Answer:
Import java.awt.*;
import java.util.*;
public class Sprhere
{// Instance Data private double surfaceA, volume, r, diameter; //Constructors public Sphere ()
{r = 0;diameter = 0; surfaceA = 0;volume = 0;}
public Sphere (double radius, double d, double SA, double v)
{this. r = radius; this. diameter = d; this. surfaceA = SA;this.volume = v;}
//--------------------------------------------------------------------// Accesors.//--------------------------------------------------------------------public double get Radius()
{return r;}
public double get Diameter()
{return diameter;}
public double get SurfaceA()
{return surfaceA;}
public double get Volume()
{return volume;}
//--------------------------------------------------------------------// Mutators.//--------------------------------------------------------------------
A. self-efficacy.
B. overreward inequity.
C. expectancy.
D. cognitive distortion.
Answer:B. overreward inequity.
Explanation: Overreward inequity is a term mostly associated with the feeling of guilt by an employee who believes that his contributions to work or his performance rating is less than what he or she is been paid for.
An employee with this kind of guilt feelings will mostly like do his or her best to engage in in performance improvement Activities which includes enrollment for training and classes to enhance his or her performance.
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.
