Answer:
Explanation:
A lower triangular matrix is one whose elements above the main diagonal are zero meanwhile all the main diagonals elements and below are nonzero elements. This is one of the two existing types of triangular matrixes. Attached you will find a image referring more about triangular matrixes.
If there is any question, just let me know.
Answer:
Correct option: B. 90%
Explanation:
The confidence interval is given by:
If is 190, we can find the value of
:
Now we need to find the value of :
So the value of z is 1.71.
Looking at the z-table, the z value that gives a z-score of 1.71 is 0.0436
This value will occur in both sides of the normal curve, so the confidence level is:
The nearest CI in the options is 90%, so the correct option is B.
Answer:
Suction and exhaust processes do not affect the performance of Otto cycle.
Explanation:
Step1
Inlet and exhaust flow processes are not including in the Otto cycle because the effect and nature of both the process are same in opposite direction.
Step2
Inlet process or the suction process is the process of suction of working fluid inside the cylinder. The suction process is the constant pressure process. The exhaust process is the process of exhaust out at constant pressure.
Step3
The suction and exhaust process have same work and heat in opposite direction. So, net effect of suction and exhaust processes cancels out. The suction and exhaust processes are shown below in P-V diagram of Otto cycle:
Process 0-1 is suction process and process 1-0 is exhaust process.
Answer:
Explanation:
The python code to generate this is quite simple to run.
i hope you understand everything written here, you can as well try out other problems to understand better.
First to begin, we import the package;
Code:
import pandas as pd
import matplotlib.pyplot as plt
name = input('Enter name of the file: ')
op = input('Enter name of output file: ')
df = pd.read_csv(name)
df['Date'] = pd.to_datetime(df["Date"].apply(str))
plt.plot(df['Date'],df['Absent']/(df['Present']+df['Absent']+df['Released']),label="% Absent")
plt.legend(loc="upper right")
plt.xticks(rotation=20)
plt.savefig(op)
plt.show()
This should generate the data(plot) as seen in the uploaded screenshot.
thanks i hope this helps!!!
