Chi-squared test in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the chi2_contingency function from scipy.stats.

SciPy

from scipy.stats import [1]

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

Achi2_contingency

Bchi_square

Ccontingency_test

Dchi2_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent function like chi_square or chi2_test.

Trying to import from the wrong module.

The chi2_contingency function is the correct function to perform the chi-squared test on a contingency table.

2fill in blank

medium

Complete the code to create a 2x2 contingency table using a list of lists.

SciPy

table = [[20, 15], [[1], 30]]

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

A10

B35

C40

D25

Attempts:

3 left

💡 Hint

Common Mistakes

Using a number that makes the table invalid or unbalanced.

Using a non-integer value.

The missing value in the table is 25 to complete the 2x2 contingency table.

3fill in blank

hard

Fix the error in the code to perform the chi-squared test on the table.

SciPy

chi2, p, dof, expected = [1](table)

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

Achi_square

Bchi2_contingency

Ccontingency_chi2

Dchi_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using a function name that does not exist in scipy.stats.

Misspelling the function name.

The correct function to call is chi2_contingency to perform the chi-squared test.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each category to its expected count if the expected count is greater than 20.

SciPy

result = {category: expected.flatten()[[1]] for [2], category in enumerate(['A', 'B', 'C', 'D']) if expected.flatten()[[1]] > 20}

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Ai

Bcategory

Cj

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using the category name as an index.

Using different variables for the two blanks.

We use i as the index from enumerate to access the expected counts by position.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each category name in uppercase to its expected count if the count is less than 25.

SciPy

filtered = {category[1](): expected.flatten()[[2]] for [3], category in enumerate(['A', 'B', 'C', 'D']) if expected.flatten()[[2]] < 25}

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

A.upper

Bi

Cindex

D.lower

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using different variables for the index in the comprehension.

The method .upper() converts the category to uppercase. The index i from enumerate accesses the expected counts.