Below are the input and results of an OLS linear regression. The president of a specialty fruit juice company believed that for every single degree change in average temperature during the summer months, the profit would decline 3%. This information was used to prepare budgets. The president obtained forecasts of the average summer temperature then requested the budget be adjusted. The table of values presents the raw data for temperatures (Predictor Variable X) and net income (Outcome Variable Y).

Required
1. What do the coefficients of the OLS linear regression line: y = a + b X + e (where a = $943.843 and b = $47.126) tell you about the relationship between these two sets of data?
2. What does the R Square (r2 ) tell you about the explanatory power of temperature to predict net income?
3. What does the t -Stat and the P-value tell you about whether or not the coefficients a and b could arise by accident?
4. What recommendations would you make to the president?