This is an open-ended lab. Using Python, run a linear regression analysis on data you have collected from public domain.

Recommended packages:

• scikit-learn
• numpy
• matplotlib
• pandas

Deliverables:

1. python code [.py file(s)]
2. Explanation of work

Create an original how-to document with step by step instructions you have followed to create your program. Your document should be used as an adequate tutorial for someone to reproduce your work by following the steps/instructions.

This is the excel CSV file for the data.

X,Y 32.50234527,31.70700585 53.42680403,68.77759598 61.53035803,62.5623823 47.47563963,71.54663223 59.81320787,87.23092513 55.14218841,78.21151827 52.21179669,79.64197305 39.29956669,59.17148932 48.10504169,75.3312423 52.55001444,71.30087989 45.41973014,55.16567715 54.35163488,82.47884676 44.1640495,62.00892325 58.16847072,75.39287043 56.72720806,81.43619216 48.95588857,60.72360244 44.68719623,82.89250373 60.29732685,97.37989686 45.61864377,48.84715332 38.81681754,56.87721319 66.18981661,83.87856466 65.41605175,118.5912173 47.48120861,57.25181946 41.57564262,51.39174408 51.84518691,75.38065167 59.37082201,74.76556403 57.31000344,95.45505292 63.61556125,95.22936602 46.73761941,79.05240617 50.55676015,83.43207142 52.22399609,63.35879032 35.56783005,41.4128853 42.43647694,76.61734128 58.16454011,96.76956643 57.50444762,74.08413012 45.44053073,66.58814441 61.89622268,77.76848242 33.09383174,50.71958891 36.43600951,62.12457082 37.67565486,60.81024665 44.55560838,52.68298337 43.31828263,58.56982472 50.07314563,82.90598149 43.87061265,61.4247098 62.99748075,115.2441528 32.66904376,45.57058882 40.16689901,54.0840548 53.57507753,87.99445276 33.86421497,52.72549438 64.70713867,93.57611869 38.11982403,80.16627545 44.50253806,65.10171157 40.59953838,65.56230126 41.72067636,65.28088692 51.08863468,73.43464155 55.0780959,71.13972786 41.37772653,79.10282968 62.49469743,86.52053844 49.20388754,84.74269781 41.10268519,59.35885025 41.18201611,61.68403752 50.18638949,69.84760416 52.37844622,86.09829121 50.13548549,59.10883927 33.64470601,69.89968164 39.55790122,44.86249071 56.13038882,85.49806778 57.36205213,95.53668685 60.26921439,70.25193442 35.67809389,52.72173496 31.588117,50.39267014 53.66093226,63.64239878 46.68222865,72.24725107 43.10782022,57.81251298 70.34607562,104.2571016 44.49285588,86.64202032 57.5045333,91.486778 36.93007661,55.23166089 55.80573336,79.55043668 38.95476907,44.84712424 56.9012147,80.20752314 56.86890066,83.14274979 34.3331247,55.72348926 59.04974121,77.63418251 57.78822399,99.05141484 54.28232871,79.12064627 51.0887199,69.58889785 50.28283635,69.51050331 44.21174175,73.68756432 38.00548801,61.36690454 32.94047994,67.17065577 53.69163957,85.66820315 68.76573427,114.8538712 46.2309665,90.12357207 68.31936082,97.91982104 50.03017434,81.53699078 49.23976534,72.11183247 50.03957594,85.23200734 48.14985889,66.22495789 25.12848465,53.45439421