## 10 Green Public Procurement Applied in Organic Agriculture in Romania
The correlation matrix shows a strong positive correlation between the variable _Total area in organic farming_ and _Cereals_ (0.96), _Permanent crops (ha) vineards, cultivated fruit bushes_ (0.81), and _Green harvested plants_ (0,80). The first conclusion is that most of the organic farming is represented by cereals, permanent crops and Green harvested plants. The second conclusion is that industrial crops are represented by Permanent crops (ha) vineards, cultivated fruit bushes (0.89), and Dried legumes and protein crops (including seeds and mixtures of cereals and legumes) (0.80). Our model is significant because the test assumptions are met, and the statistics are relevant. The test assumptions are: our variables are continuous, there is a linear relationship between the independent variables and dependent variables (graphic scatterplot), there are no extreme values, residuals are not correlated (Durbin-Watson coefficient 2214, included in the interval \(1.5<\) d \(<\) 2.5, Table 3), the dates demonstrate homoskedasticity and residuals have an approximately normal curve (Figure 5 P-P plot). Overall, the model is very representative (R\({}^{2}\) is 0.971). The variation of the independent variable (_creals, green_harv, uncultivated_) explains 97% of the variation of the dependent variable (_total cultivated land_). The other percentages should be explained by other variables such as dried legumes and protein crops, tuberous and root plants, industrial crops, vegetables, and permanent crops. The ANOVA test confirms the before mentioned findings because F > F crit and Sig F < 0.01 (Table 3). Figure 5: P-P plot for each analyzed variable. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & & \multicolumn{6}{c}{**Model Summary b**} \\ \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{**R**} & \multirow{3}{*}{\
\begin{tabular}{l} **R** \\ **Square** \\ **R** \\ **Square** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{l} **Adjusted** \\ **R** \\ **Square** \\ \end{tabular} } & \multicolumn{6}{c}{**Std. Error of**} & \multicolumn{6}{c}{**Change Statistics**} & \multicolumn{1}{c}{} \\ \cline{3-10}  & & & & **RSquare** & & **RSquare** & **F Change** & **df1** & **df2** & **Sig. F** & **Durbin-Watson** \\ \hline
1 & 0.985 a & 0.971 & 0.957 & 12,444.97670 & 0.971 & 67.256 & 3 & 6 & 0.000 & 2.214 \\ \hline \multicolumn{10}{c}{**ANOVA b**} \\ \hline \multicolumn{10}{c}{**Model**} & \multirow{3}{*}{\
\begin{tabular}{l} **Sum of Squares** \\ **R** \\ **Square** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{l} **Df** \\ **R** \\ **Square** \\ \end{tabular} } & \multicolumn{6}{c}{**Mean Square**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{**Sig.**} \\ \hline \multirow{3}{*}{1} & Regression & 31,249,080,149.865 & 3 & 10,416,360,049.955 & 67.256 & 0.000 a & \\ \cline{2-10}  & Residual & 929,264,670.535 & 6 & 154,877,445.089 & & & \\ \cline{1-1} \cline{2-10}  & Total & 32,178,344,820.400 & 9 & & & & & \\ \hline \hline \end{tabular}  
* Predictors: (Constant), Uncultivated, Cereals, Green_harvested; b Dependent Variable: Organic_farm.  
\end{table} 
Table 3: Model Significance. The R coefficient is 0.99, bigger than the minimum limit of 0.95 as the model to be reliable. The T-test marginal significance level for estimated coefficients is less than only 0.01 for the cereals' coefficient, meaning that this coefficient is very well estimated, and the others could be better estimated. Following the statistical test F, the resulting coefficient for _ cereals_ variable is 2.4, with a probability of guaranteeing results (Prob) of 0.01 (less than the sensitivity threshold of 0.05) so this variable's coefficient is well estimated. The coefficient for _green_harv_ variable is 0.8 with 95% likelihood, it will be found in the interval [\(-\)1.1:2.7]. The coefficient for the _uncultivated_ variable is \(-\)6.06, with 95% likelihood, it will be found in the interval [\(-\)16.16; 4.04].