Design PYTHON code to fit the linearly interpolated integrated function. The following graph is ArrivalTImes.xls.

18. Each summer a car dealer runs a 40-h sale. Customer arrival times (in hours) from two of these sales can be found on the book website in ArrivalTimes.xls. Use these data to form a linearly interpolated inte grated rate function (t), then generate customer arrival times for 1 day from a nonstationary Poisson arrival process using A(t). Each column is an observation of arrival times (in hours) over a 40 hour period [0, 40] 0. 08 0. 1 0. 15 0. 34 0. 54 0. 94 1.27 1. 31 1. 93 2. 31 2. 34 3. 1 3. 61 3. 67 4. 07 4. 65 7. 76 8. 31 9. 37 9. 51 10. 5 11. 12 14. 39 0. 15 0. 62 1. 22 1. 39 1. 61 1.97 3. 2 3. 38 3. 91 4. 02 4. 2 4. 25 4. 31 4. 33 5. 25 5. 47 5. 79 5. 93 6. 53 6. 79 7. 86 9. 06 14.7310. 35 25. 1910. 36 30. 12 11.59 31.76 17. 19 32. 02 25. 78 33. 2727.84 33. 5827.88 36. 6231.01 36. 88 33. 65 36. 91 34. 88 37.1135. 02 37. 15 35. 16 37. 92 35. 17 35. 5 36 39. 55 36. 83 37. 5 39. 98 37. 76 38. 05 38. 49 38. 55 39. 21 39. 77 18. Each summer a car dealer runs a 40-h sale. Customer arrival times (in hours) from two of these sales can be found on the book website in ArrivalTimes.xls. Use these data to form a linearly interpolated inte grated rate function (t), then generate customer arrival times for 1 day from a nonstationary Poisson arrival process using A(t). Each column is an observation of arrival times (in hours) over a 40 hour period [0, 40] 0. 08 0. 1 0. 15 0. 34 0. 54 0. 94 1.27 1. 31 1. 93 2. 31 2. 34 3. 1 3. 61 3. 67 4. 07 4. 65 7. 76 8. 31 9. 37 9. 51 10. 5 11. 12 14. 39 0. 15 0. 62 1. 22 1. 39 1. 61 1.97 3. 2 3. 38 3. 91 4. 02 4. 2 4. 25 4. 31 4. 33 5. 25 5. 47 5. 79 5. 93 6. 53 6. 79 7. 86 9. 06 14.7310. 35 25. 1910. 36 30. 12 11.59 31.76 17. 19 32. 02 25. 78 33. 2727.84 33. 5827.88 36. 6231.01 36. 88 33. 65 36. 91 34. 88 37.1135. 02 37. 15 35. 16 37. 92 35. 17 35. 5 36 39. 55 36. 83 37. 5 39. 98 37. 76 38. 05 38. 49 38. 55 39. 21 39. 77