Answer:
Please see explanation for the answer. The code is written in python and is as given below:
Step-by-step explanation:
The solution is obtained on the Python with the following code
import matplotlib.pyplot as plotter
import numpy as npy
x_s = npy.linspace(-5,5,100) #Defining a linear sample space with boundaries as -5 to 5 and 100 as number of samples.
def sigmo(z):return 1/(1 + npy.exp(-z)) #Defining sigmoid function for the f(x).
plotter.plot(x_s, sigmo(x_s))
plotter.plot([-5,5],[.5,.5])
plotter.xlabel("z")
plotter.ylabel("sigmoid(z)")
plotter.show()
Answer: c) About 16% of the variation in value of the car is explained by a linear relationship with the age of the car.
e) The correlation coefficient, r, is 0.397.
Step-by-step explanation:
Given that:
Coefficient of determination (r²) between two variables, age of car (x) and value of car (y) = 0.158
Correlation of determination (r²) of 0.158 = (0.158 × 100% = 15.8% of the variation between the two variables can be explained by the regression line). Hence, about 16% of the variation between age and value of car can be explained by the linear relationship.
Coefficient of correlation (r) = sqrt(r²) = sqrt(0.158) = 0.397
Answer:
729.
Step-by-step explanation:
Answer:
(5 x (-20)) * (100 x (0.7))
A for #1
C for #2 ! Hope this helped you out!
