Question

How would you generate a random sample of 1000 observation from a N(0,4) distribution and find the mean for only the positive values? numpy as np n=10eθ x=np, random, normal (θ,4,n) np, mean (x>θ) import numpy as np n=10θθ x=np,random. norma 1(θ,2,n) y=x[x>θ] np. mean (y) n=1θ0θ x=np,random.norma1(θ,2,n) y=x[x>θ] mean (y)

Answer 1

To generate a random sample of 1000 observations from a N(0,4) distribution and find the mean of only the positive values, use the numpy.random.normal() function in Python with the parameters (0, 2, 1000), filter for positive values, and compute the mean using np.mean().

Step-by-step explanation:

To generate a random sample of 1000 observations from a N(0,4) distribution and find the mean of only the positive values, you should use the Python NumPy library. The correct steps would be to first specify the number of observations, n, as 1000. Then, you would use numpy.random.normal() to generate your random sample with a mean (mu) of 0 and a standard deviation (sigma) of 2, since the variance is 4 (standard deviation is the square root of variance). After generating the sample, you'd filter out the positive values and compute their mean using numpy.mean(). Here's how you could do this:

import numpy as np
n = 1000
x = np.random.normal(0, 2, n)
y = x[x > 0]
positive_mean = np.mean(y)

Note that x > 0 is used to filter the array x for positive values, which are then stored in the array y. The mean of these positive values is then calculated with np.mean(y).