Linear regression model for data documenting public

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Reference no: EM131298213

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.

Reference no: EM131298213

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