les The Unit Coordinator for Business Statistics is currently conducting a research on the impact of temperature changes on residential energy (RE) consumption. The research team is using Cooling Degree Days (CDD) to measure the need to cool the inside of a building to a comfortable target temperature and the number of days the cooling is needed. Below is the regression estimation results for a rather simple version of the model. The dependant variable is residential energy consumption per capita in kilotonne of oil equivalent (ktoe) for a year and the independent variable is the CDDs for the corresponding year. Regression Statistics Multiple R 0.4690 R Square 0.2200 Adjusted R Square 0.2053 Standard Error 2.8690 Observations 55 Coefficients Standard t Stat P-value Lower Upper Intercept 6.60 4.0677 1.6233 0.1105 -1.5559 14.7617 CDD 0.02 0.0040 3.8660 0.0003 0.0074 0.0234 25. With reference to information provided in the Coefficients table, state the values of the intercept and slope coefficient and provide practical interpretations for both intercept and slope coefficients in the context of this section. Q6. State the least squares linear regression equation for this relationship