The coefficient of determination, also known as the R-squared value, is a statistical measure that tells you how well a data set fits a mathematical model. In other words, it tells you how close the data points are to the line of best fit. The coefficient of determination ranges from 0 to 1, with 1 indicating a perfect fit.

To calculate the coefficient of determination, you first need to calculate the sum of the squares of the residuals. The residual is the difference between the actual data point and the predicted value. The sum of the squares of the residuals is then divided by the sum of the squares of the differences between the actual data values and the mean of the data set. The result is multiplied by 100 to get the coefficient of determination.

The coefficient of determination can be used to compare different data sets or different models. A higher R-squared value indicates a better fit.

##### What is the coefficient of determination and what does it measure quizlet?

The coefficient of determination is a statistical measure that determines how well a regression model explains the variation in the dependent variable. It is a measure of the amount of variation in the dependent variable that is explained by the independent variable. The coefficient of determination is also known as the R-squared. How do you interpret r2? The r-squared value, also known as the coefficient of determination, is a statistical measure that represents the percentage of variability in a data set that can be explained by a linear regression model. In other words, it tells you how well a linear regression model fits a data set.

The r-squared value can range from 0 to 1, where 0 indicates that the linear regression model does not explain any of the variability in the data set, and 1 indicates that the linear regression model explains all of the variability in the data set.

Generally speaking, the closer the r-squared value is to 1, the better the linear regression model fits the data set.

How do you find coefficient of determination in statistics? The coefficient of determination is a measure of how well a linear regression model explains the variance of the data. It is calculated as the percentage of the variance of the data that is explained by the model. The coefficient of determination can be used to compare different linear regression models. A higher coefficient of determination indicates a better fit. What is a good r-squared value? A good r-squared value is a value that is close to 1.0. This indicates that the model is a good fit for the data.

#### What is the single best answer regarding coefficient of determination?

The coefficient of determination is a statistical measure of how well a model explains the variation in a dependent variable. It is a number between 0 and 1, where 0 indicates that the model does not explain any of the variation in the dependent variable, and 1 indicates that the model explains all of the variation in the dependent variable.