d. Write the regression equations for: the data from Hubei province (without Wuhan): and the data from all of mainland China: e. For each additional case of COVID-19, how much higher is the number of deaths in Hubei province (without Wuhan)? All of mainland China? For every 100 additional cases of COVID-19, how much higher is the number of deaths in Hubei province (without Wuhan)? All of mainland China? Explain the practical implications of the difference between the last two answers. f. For the data from Wuhan, the pattern prior to January 26 clearly differs from the pattern after January 26. These two patterns are modeled with nonlinear (exponential) equa- tions separately in the next two graphs (Figure 2) in the research paper. The proportion of variation in number of deaths explained by the variation in number of cases for the curved model in Figure 2 prior to January 26 is (circle one) lower about the same higher than when the model was linear. This is an indication that the new (nonlinear) equation is a better "fit" than the straight line. Explain the relationships in the graphs before and after January 26; in practical terms, what was happening? g. For the data from mainland China except Hubei (that was clearly curved), a nonlinear (exponential) equation models the pattern and a graph of the fitted curve with the data is shown in Figure 3 of the paper. Explain this pattern.Epidemic data of Hubei province (except Wuhan) A Epidemic data of Wuhan B 350- -0.053455x+1.668 100 y=0.01413x+0.076223 300- R-0.98565, P-0.0001 80 R -0.9986, P