Answer:
# Program is written in Python Programming Language
# Comments are used for explanatory purpose
# Program starts here
# Accept input
Steps = input (Number of Steps: ")
# Calculate distance
distance = float(2000) * float(steps)
#Print Formatted Result
print('%0.2f' % distance)
# End of Program
.--------
The above program converts number of steps to miles.
At line 5, the number of steps is inputted and stored in variable named Steps.
At line 6, the number of miles is calculated by multiplying 2000 by the content of variable Steps
The result is printed at line 8
Answer:
Answer for the question:
"Show that for a linearly separable dataset, the maximum likelihood solution for the logisitic regression model is obtained by finding a weight vector w whose decision boundary wx.
"
is explained in the attachment.
Explanation:
Answer:
80.7lbft/hr
Explanation:
Flow rate of water in the system = 3.6x10^-6
The height h = 100
1s = 1/3600h
This implies that
Q = 3.6x10^-6/[1/3600]
Q = 0.0000036/0.000278
Q = 0.01295
Then the power is given as
P = rQh
The specific weight of water = 62.3 lb/ft³
P = 62.3 x 0.01295 x 100
P = 80.675lbft/h
When approximated
P = 80.7 lbft/h
This is the average power that could be generated in a year.
This answers the question and also corresponds with the answer in the question.
Answer:
blah blah blah sh ut up read learn
Answer:
a) Under damped
Explanation:
Given that system is critically damped .And we have to find out the condition when gain is increased.
As we know that damping ratio given as follows
Where C is the damping coefficient and Cc is the critical damping coefficient.
So from above we can say that
From above relationship we can say when gain (K) is increases then system will become under damped system.
