The data in running.csv contains data from a 1963 study to assess energy expenditure while running. Researchers asked athletes to run on a treadmill at various speeds and inclines and assessed energy expenditure (computed indirectly via oxygen consumption and individual body measurements). Speed is measured in kilometers per hour and treadmill incline as either downhill, at, or uphill. Energy is measured in units of Cal / kg hour. The goal for this analysis is to create a model that can predict energy expenditure from running speed and treadmill incline; this kind of model would be useful for programming the software on a computerized treadmill that displays how many calories have been burned during an exercise session. Read the data into R using mydata=read . csv(\"http: //www . datadescant . com/stat104/ running. csv") a) Explore the data using numerical and graphical summaries. Describe the distributions of Speed, Energy, and Incline. b) Use graphical summaries to explore the relationships between the variables. Describe what you see. C) R can neatly create dummy variables for you automatically from a categorical variable. Fit and interpret the following model in R. Which category is dened as the baseline category? fit=1m(energy~speed+incline , data=mydata) d) One can include interaction variables by adding the term speed*incline to the model as follows fit=1m(energy~speed*incline , data=mydata) This model is actually tting three models at the same time; write out what the three models are. Is there evidence that the interaction terms are needed