cell_treatment.csv ,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG...

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need python code for attached assignment


cell_treatment.csv 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cell_1,192.0,285.99,2501.0,299.0,41.701,687.448,311.0,411.99,721.0010000000003,105.999,121.169,87.001,392.0,668.9989999999998,114.0,1456.0,1297.99,104.95,139.999,392.02099999999996,360.0,8056.0,1056.0,153.0,52.0,86.0,212.0,53.0,630.0,31.0,232.0,0.0,1284.67,685.0010000000002,1301.0,33.043,107.0,94.15100000000001,429.0,421.0,3054.0,52.998999999999995,9.0,225.99900000000002,24.0,0.0,56.0,55.0,426.0,12.0,155.957,247.935,59.0,361.89599999999996,7.0,22.0,148.001,4630.0,55.998999999999995,121.0,225.0,51.0,223.0,230.426,31.0,0.0,16.0,684.0010000000002,134.0,504.0,121.369,104.573,748.9989999999997,217.99900000000002,18.0,129.0,577.999,199.0,14.0,150.0,16.0,342.999,125.095,172.0,0.0,103.0,198.195,10.0,57.0,137.0,1141.0,22.996,286.001,141.0,341.001,227.197,21.0,45.0,157.0,0.0,7.0,64.0,59.0,86.0,17.0,0.0,14.0,0.0,353.0,32.0,75.0,113.0,19.0,200.0,1769.24,0.0,21.0,358.0,292.659,0.0,25.999000000000002,121.0,86.391,3.0,89.00299999999999,173.00099999999995,121.08200000000001,115.0,218.0,7.0,15.0,83.949,176.0,214.0,39.0,289.907,289.574,273.0,197.0,41.0,90.0,0.0,8.0,35.0,374.0,123.95299999999999,723.0,106.03399999999999,438.0,81.0,158.0,86.0,180.00099999999995,1032.98,145.657,19.0,877.0,36.0,321.999,5.001,273.0,162.001,93.0,106.0,462.999,116.001,11.0,191.0,12.0,48.00100000000001,107.083,329.001,1.0,317.999,59.0,52.0,750.0,189.85,191.0,355.009,506.0,85.0,35.0,524.001,43.14,91.0,99.0,0.0,119.515,510.0,0.0,34.0,244.0,28.0,2.861,40.0,185.0,126.001,28.0,157.0,203.0,37.0,77.999,506.0,0.0,216.0,138.0,66.0,0.0,236.99900000000002,181.076,41.0,85.0,28.0,241.0,53.0,0.0,13.0,17.266,262.0,108.476,381.0,333.0,85.001,57.0,31.000999999999998,9.0,56.0,710.9989999999997,182.0,14.0,151.0,77.917,0.0,37.999,195.99900000000002,472.923,275.0,1160.0,65.555,14.0,74.0,3.0,18.0,734.637,176.0,262.0,74.0,32.0,388.0,31.0,37.0,85.0,248.0,4.709,0.0,157.0,205.399,198.0,168.43,73.0,0.0,591.942,502.0,18.0,178.0,86.0,77.999,61.00100000000001,40.001,11.0,74.0,122.0,186.0,84.0,141.816,99.0,227.0,49.0,49.0,44.0,2.0,59.0,84.0,542.9540000000002,1495.0,332.0,58.998999999999995,159.0,55.0,38.0,97.0,63.0,46.0,44.0,302.001,126.999,166.0,87.001,21.0,19.0,105.0,35.0,85.22399999999998,57.0,82.0,19.559,0.0,0.0,0.0,0.0,1.368,0.9940000000000001,223.078,46.0,0.0,20.477999999999998,3.0,10.0,867.0,39.0,0.0,1.297,0.0,74.242,71.0,13.0,0.0,71.0,0.0,41.0,89.95700000000002,70.0,93.0,296.0,26.066,1.0,28.0,67.007,2.0,1.0,0.0,14.0,35.0,109.44,11.0,46.0,24.0,25.0,39.632,78.0,0.0,53.0,19.0,49.0,0.0,0.0,302.0,0.0,0.0,28.0,6.0,0.0,8.0,41.0,39.0,10.0,0.0,0.0,0.0,47.111000000000004,27.0,69.0,127.0,0.0,161.0,111.999,201.99900000000002,17.0,193.00099999999998,63.0,142.0,83.0,6.0,0.0,0.0,0.0,67.0,0.0,55.00100000000001,0.0,0.0,3.012,40.775,45.0,180.99900000000002,20.0,11.0,0.0,12.0,0.0,0.0,0.0,0.0,1.0,28.0,0.0,0.0,0.0,0.0,0.0,0.0,2.046,0.0,0.0,17.0,86.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,11.0,0.0,0.0,20.0,0.0,0.0,0.0,0.0,0.0,0.0,247.0,2.0,0.0,9.0,0.0,0.0,0.0,0.0,169.0,0.0,0.0,0.0,0.0,0.0,165.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15.0,0.0,0.0,0.0,0.0,61.0,0.0,0.0,0.0,0.0,0.0,119.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,21.006,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,41.0,0.0,0
Answered 2 days AfterJul 02, 2021

Answer To: cell_treatment.csv ,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG XXXXXXXXXX,ENSG...

Pranav answered on Jul 04 2021
147 Votes
Question 6.2 :
Key points to remember before approaching the question :
1. The diagonal elements of a covariance matrix denote the variance of that attribute. The square root of this variance gives us the standard deviation.
2. The non
– diagonal elements of a covariance matrix give us the covariance between two attributes.
3. Covariance and covariance- matrix only tell us if there is a positive relationship between two attributes or negative. It does not tell us about the strength of the correlation between those attributes. Source : https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/how-to/covariance/interpret-the-results/ (para. 2 ) , https://www.youtube.com/watch?v=xGbpuFNR1ME ( Look at timestamp 09 : 46 )
4. Two attributes are uncorrelated only if their covariance is zero (or very close to zero) . Note : In the Jupyter notebook, just before Question 7.1, there is a paragraph starting with “Carefully inspect ….. “ where we can see at pt. no. 2 that attributes having a covariance very very close to zero, e.g. in the order of 10-17 / 10-15 are considered uncorrelated.
Now let us look at the question at hand :
Covariance matrix :
[[ 1.00210577 0.95044461 -0.03119992]
[ 0.95044461 0.9976996 -0.02444553]
[-0.03119992 -0.02444553 7.84401534]]
a) The first and second features are strongly correlated. False
From 3. , we can see that such a conclusion cannot be made. We cannot tell if two attributes are strongly correlated using a covariance matrix.
b) The second and third features are strongly correlated. False
Same as a)
c) The first and third features are not correlated. False
Feature 1 and Feature 3 show a covariance of -0.0311. Now, even though this value looks small, in the context of covariance matrix, we cannot conclude these attributes to be uncorrelated because they have just the order of 10-2. Look at pt. 4. ‘s note above.
Also, it can’t be said to be near zero because covariance is sensitive to the range of data used, and in our data, 0.0311 comes within its range and hence is not very close to zero.
d) The standard deviation of the third feature is close to 8. False
No. Rather, the variance of the third feature is close to 8. The standard deviation will be root of 7.844 = 2.80 which is not close to 8.
e) The standard deviation of the first feature is close to 1. ...
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