The answer in the space provided is the bullets as this is
the graphic elements that are used in each items that is organized in a list in
a way of making the list more clearer and more presentable with the given
indications or symbols.
Answer:
Compare the predictions in terms of the predictors that were used, the magnitude of the difference between the two predictions, and the advantages and disadvantages of the two methods.
Our predictions for the two models were very simmilar. A difference of $32.78 (less than 1% of the total price of the car) is statistically insignificant in this case. Our binned model returned a whole number while the full model returned a more “accurate” price, but ultimately it is a wash. Both models had comparable accuracy, but the full regression seemed to be better trained. If we wanted to use the binned model I would suggest creating smaller bin ranges to prevent underfitting the model. However, when considering the the overall accuracy range and the car sale market both models would be
Explanation:
Answer:
import random
decisions = int(input("How many decisions: "))
for i in range(decisions):
number = random.randint(0, 1)
if number == 0:
print("heads")
else:
print("tails")
Explanation:
*The code is in Python.
import the random to be able to generate random numbers
Ask the user to enter the number of decisions
Create a for loop that iterates number of decisions times. For each round; generate a number between 0 and 1 using the randint() method. Check the number. If it is equal to 0, print "heads". Otherwise, print "tails"
Answer:
A. A quiet room
Explanation:
Because if you open the windows the will be noise
a huge room will echoe
a room with air conditioning will make noise
