Answer: option d is correct
Explanation:
It improves quality and efficiency of computer systems. They can have part to whole relations, extrapolations, or predictions.
The examination phase
Further Explanation:
Hardware troubleshooting in computers requires a systematic and logical approach. Taking a logical approach helps you identify the root cause much easily. Ask yourself those questions first before getting to the bottom of anything. You will find it helpful to reproduce the problem and develop a hypothesis of the problem if you ask yourself those 20 questions.
Next comes the examination phase. Having gathered everything, I will now attempt a fix based on what I think I found. In my case, I suspect that there a component in my computer that is fried. A few ways to tell if my motherboard or components attached to the motherboard are fried is to remove the side panel first and examine the circuitry before removing any unnecessary hardware devices. Obvious sings will be smell of smoke. Examine the capacitors as well. Burnt capacitors have rounded tops. This is a clear indication that they are blown.
I will now remove every single component one by one from the motherboard and test my hardware on a low-level.
Learn more about computer hardware troubleshooting
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Answer:
import numpy as np
import matplotlib.pyplot as plt
def calculate_pi(x,y):
points_in_circle=0
for i in range(len(x)):
if np.sqrt(x[i]**2+y[i]**2)<=1:
points_in_circle+=1
pi_value=4*points_in_circle/len(x)
return pi_value
length=np.power(10,6)
x=np.random.rand(length)
y=np.random.rand(length)
pi=np.zeros(7)
sample_size=np.zeros(7)
for i in range(len(pi)):
xs=x[:np.power(10,i)]
ys=y[:np.power(10,i)]
sample_size[i]=len(xs)
pi_value=calculate_pi(xs,ys)
pi[i]=pi_value
print("The value of pi at different sample size is")
print(pi)
plt.plot(sample_size,np.abs(pi-np.pi))
plt.xscale('log')
plt.yscale('log')
plt.xlabel('sample size')
plt.ylabel('absolute error')
plt.title('Error Vs Sample Size')
plt.show()
Explanation:
The python program gets the sample size of circles and the areas and returns a plot of one against the other as a line plot. The numpy package is used to mathematically create the circle samples as a series of random numbers while matplotlib's pyplot is used to plot for the visual statistics of the features of the samples.
Answer:
Awww what happened? Sometimes its hard for me to keep a promise.
Explanation:
