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An online coefficient of determination calculator allows you to determine the correlation coefficient, Rsquared (coefficient of determination) value of the given variable dataset. This r value calculator displays the relationship among the given two datasets and predicts the preciseness of future outcomes. In the text, you can understand better what is R squared in statistics, and how to find coefficient of determination with the R squared formula.
In statistics, the coefficient of determination is also called the Rsquared value, which is the result of the regression analysis method. The coefficient of determination measures how closely the actual data points match the regression prediction, thereby quantifying the strength of the linear relationship between the explanatory variable and the explained variable. So, it is sometimes called model fitting. In most cases, the coefficient of determination is called R2 that referred to as "Rsquared" for short. However, an online Coefficient of Variation Calculator helps to evaluate the coefficient of variation corresponding to the given dataset values.
There are multiple Formulas used by the R value calculator to compute the coefficient of determination:
Correlation Coefficient = Σ [(A – A_m) * (B – B_m)] / \sqrt{ [Σ (A – A_m)^2 * Σ (B – B_m)^2]}
Where,
A are data points in the data set A
B is the data points in the data set B
A_m is the mean of data set A
B_m is the Mean of data set Y
Then,
Coefficient of Determination = (Correlation Coefficient)^2
The following formula used by the coefficient of determination calculator for regression outputs:
R2 (Coefficient of Determination) = Explained Variation / Total Variation
R2 (Coefficient of Determination) = MSS / TSS
R2 (Coefficient of Determination) = (TSS – RSS) / TSS
Where:
Total Sum of Squares (TSS) = Σ (Y_i – Y_m)^2
Model Sum of Squares (MSS) = Σ (Y^ – Y_m)^2
Residual Sum of Squares (RSS) = Σ (Yi – Y^)^2
Y^ is the predicted value, Ym is the mean value, and Y_i is the ith value of the model.
However, the Covariance Calculator estimates the covariance between two random variables X and Y in statistics experiments.
Find the coefficient of determination of: (12, 13, 23, 44, 55), (17, 10, 20, 14, 35).
When you substitute these datasets in the r squared calculator, it calculates the coefficient of determination as:
When you substitute the same values in the r2 calculator, it shows similar table for the given regression model.

Number of values (n) = 5
Now, coefficient of determination calculator find \( SS_{xx}\):
\( SS_{xx}\) = ∑^n_{i = 1} X^2_i  1/n (∑^n_{i = 1} X^2_i)^2
\( SS_{xx}\) = 5803 – 1/5 * 21609
\( SS_{xx}\) = 1481.2
In the next step, find \( SS_{yy}\):
\( SS_{yy}\) = ∑^n_{i = 1} Y^2_i  1/n (∑^n_{i = 1} Y^2_i)^2
\( SS_{yy}\) = 2210 – 1/5 * 9216
\( SS_{yy}\) = 366.8
Now, find \( SS_{xy}\):
\( SS_{xy}\) = ∑^n_{i = 1} X^2_i Y^2_i  1/n (∑^n_{i = 1} X^2_i) (∑^n_{i = 1} Y^2_i)
\( SS_{xy}\) = 3335 – 1/5 * 14112
\( SS_{xy}\) = 512.6
Then, R squared calculator finds Correlation Coefficient
R = \frac{ 512.6 } { \sqrt { 1481.2 * 366.8} }r = \frac{ SS_{xy} } { \sqrt { SS_{xx} SS_{yy} }
R = 0.6954
Hence, the coefficient of determination calculator finds the coefficient of determination
(R^2) R^2 = (0.6954)^2
R^2 = 0.4836
In the simple linear leastsquares regression, Y ~ aX + b, the square of the Pearson correlation coefficient coincides with the coefficient of determination (R Squared) among the x_1, x_2, ..., x_n and y_1, y_2 ..., y_n.
The coefficient of determination is a number between 0 and 1 that can be converted to a percentage by multiplying it by 100. The standard interpretation is the amount of change in y, which can be explained by the x. In other words, the data also corresponds to the regression model you are using and describes it. However, it is crucial to note the general caveats of the relevant data. These two variables can be related to each other without any causal relationship.
The r value calculator provides the variability of a factor in relation to another factor by following these steps:
First of all, substitute datasets in both fields that are separated with commas. Hit the calculate button for the solution.
An extremely reliable model for future predictions and a value of 1.0 indicates a perfect fit, while a value of 0.0 indicates that the computation fails to accurately model the data.
The multiple coefficients of determination (R2) measure the amount of change in the dependent variable, which can be predicted based on the explanatory variable set in the multiple regression equation.
In statistics, the correlation coefficient r measures the direction and strength of the linear relationship between two different variables on the scatter plot. The r value is always in the range of +1 to 1.
The NonDetermination Coefficient explains the amount of unaccounted, or unexplained, the variance between the two variables, or a set of variables (predictors) in the outcome variable. Where the NonDetermination Coefficient is simply 1 – R2.
The coefficient of determination calculator finds the correlation coefficient, r squared for the given regression model. Also, provide interpretation in the form of variance percentage in datasets. This calculator provides the solution in different ways such as the regression sum method and correlation coefficient method.
From the source of Wikipedia: Coefficient of determination, Relation to unexplained variance, explained variance, squared correlation coefficient, Interpretation. From the source of Investopedia: Understanding the Coefficient of Determination, Graphing the Coefficient of Determination, multivariate linear model. From the source of Stat Trek: Coefficient of Determination, linear regression, standard deviation.
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