[Solved] What will be the output of this code snippet?import numpy as np from scipy import stats sample = np.array([5, 7, 8, 9, 10]) mean = np.mean(sample) sem = stats.sem(sample) print(round(sem, 3)) | SciPy

SciPy - Statistical Functions (scipy.stats) Basics

What will be the output of this code snippet?

import numpy as np
from scipy import stats

sample = np.array([5, 7, 8, 9, 10])
mean = np.mean(sample)
sem = stats.sem(sample)
print(round(sem, 3))

A0.748

B2.236

C1.673

D0.707

Step-by-Step Solution

Solution:

Step 1: Calculate sample mean and standard error of mean (SEM)
SEM is standard deviation divided by sqrt of sample size. Using scipy.stats.sem calculates this.
Step 2: Compute SEM for given sample
Sample: [5,7,8,9,10], std dev ≈ 1.58, n=5, SEM = 1.58 / sqrt(5) ≈ 0.707.
Final Answer:
0.707 -> Option D
Quick Check:
SEM calculation = 0.707 [OK]

Quick Trick: SEM = std deviation divided by sqrt(sample size) [OK]

Common Mistakes:

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What will be the output of this code snippet?