Confidence intervals on parameters in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

SciPy

import numpy as np

data = np.array([5, 7, 8, 9, 10])
mean_value = np.[1](data)
print(mean_value)

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

Amedian

Bmean

Csum

Dstd

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.median instead of np.mean

Using np.sum which adds all values instead of averaging

The np.mean() function calculates the average (mean) of the array elements.

2fill in blank

medium

Complete the code to calculate the standard error of the mean (SEM) for the data.

SciPy

import numpy as np

data = np.array([5, 7, 8, 9, 10])
sem = np.std(data, ddof=1) / np.sqrt([1])
print(sem)

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Alen(data) - 1

Bnp.sum(data)

Cnp.mean(data)

Dlen(data)

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(data) - 1 instead of len(data)

Using np.mean(data) or np.sum(data) which are unrelated

The SEM is the standard deviation divided by the square root of the sample size, which is len(data).

3fill in blank

hard

Fix the error in the code to calculate a 95% confidence interval for the mean using scipy.stats.

SciPy

from scipy import stats
import numpy as np

data = np.array([5, 7, 8, 9, 10])
confidence = 0.95
mean = np.mean(data)
sem = stats.sem(data, ddof=1)
margin = sem * stats.t.ppf((1 + confidence) / 2, df=[1])
ci_lower = mean - margin
ci_upper = mean + margin
print(ci_lower, ci_upper)

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Alen(data)

Blen(data) + 1

Clen(data) - 1

Dlen(data) - 2

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(data) instead of len(data) - 1 for degrees of freedom

Using len(data) + 1 or len(data) - 2 which are incorrect

The degrees of freedom (df) for the t-distribution is the sample size minus 1, so len(data) - 1.

4fill in blank

hard

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

SciPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
lengths = {word: [1] for word in words if [2]
print(lengths)

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length

Checking if the word string is greater than 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 dictionary comprehension that maps uppercase words to their lengths only if the length is greater than 4.

SciPy

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
lengths = { [1]: [2] for word in words if [3] }
print(lengths)

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Aword.upper()

Blen(word)

Clen(word) > 4

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of word.upper() for keys

Using word instead of len(word) for values

Filtering with length greater than 3 instead of 4

The dictionary comprehension uses word.upper() as keys, maps to len(word), and filters words with length greater than 4 using len(word) > 4.