Using the average daily rates for the months of Janurary through May 2016, a student created a simple linear regression model for data documenting public transportation usage. The single causal factor considered was a linear trend. The resulting regression coefficient estimates were Â0=1552.3 and Â1=52.67 where Â0 is the estimate for the base level (or intercept) and Â1 is the estimate for the trend (or slope). Use this model to predict average daily arrival rates for June through December 2016.