The following scatterplot shows the mean annual carbon dioxide (CO2 ) in A regression predicting mean temperature from CO2 produces the parts per million (ppm) measured at the top of a mountain and the mean following output table. annual air temperature over both land and sea across the globe, in degrees Dependent variable is: Temperature Celsius (C). R-squared =33.9% 16.800 Variable Coefficient 16.725 Intercept 15.606 CO2 0.003 8. 16 650 a) What is the correlation between CO2 and Temperature? 16.500 r = (Round to three decimal places as needed.) 325.0 1375 350.0 CO. (ppm) b) Explain the meaning of R-squared in this context. A. Mean temperature accounts for 66.1% of the variation in CO2 levels. O B. CO2 levels account for 66.1% of the variation in mean temperature. O c. CO2 levels account for 33.9% of the variation in mean temperature. O D. Mean temperature accounts for 33.9% of the variation in CO2 levels. Dependent variable is: Temperature R-squared # 33.9% Variable Coefficient Intercept 15.606 CO2 0.003 c) Give the regression equation. Temp =+(co2) (Type integers or decimals.) d) What is the meaning of the slope of this equation? O A. For every degree that the mean temperature increases, CO2 levels increase by 0.003 ppm. O B. For every 1 ppm increase in CO2 levels, the mean temperature increases by 0,003*C. O C. For every 0.003 ppm increase in CO2 levels, the mean temperature increases by 1"C. e) What is the meaning of the y-intercept of this equation? O A. When the CO2 level is 0 ppm, the global mean temperature will be 15.606"C. O B. When the global mean temperature is ("C, the CO2 level is 15.606 ppm. O C. The y-intercept does not have any meaning in the context of this problem. f) Suppose CO2 levels reach 367 ppm this year. What mean temperature does the regression predict from this information? (Round to four decimal places as needed.)