50%. However, if you analyze a physical process and have very good measurements, you might expect R-squared values over 90%. There is no one-size fits all best answer for how high R-squared should be.
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Is an R2 of 1 bad?

An R2=1 means that the data is perfectly correlated. This is reflected in your standard errors being 0.
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How do you tell if a regression model is a good fit?

The best fit line is the one that minimises sum of squared differences between actual and estimated results. Taking average of minimum sum of squared difference is known as Mean Squared Error (MSE). Smaller the value, better the regression model.
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Can an R value be greater than 1?

Correlation coefficient cannot be greater than 1.
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Is a low r2 bad?

R-squared does not indicate whether a regression model is adequate. You can have a low R-squared value for a good model, or a high R-squared value for a model that does not fit the data! The R-squared in your output is a biased estimate of the population R-squared.
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How do you interpret regression results?

The sign of a regression coefficient tells you whether there is a positive or negative correlation between each independent variable and the dependent variable. A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase.
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What if the R-squared is less than 1?

As above, since the sum of squared errors is positive, R-square should be less than one, so such a result as yours would be due to the algorithm, sample size, round-off, or some other such cause, if over one and not an outright error.
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How do you explain R-squared?

R-squared is the percentage of the dependent variable variation that a linear model explains. 0% represents a model that does not explain any of the variation in the response variable around its mean. The mean of the dependent variable predicts the dependent variable as well as the regression model.
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What does it mean when R2 is 0?

R2=0 implies that the linear model is not better than the model using the mean, namely a confirmation that indeed it is not appropriate.
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Is 1.01 a correlation coefficient?

The values range between -1.0 and 1.0. A calculated number greater than 1.0 or less than -1.0 means that there was an error in the correlation measurement. A correlation of -1.0 shows a perfect negative correlation, while a correlation of 1.0 shows a perfect positive correlation.
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What does a correlation close to 1 mean?

A correlation coefficient, often expressed as r, indicates a measure of the direction and strength of a relationship between two variables. When the r value is closer to +1 or -1, it indicates that there is a stronger linear relationship between the two variables.
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Why is the correlation coefficient between 1 and 1?

Pearson's Correlation Coefficient is a linear correlation coefficient that returns a value of between -1 and +1. A -1 means there is a strong negative correlation and +1 means that there is a strong positive correlation. A 0 means that there is no correlation (this is also called zero correlation).
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Why R2 is not a good measure?

R-squared does not measure goodness of fit. R-squared does not measure predictive error. R-squared does not allow you to compare models using transformed responses. R-squared does not measure how one variable explains another.
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What is a good mean squared error?

An ideal Mean Squared Error (MSE) value is 0.0, which means that all predicted values matched the expected values exactly. MSE is most useful when the dataset contains outliers , or unexpected values (too high values or too low values).
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What is a good R-squared value for a trendline?

Trendline reliability A trendline is most reliable when its R-squared value is at or near 1.
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Do you want a high or low adjusted R-squared?

R-squared measures the goodness of fit of a regression model. Hence, a higher R-squared indicates the model is a good fit while a lower R-squared indicates the model is not a good fit.
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