The question has 2 parts that needs to be solved (Step 1 and Step 2) as mentioned in the attached image. Its reference files are also attached as image with the same name as mentioned in the question. Please mention what version of Python you are using, which IDE also. In case you need more details please let me know. Thank you! Please note I am asking this question again as the previous solution provided for this is not working for me, I am getting error and there is no way on this platform to reach out to the person who has given the python code. So, I would appreciate if you can mention all the details. Here the scatter log log needs to be done for x, y where they dont have same data size, I am getting error over there also.

First data set for the file mentioned in Step 1

Second dataset as mentioned in the Step 2

Step 1: Finding the Path Loss Exponent ( 20 points) The purpose of this step is to find out the path loss exponent of an unknown environment. Use any programming language/tools to solve your problem. Describe the outcomes in a report while submitting. - First open the spreadsheet named HW2_part1.csv; the spreadsheet consists of 13 RSSI (signal strength) values from columns B-N in dBm, with different distances in meters (in column A). So in the same location, the RSSI values are slightly different for different measurements. - Plot all these points in a graph where the RSSI values are in y-axis (dBm), and the distances are in x-axis (in log scale) - Draw a best fit straight line corresponding to this log-log plot. Find out the slope of this line, divide it by 10 and take the absolute value, which is your path loss exponent. - Also find out the variance of these RSSI samples, w.r.t. the best fit line. Step 2: Range Estimation (20 points) The purpose of this step is to find out the distance/range from the path loss exponent that you have found in the last step. Use any programming language/tools to solve your problem. Describe the outcomes in a report while submitting. - Now use the obtained path loss exponent for estimating some distances, using the following formula (I have ignored the noise term). Use HW2_part2.csv: column A is the distance in meters and columns B-P are the RSSI measurements at those distances. Assume d0 as 1 meter, and find Pr(d0) by averaging columns B1-P1. Assume that column A is unknown, which you want to estimate based on the measurements of columns B-P. - However, due to the noise there will be some errors in range/distance estimation. So, calculate the distance error by comparing with the actual distance. Repeat this experiment for 5 different distances (rows 2-6) that are given in the spreadsheet, and report the average error. Pr(d)[dBm]=Pt(d)[dBm][PL(d0)]dB10nlog10(d0d)=Pr(d0)[dBm]10nlog10(d0d)