Why statistics validates hypotheses in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the sample data.

Data Analysis Python

import numpy as np
sample = [5, 7, 8, 6, 9]
mean_value = np.[1](sample)
print(mean_value)

Drag options to blanks, or click blank then click option'

Amean

Bmedian

Cmode

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using median instead of mean

Using sum without dividing by count

The mean function calculates the average value of the sample data, which is essential for hypothesis testing.

2fill in blank

medium

Complete the code to calculate the standard deviation of the sample.

Data Analysis Python

import numpy as np
sample = [5, 7, 8, 6, 9]
std_dev = np.[1](sample, ddof=1)
print(std_dev)

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Avar

Bstd

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using variance instead of standard deviation

Forgetting to set ddof=1 for sample std deviation

The std function calculates the standard deviation, which measures how spread out the data is. The parameter ddof=1 gives the sample standard deviation.

3fill in blank

hard

Fix the error in the code to calculate the t-statistic for hypothesis testing.

Data Analysis Python

import numpy as np
sample = [5, 7, 8, 6, 9]
mean_sample = np.mean(sample)
std_sample = np.std(sample, ddof=1)
n = len(sample)
t_stat = (mean_sample - 6) / (std_sample / [1])
print(t_stat)

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Anp.sqrt(n)

Bn

Cnp.square(n)

D1/n

Attempts:

3 left

💡 Hint

Common Mistakes

Using n instead of sqrt(n)

Using 1/n which is incorrect for standard error

The denominator of the t-statistic uses the standard error, which is the standard deviation divided by the square root of the sample size.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Data Analysis Python

words = ['data', 'is', 'fun', 'and', 'useful']
lengths = {word: [1] for word in words if [2] > 3}
print(lengths)

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Alen(word)

Bword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for length

Checking word > 3 instead of length

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of uppercase keys and values greater than 0.

Data Analysis Python

data = {'a': 1, 'b': -2, 'c': 3}
result = { [1]: [2] for k, v in data.items() if v [3] 0 }
print(result)

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper()

Using < instead of > in condition

The dictionary comprehension creates keys as uppercase letters using k.upper(), keeps values as v, and filters only values greater than zero with v > 0.