R Squared or Coefficient of Determination: Interpretation, Calculation, & Visual Explanation with Examples; 👉🏼Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1
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This statistics video tutorial will help you answer the following questions:
What does R squared mean? Why is R squared important? What is the interpretation of the coefficient of determination? Is R squared the same as the correlation coefficient? How do you calculate R squared (R2)? What is the difference between the correlation coefficient and the coefficient of determination? What are the limitations of R squared?
► This video introduces the concept of R-Squared, a measure of model fit. While we show the formula, the focus is on the concept of R squared. In the case of simple linear regression, R squared is equal to Pearson's correlation coefficient squared.
► R squared tells us the percentage of the variability in Y that can be explained by (or attributed to) our model (or to X in the case of simple linear regression). Of all the variability in Y, what percentage of the variability can be explained by the terms in our model?
► R squared takes on values between 0 and 1, with values closer to 1 indicating a better model fit.
► One limitation of R squared as a measure of model fit is that the same set of data is used to fit the model (estimate its parameters) and then that same data is used to calculate R squared, the measure of model fit. In other words, we measure how well the model can predict the data that was used to build the model. Regardless, it is still a useful measure of model fit.
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