In R generate 1000 data points normally distributed, with mean=x, and variance=y. Perform summary stat on the data points, explain stat metrics. Create density plot. Do the same in python script, in codility.com editor

Question

Anonymous · Accepted Answer

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Parameters
x = 5       # mean
y = 4       # variance
sd = np.sqrt(y)

# Generate 1000 normally distributed data points
data = np.random.normal(loc=x, scale=sd, size=1000)

# Summary statistics
mean = np.mean(data)
median = np.median(data)
minimum = np.min(data)
maximum = np.max(data)
q1 = np.percentile(data, 25)
q3 = np.percentile(data, 75)
variance = np.var(data, ddof=1)  # sample variance
std_dev = np.std(data, ddof=1)

# Print summary
print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Min: {minimum}")
print(f"1st Quartile (Q1): {q1}")
print(f"3rd Quartile (Q3): {q3}")
print(f"Max: {maximum}")
print(f"Variance: {variance}")
print(f"Standard Deviation: {std_dev}")

# Explanation:
# Mean: average value
# Median: middle value
# Min/Max: smallest/largest
# Q1/Q3: 25th and 75th percentiles

In R generate 1000 data points normally distributed, with mean=x, and variance=y. Perform summary stat on the data points, explain stat metrics. Create density plot. Do the same in python script, in codility.com editor

Practice this question with AI

Go Premium

Practice More Questions

Community Answers

Unlock Community Insights

Master Data Science Interviews