Plz use the given code to plot the fit plz MarkdownO nbdiff l123.771659128.10434637 140.17173392 146.29001843 152.50565637152.95840512]Value of slope :Value of intercept :Value of uncertainity: 2.83842835149537543.01222394582964144.89877050906963Q2B - Plotting 1Plot the data from the previous question along with a trendline. Make sure to include proper labels on the axes, but you do not require a title or caption for thisplot.In [183]: import matplotlib.pyplot as pltimport numpy as npplt.figure(figsize=(8,4))import matplotlib.pyplot as pltimport pandas as pdfrom scipy.optimize import curve_fitimport numpy as npsamplex = np.linspace(e, 5e, 26) + np.random.normal(scale = 0.2, size = 26)sampleY = 3 * samplex + 4 + np.random.normal (scale = 4, size = 26)print(sampleY)def obj(x,p,q) :return p*x + qans1, ans2 = curve_fit(obj, samplex, sampleY)P,q = ans1print('Value of slope : ',p)print('Value of intercept : ',q)print('Value of uncertainity : ',np.sqrt(np.mean(np.power(sampley-(p*samplex +q),2))))28.4167082127.08235946[ -0.7115631921.20507976 19.03069129O G 40)ENG(?-19°C Cleare to searchChp Use the provided code to generate a dataset. Then, using a linear fitting routine to find the slope, intercept, and their uncertainties.[9]: import matplotlib.pyplot as pltimport pandas as pdimport numpy as npsamplex = np.1linspace(0, 50, 26) + np.random.normal(scale = 0.2, size = 26)sampleY = 3 * samplex + 4 + np.random.normal(scale = 4, size = 26)print(sampleY)[ -3.746073310.8283116422.1152112727.0586507828.1941831636.2976190635.8827956145.12991823 49.0362421961.8160970662.0894118968.2806289572.8287440282.86838693 89.1699905891.2240362596.68730642 102.09560962 112.07930064 111.82978822124.77577466 122.28792314 139.47251684 144.67256319 155.22459033161.55767025]In [24]:import matplotlib.pyplot as pltimport pandas as pdimport numpy as npsamplex = np.linspace(0, 50, 26) + np.random.normal(scale = 0.2, size = 26)sampleY = 3 * samplex + 4 + np.random.normal(scale = 4, size = 26)print(sampleY)plt.figure(figsize=(10,10))#plt.plot(x,y,linestyle="-",linewidth=5,color="purple", Label="kinetic")(?-18°C Mostly cloudyhp

Here our task is to plot curve_fit. We use matplotlib library and import pyplot from it. For…

Q2B - Plotting 1 Plot the data from the previous question along with a trendline. Make sure to include proper labels on the axes, but you do not require a title or caption for this plot. In [183]: N import matplotlib.pyplot as plt import numpy as np plt.figure(figsize=(8,4)) import matplotlib.pyplot as plt import pandas as pd from scipy.optimize import curve_fit import numpy as np samplex = np.linspace(e, 5e, 26) + np.random.normal(scale = 0.2, size = 26) sampley = 3 * samplex + 4 + np.random.normal (scale = 4, size = 26) print(sampleY) def obj(x,p,q): return p*x + q ans2 = curve_fit(obj, samplex, sampleY) ans1 p,q = ans1 print('Value of slope : ',p) print('Value of intercept : ',9) print('Value of uncertainity : ',np.sqrt(np.mean(np.power(sampley-(p*samplex +q),2))))

Q2B - Plotting 1 Plot the data from the previous question along with a trendline. Make sure to include proper labels on the axes, but you do not require a title or caption for this plot. In [183]: N import matplotlib.pyplot as plt import numpy as np plt.figure(figsize=(8,4)) import matplotlib.pyplot as plt import pandas as pd from scipy.optimize import curve_fit import numpy as np samplex = np.linspace(e, 5e, 26) + np.random.normal(scale = 0.2, size = 26) sampley = 3 * samplex + 4 + np.random.normal (scale = 4, size = 26) print(sampleY) def obj(x,p,q): return px + q ans2 = curve_fit(obj, samplex, sampleY) ans1 p,q = ans1 print('Value of slope : ',p) print('Value of intercept : ',9) print('Value of uncertainity : ',np.sqrt(np.mean(np.power(sampley-(psamplex +q),2))))

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question

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Plz use the given code to plot the fit plz

Markdown
O nbdiff l
123.771659
128.10434637 140.17173392 146.29001843 152.50565637
152.95840512]
Value of slope :
Value of intercept :
Value of uncertainity: 2.8384283514953754
3.0122239458296414
4.89877050906963
Q2B - Plotting 1
Plot the data from the previous question along with a trendline. Make sure to include proper labels on the axes, but you do not require a title or caption for this
plot.
In [183]: import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(8,4))
import matplotlib.pyplot as plt
import pandas as pd
from scipy.optimize import curve_fit
import numpy as np
samplex = np.linspace(e, 5e, 26) + np.random.normal(scale = 0.2, size = 26)
sampleY = 3 * samplex + 4 + np.random.normal (scale = 4, size = 26)
print(sampleY)
def obj(x,p,q) :
return p*x + q
ans1, ans2 = curve_fit(obj, samplex, sampleY)
P,q = ans1
print('Value of slope : ',p)
print('Value of intercept : ',q)
print('Value of uncertainity : ',np.sqrt(np.mean(np.power(sampley-(p*samplex +q),2))))
28.41670821
27.08235946
[ -0.71156319
21.20507976 19.03069129
O G 40)
ENG
(?
-19°C Clear
e to search
Chp

Use the provided code to generate a dataset. Then, using a linear fitting routine to find the slope, intercept, and their uncertainties.
[9]: import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
samplex = np.1linspace(0, 50, 26) + np.random.normal(scale = 0.2, size = 26)
sampleY = 3 * samplex + 4 + np.random.normal(scale = 4, size = 26)
print(sampleY)
[ -3.7460733
10.82831164
22.11521127
27.05865078
28.19418316
36.29761906
35.88279561
45.12991823 49.03624219
61.81609706
62.08941189
68.28062895
72.82874402
82.86838693 89.16999058
91.22403625
96.68730642 102.09560962 112.07930064 111.82978822
124.77577466 122.28792314 139.47251684 144.67256319 155.22459033
161.55767025]
In [24]:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
samplex = np.linspace(0, 50, 26) + np.random.normal(scale = 0.2, size = 26)
sampleY = 3 * samplex + 4 + np.random.normal(scale = 4, size = 26)
print(sampleY)
plt.figure(figsize=(10,10))
#plt.plot(x,y,linestyle="-",linewidth=5,color="purple", Label="kinetic")
(?
-18°C Mostly cloudy
hp

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