Use the provided code to generate a dataset. Then, using a linear fitting routine to find the slope, intercept, and their uncertainties. 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) [ -3.7460733 10.82831164 22.11521127 27.05865078 28.19418316 61.81609706 36.29761906 35.88279561 45.12991823 49.03624219 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]
Use the provided code to generate a dataset. Then, using a linear fitting routine to find the slope, intercept, and their uncertainties. 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) [ -3.7460733 10.82831164 22.11521127 27.05865078 28.19418316 61.81609706 36.29761906 35.88279561 45.12991823 49.03624219 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]
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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![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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F44e7a3ce-803b-45aa-8658-f230b13e4980%2F9faba97f-cffa-4886-9a36-f36b83d2241e%2F8f6gnq_processed.jpeg&w=3840&q=75)
Transcribed Image Text: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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