Answer:
:D
Explanation:
Literally speaking, the sign of the beaver is a scratched pattern the Beaver clan uses to mark their territory. Symbolically, though, it lets other clans know not to hunt on the land it marks.
Authors help readers understand their characters in two different ways: direct characterization and indirect characterization. When an author uses words and phrases that describe a character, it's called direct characterization.
Some books use this technique more than others. Although direct characterization can be interesting if done well, most authors prefer to present their characters using indirect characterization—that is, by showing the reader what characters are like by how they act, what they think, and what other people say about them.
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:We start each project to get some business benefits. We design it to achieve users and other stakeholder’s satisfaction. And we build it to improve organization KPIs. But, we live in a world where the project faces many uncertainties. These uncertainties or risks can prevent from achieving our project goals or objectives. So, it is critical that we identify them in time to take care of their effective responses.
The more we know our risks, the more we can evaluate and prioritize them timely for:
Reducing their probable negative impacts, or
Increase their likely positive impacts
We can use Qualitative Risk Analysis and Quantitative Risk Analysis techniques to evaluate and prioritize risks. I see there are a lot of confusions around how these two techniques are different from each other. In this blog, I will address these confusions and differences between these two techniques.
Before we get into the difference between qualitative and quantitative risk analysis/assessment, it is mandatory to understand how we perform risk analysis in projects. Below is the summarized demonstration of the risk analysis:
Explanation:
The synonym for term port is seaport
Maybe like 3-5 I think im right
