Understanding R Squared Regression for Josh

In summary, R Squared regression is a statistical measure used to determine the strength of the relationship between variables and make predictions. It is calculated by dividing the sum of squares of the regression by the total sum of squares, resulting in a value between 0 and 1. A good R Squared value is typically 0.7 or higher, with lower values indicating a weaker relationship. R Squared cannot be negative, but it can be close to 0. It can be used to evaluate the goodness of fit for a regression model, compare different models, and identify outliers or influential data points.
  • #1
JoshMaths
26
0
http://imgur.com/LAOgGyY
http://imgur.com/LAOgGyY

I know the components of R2 as in ESS, TSS, RSS.
I know cov(x,y) = [[itex]\sum[/itex](yi - ybar)(xi - xbar)]/n-1

But that's as far as I can go, I have come across lowercase r yet or the sample correlation and proofing how they all fit is a bit beyond me so if someone can provide some help that would be great thanks.

This is not homework.

Josh
 
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  • #2
The link is difficult to read.
 
  • #3
Sorry about that. Didn't come up in the test so I am all good.
 

Related to Understanding R Squared Regression for Josh

1. What is R Squared regression and why is it important?

R Squared regression is a statistical measure that represents the proportion of variation in a dependent variable that can be explained by the independent variable(s). It is important because it helps determine the strength of the relationship between the variables and can be used to make predictions.

2. How is R Squared calculated?

R Squared is calculated by dividing the sum of squares of the regression by the total sum of squares. This results in a value between 0 and 1, with 1 representing a perfect fit and 0 representing no relationship between the variables.

3. What is a good R Squared value?

A good R Squared value depends on the context and the field of study. In general, an R Squared value of 0.7 or higher is considered a strong fit, while values below 0.5 may indicate a weak relationship.

4. Can R Squared be negative?

No, R Squared cannot be negative as it represents the proportion of variation that can be explained by the independent variable(s). However, it can be close to 0, indicating a weak relationship.

5. How can R Squared be used to evaluate a regression model?

R Squared can be used as a measure of goodness of fit for the model. It can also be compared to the R Squared values of other models to determine which one is a better fit for the data. Additionally, it can be used to identify outliers or influential data points that may be affecting the model's performance.

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