Statistics
Given the initial measured values Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle x_0} , final observed or measured values Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle x_m} and final calculated values Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle x_c} , there are several goodness of fit statistics or skill scores which can be calculated. The definition for some of the more common ones are provided below.
Brier Skill Score
The Bier Skill Score (BSS) is given by \begin{equation} \tag{1}
BSS = 1 - \frac{\bigg\langle \big(x_m-x_c\big)^2 \bigg\rangle}{\bigg \langle \big(x_m-x_0\big)^2 \bigg\rangle }
\end{equation}
where is the measured or observed values, is the calculated values, is the initial measured values and the angled brackets indicate averaging. The BSS ranges between negative infinity and one. A BSS value of 1 indicates a perfect agreement between measured and calculated values. Scores equal to or less than 0 indicates that the mean observed value is as or more accurate than the calculated values.
Nash-Sutcliffe Coefficient
The Nash-Sutcliffe Coefficient (E) is commonly used to assess the predictive power of a model. It is defined as
\begin{equation} \tag{2}
E = 1 - \frac{\bigg\langle \big(x_m-x_c\big)^2 \bigg\rangle}{\bigg\langle \big(x_m- \langle x_m \rangle \big)^2 \bigg\rangle }
\end{equation}
where is the measured or observed values, is the calculated values, and the angled brackets indicate averaging. The Nash-Sutcliffe efficiency coefficient ranges from negative infinity to one. An efficiency of 1 corresponds to a perfect match between measured and calculated values. An efficiencies equal 0 or less indicates that the mean observed value is as or more accurate than the calculated values.
Root-Mean-Squared Error
The Root-Mean-Squared Error (RMSE) also referred to as Root-Mean-Squared Deviation (RMSD) is defined as
\begin{equation} \tag{3}
RMSE = \sqrt{ \bigg\langle \big( x_m - x_c \big)^2 \bigg\rangle }
\end{equation}
where where is the measured or observed values, is the calculated values, and the angled brackets indicate averaging. The RMSE has the same units as the measured and calculated data.
Normalized-Root-Mean-Squared Error
In order to make comparing different RMSE with different units or scales (lab vs field) several non-dimensional forms of the RMSE have been proposed in literature. Here the Normalized-Root-Mean-Squared Error (NRMSE) is defined as
\begin{equation} \tag{4}
NRMSE = \frac{\sqrt{ \bigg\langle \big( x_m - x_c \big)^2 \bigg\rangle }}{\max{(x_m)}-\min{(x_m)}}
\end{equation}
where is the measured or observed values, is the calculated values, and the angled brackets indicate averaging. The NRMSE is often expressed in units of percent. Smaller values indicate a better agreement between measured and calculated values.
Mean-Absolute Error
\begin{equation} \tag{5}
MAE = \bigg\langle \big| x_m - x_c \big| \bigg\rangle
\end{equation}
where where is the measured or observed values, is the calculated values, and the angled brackets indicate averaging.
Normalized-Mean-Absolute Error
The normalized-Mean-Absolute Error is defined as
\begin{equation} \tag{6}
NMAE = \frac{\bigg\langle \big| x_m - x_c \big| \bigg\rangle }{ \max{(x_m)}-\min{(x_m)} }
\end{equation}
where is the measured or observed values, is the calculated values.
Correlation coefficient is defined as
Correlation is a measure of the strength and direction of a linear relationship between two variables. The correlation coefficient is defined as
\begin{equation} \tag{7}
R = \frac { \langle x_m x_c \rangle - \langle x_m \rangle \langle x_c \rangle }{ \sqrt{ \langle x_m^2 \rangle - \langle x_m \rangle ^2} \sqrt{ \langle x_c^2 \rangle - \langle x_c \rangle ^2} }
\end{equation}
where where is the measured or observed values, is the calculated values, and the angled brackets indicate averaging. A correlation of 1 indicates a perfect one-to-one linear relationship and -1 indicates a negative relationship. The square of the correlation coefficient describes how much of the variance between two variables is described by a linear fit.
Bias
The bias is a measure of the over or under estimation and is defined as
\begin{equation} \tag{8} B = \langle x_c - x_m \rangle \end{equation}
where is the measured or observed values, is the calculated values, and the angled brackets indicate averaging. The bias is a measure of the over or under prediction of a variable. Positive values indicate overprediction and negative values indicate underprediction.
Normalized Bias
The normalized bias is a measure of the over or under estimation and is defined as \begin{equation} \tag{9} NB = \frac{\langle x_c - x_m \rangle}{\max{(x_m)}-\min{(x_m)}} \end{equation}
where Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle x_m} is the measured or observed values, Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://en.wikipedia.org/api/rest_v1/":): {\displaystyle x_c} is the calculated values, and the angled brackets indicate averaging. The normalized bias is a measure of the over or under prediction of a variable and is often expressed as a percentage. Positive values indicate overprediction and negative values indicate underprediction.