6. In a university town, the sales (in thousands of pounds) of coffee shops were thought to be related to the student population (in thousands) in their neighbourhoods. The figures for a random sample of 10 coffee shops are recorded, and are shown in the following table: Student population (in 000s) 2 6 8 8 12 16 20 20 22 26 Sales (in (000s) 58 105 88 118 117 137 157 169 149 202 Computer output from the regression of sales on student population is: Predictor Coefficient | Std error | t-ratio p-value Constant 60.00 9.2260 6.5033 0.0002 Student population 5.00 0.5803 8.6167 2.55 x 10-5 S = 13.8293 R-sq = 90.27% R = 0.9501 (a) Interpret R = 0.9501 from the above computer output. (3 marks) (b) Write down the equation of the estimated regression line. (2 marks) (c) What conclusion can you draw about the connection between the two variables? (5 marks) (d) Would it be advisable to use this regression model to predict coffee shop sales for a university town with 35,000 students? Explain your answer. (3 marks) (e) Suggest an alternative independent variable to explain coffee shop sales, briefly justifying your choice. (2 marks)