ANOVA in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

from scipy.stats import [1]

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Apearsonr

Bchi2_contingency

Cf_oneway

Dttest_ind

Attempts:

3 left

💡 Hint

Common Mistakes

Importing ttest_ind instead of f_oneway

Using pearsonr which is for correlation

Using chi2_contingency which is for categorical data

The f_oneway function from scipy.stats is used to perform one-way ANOVA.

2fill in blank

medium

Complete the code to perform one-way ANOVA on three groups: group1, group2, and group3.

Data Analysis Python

stat, p = f_oneway([1], group2, group3)

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Adata

Bgroup1

Cgroup4

Dsamples

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable that does not exist like group4

Using the whole dataset instead of individual groups

The first argument to f_oneway should be the first group of data, here group1.

3fill in blank

hard

Fix the error in the code to correctly perform ANOVA on three groups stored in a list called data_groups.

Data Analysis Python

stat, p = f_oneway(*[1])

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Adata_groups

Bdata_groups[0]

Cdata_groups()

Ddata_groups[:]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing only the first group instead of all groups

Calling the list as a function which causes an error

The asterisk * unpacks the list data_groups so each group is passed as a separate argument.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each group name to its mean value.

Data Analysis Python

group_means = {name: [1] for name, data in [2].items()}

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Adata.mean()

Bdata.sum()

Cgroups

Dgroup_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum instead of mean

Using the wrong dictionary name

The comprehension calculates the mean of each group's data. The dictionary groups holds the group names and data.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that includes only groups with mean greater than 50.

Data Analysis Python

filtered_means = {name: mean for name, mean in [1].items() if mean [2] [3]

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Agroup_means

B>

C50

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than operator instead of greater than

Using the wrong dictionary variable

This comprehension filters the group_means dictionary to keep only groups with mean values greater than 50.