Why statistics quantifies uncertainty in SciPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the data using scipy.

SciPy

from scipy import stats

data = [2, 4, 6, 8, 10]
mean_value = stats.[1](data)
print(mean_value)

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Atmean

Bmode

Cmedian

Dvariance

Attempts:

3 left

💡 Hint

Common Mistakes

Using mode or median instead of mean.

Trying to use variance which measures spread, not central value.

The tmean function from scipy.stats calculates the mean (average) of the data, which helps summarize the central value.

2fill in blank

medium

Complete the code to calculate the standard deviation of the data using scipy.

SciPy

from scipy import stats

data = [1, 3, 5, 7, 9]
std_dev = stats.[1](data)
print(std_dev)

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Amode

Bsem

Cvariation

Dtstd

Attempts:

3 left

💡 Hint

Common Mistakes

Using sem which measures error, not spread.

Using mode which finds the most frequent value.

The tstd function calculates the standard deviation, which measures how spread out the data is around the mean.

3fill in blank

hard

Fix the error in the code to calculate the 95% confidence interval for the mean.

SciPy

from scipy import stats
import numpy as np

data = [10, 12, 14, 16, 18]
mean = np.mean(data)
sem = stats.sem(data)
confidence = 0.95
h = sem * stats.[1].ppf((1 + confidence)/2, len(data)-1)
print(f"Confidence interval: ({mean - h}, {mean + h})")

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Achi2

Bnorm

Ct

Df

Attempts:

3 left

💡 Hint

Common Mistakes

Using norm which is for large samples or known variance.

Using chi2 or f which are for variance tests.

The t distribution is used for confidence intervals when the sample size is small and population standard deviation is unknown.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length if the length is greater than 3.

SciPy

words = ['data', 'is', 'fun', 'and', 'useful']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword > 3

Dword == 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length.

Comparing the word string to a number.

The dictionary comprehension maps each word to its length using len(word) only if the length is greater than 3, checked by len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths if length is greater than 2.

SciPy

words = ['hi', 'data', 'science', 'is', 'cool']
result = { [1]: [2] for word in words if [3] }

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Aword.upper()

Blen(word)

Clen(word) > 2

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Not checking the length condition correctly.

The dictionary comprehension maps each word in uppercase (word.upper()) to its length (len(word)) only if the length is greater than 2 (len(word) > 2).