Goodness of fit evaluation in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function used for goodness of fit test from scipy.stats.

SciPy

from scipy.stats import [1]

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

Attest_ind

Bpearsonr

Cchisquare

Dlinregress

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a test for means like ttest_ind instead of chisquare.

Confusing correlation tests with goodness of fit tests.

The chisquare function from scipy.stats is used to perform the chi-square goodness of fit test.

2fill in blank

medium

Complete the code to perform a chi-square goodness of fit test on observed data.

SciPy

observed = [20, 30, 50]
expected = [25, 25, 50]
stat, p_value = chisquare(f_obs=[1], f_exp=expected)

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

Aobserved

Bexpected

C[25, 25, 50]

D[20, 30, 50]

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping observed and expected data in the function call.

Passing the expected list to f_obs.

The f_obs parameter requires the observed frequencies, so we pass the observed list.

3fill in blank

hard

Fix the error in the code to correctly calculate the chi-square test statistic and p-value.

SciPy

from scipy.stats import chisquare
observed = [15, 25, 60]
expected = [20, 20, 60]
stat, p_value = chisquare(f_obs=observed, f_exp=[1])

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

Aobserved

Bexpected

C[15, 25, 60]

D[20, 25, 60]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing observed data as expected frequencies.

Using incorrect lists for expected frequencies.

The f_exp parameter must be the expected frequencies, so expected is correct.

4fill in blank

hard

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

SciPy

categories = ['A', 'B', 'C', 'D']
observed = [10, 25, 30, 15]
result = {categories[i]: [1] for i in range(len(categories)) if [2]

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

Aobserved[i]

Bcategories[i]

Cobserved[i] > 20

Dcategories[i] > 20

Attempts:

3 left

💡 Hint

Common Mistakes

Using category names as values instead of frequencies.

Filtering by category names instead of frequencies.

The dictionary maps category names to observed frequencies, but only includes those where the frequency is greater than 20.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase category names to observed frequencies only if the frequency is less than or equal to 25.

SciPy

categories = ['a', 'b', 'c', 'd']
observed = [10, 25, 30, 15]
result = { [1]: [2] for i in range(len(categories)) if [3] }

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

Acategories[i].upper()

Bobserved[i]

Cobserved[i] <= 25

Dcategories[i]

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting category names to uppercase.

Using wrong comparison operators in the condition.

Mixing up keys and values in the dictionary comprehension.

The dictionary comprehension maps uppercase category names to observed frequencies, including only those frequencies less than or equal to 25.