The following table contains the indicators: GDP per inhabitant and the volume of greenhouse gas emissions in transport activity in Romania during the period 1995-2014.

Using the function =CORREL(array_1,array_2) of Excel, calculate the correlation coefficient and appreciate the intensity and the link nature, for the required period (write with 3 decimals).

The simple correlation coefficient between the two indicators for the period 2000-2014 is (with 3 decimals): .

The nature of correlation between the two indicators is 

The intensity of the correlation is

The simple correlation coefficient between the two indicators for the period 1995-2014 is (with 3 decimals) .

The intensity of the correlation is 

For the period 2000-2014 the correlation is

Use the functions intercept and slope to identify the estimators of the simple regression model for the greenhouse gas emissions depending on the real GDP per capita for the entire period 1995-2014.

The free term (with 3 decimals) is estimated a₀= and the regression coefficient (with 6 decimals) is estimated a₁=.

Without considering the GDP per capita, the greenhouse gas emissions from transports activity, yearly recorded an average quantity of (3 decimals) millions tonnes CO₂ equivalent.

For each euro added to the GDP per capita, the greenhouse gas emissions increased yearly in average with (round at integer; attention to the unit measure) tonnes CO₂ equivalent.

Calculate the theoretical values using the identified linear model and then the residuals.

The maximum positive deviation of the empirical values of greenhouse gas emissions from to the model was the residual value of (3 decimals) millions tonnes CO₂ equivalent, recorded in the year .

The maximum negative deviation of the empirical values of greenhouse gas emissions from to the model was the residual value of (3 decimals) millions tonnes CO₂ equivalent, recorded in the year .

Make the chart of correlation between y and x.

Make the chart of evolution for y variable. On both charts add the theoretical y values.

Specify the number of y values lower and equal than the theoretical coresponding values: .

Specify the number of y values higher than the theoretical coresponding values: .

Make also the chart of x variable evolution.

Looking at the chart of the theoretical y variable evolution (on the same chart with y variable evolution) you may conclude that it looks