ANOVA (f_oneway) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

SciPy

from scipy.stats import [1]

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

Attest_ind

Blinregress

Cpearsonr

Df_oneway

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong function like ttest_ind instead of f_oneway.

Using a function for correlation or regression instead of ANOVA.

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

2fill in blank

medium

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

SciPy

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

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

Adata1

Bgroup1

Csample1

Dvalues1

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable name that does not exist like data1.

Passing the wrong variable as the first argument.

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

3fill in blank

hard

Fix the error in the code to correctly perform ANOVA on groupA, groupB, and groupC.

SciPy

result = f_oneway(groupA, [1], groupC)

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

AgroupB

BgroupC

CgroupA

DgroupD

Attempts:

3 left

💡 Hint

Common Mistakes

Repeating groupC or groupA instead of using groupB.

Using a group name that does not exist.

The second argument should be groupB to include all three groups in the ANOVA test.

4fill in blank

hard

Fill both blanks to create a dictionary with keys 'statistic' and 'pvalue' from the ANOVA result.

SciPy

anova_result = {'statistic': result.[1], 'pvalue': result.[2]

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

Astatistic

Bpvalue

Cp_value

Dstat

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'p_value' instead of 'pvalue'.

Using 'stat' instead of 'statistic'.

The ANOVA result object has attributes statistic and pvalue to access the F-statistic and p-value.

5fill in blank

hard

Fill all three blanks to perform ANOVA on data1, data2, data3 and print the p-value.

SciPy

stat, [1] = f_oneway([2], [3], data3)
print('P-value:', p)

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

Ap

Bdata1

Cdata2

Dstatistic

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning p-value to a wrong variable name.

Swapping the order of data1 and data2.

The function returns statistic and p-value; assign p-value to p. The groups are data1, data2, and data3.

Practice

1.

What does the scipy.stats.f_oneway function test in data science?

easy

A. If a dataset follows a normal distribution

B. If two variables are correlated

C. If the means of multiple groups are significantly different

D. If the variance within a single group is zero

Solution

1. Step 1: Understand the purpose of ANOVA

   ANOVA (Analysis of Variance) tests if the means of multiple groups differ significantly.

2. Step 2: Identify what f_oneway does

   The f_oneway function in scipy performs this ANOVA test on given groups.

3. Final Answer:

   If the means of multiple groups are significantly different -> Option C

4. Quick Check:

   ANOVA tests group means difference = A [OK]

Hint: ANOVA checks if group averages differ significantly [OK]

Common Mistakes:

• Confusing ANOVA with correlation tests
• Thinking it tests variance within one group
• Assuming it checks normality of data

2.

Which of the following is the correct way to call scipy.stats.f_oneway for three groups a, b, and c?

import scipy.stats as stats

# Choose the correct call

easy

A. stats.f_oneway(a + b + c)

B. stats.f_oneway(a, b, c)

C. stats.f_oneway([a, b, c])

D. stats.f_oneway(a, [b, c])

Solution

1. Step 1: Check function signature

   f_oneway takes each group as a separate argument, not a list of groups.

2. Step 2: Match correct call

   Passing groups as separate arguments like a, b, c is correct.

3. Final Answer:

   stats.f_oneway(a, b, c) -> Option B

4. Quick Check:

   Separate groups as arguments = C [OK]

Hint: Pass each group as separate arguments, not a list [OK]

Common Mistakes:

• Passing a list of groups instead of separate arguments
• Concatenating groups instead of passing separately
• Mixing list and individual arguments

3.

What will be the output of the following code?

import scipy.stats as stats

a = [5, 7, 8, 6]
b = [6, 9, 7, 10]
c = [15, 18, 20, 17]

result = stats.f_oneway(a, b, c)
print("{:.3f}".format(result.pvalue))

medium

A. 0.000

B. 0.500

C. 0.050

D. 0.100

Solution

1. Step 1: Understand the data groups

   Groups a and b have similar means (~6.5-8), group c has much higher values (~17.5).

2. Step 2: Interpret ANOVA result

   Because group c differs strongly, the p-value will be very low, indicating significant difference.

3. Final Answer:

   0.000 -> Option A

4. Quick Check:

   Big group mean difference -> low p-value = 0.000 [OK]

Hint: Big mean differences give very low p-values [OK]

Common Mistakes:

• Assuming p-value is high when groups differ
• Confusing p-value with F-statistic
• Rounding errors in p-value

4.

Identify the error in this code snippet that uses scipy.stats.f_oneway:

import scipy.stats as stats

a = [1, 2, 3]
b = [4, 5, 6]

result = stats.f_oneway(a, b)
print(result.pvalue)

medium

A. Missing import statement for scipy

B. Using pvalue instead of p_value

C. Passing lists instead of numpy arrays

D. No error; code runs correctly

Solution

1. Step 1: Check imports and function usage

   The code imports scipy.stats as stats correctly and calls f_oneway with two lists.

2. Step 2: Verify output attribute

   The result object has attribute pvalue (not p_value), so accessing result.pvalue is correct.

3. Final Answer:

   No error; code runs correctly -> Option D

4. Quick Check:

   Lists accepted and pvalue attribute correct = B [OK]

Hint: Lists work; attribute is 'pvalue', not 'p_value' [OK]

Common Mistakes:

• Thinking lists must be numpy arrays
• Using wrong attribute name for p-value
• Missing import statement

5.

You have three groups of exam scores:

group1 = [70, 75, 80, 85]
group2 = [72, 78, 79, 83]
group3 = [90, 92, 88, 91]

You want to test if the average scores differ significantly. After running scipy.stats.f_oneway(group1, group2, group3), you get a p-value of 0.04.

What is the correct conclusion at a 5% significance level?

hard

A. At least one group mean is significantly different

B. The test is invalid because p-value is less than 0.05

C. All group means are exactly equal

D. There is no significant difference between group means

Solution

1. Step 1: Understand p-value meaning

   A p-value of 0.04 means there is a 4% chance the observed differences happened by random chance.

2. Step 2: Compare p-value to significance level

   Since 0.04 < 0.05, we reject the null hypothesis that all group means are equal.

3. Final Answer:

   At least one group mean is significantly different -> Option A

4. Quick Check:

   p-value < 0.05 means significant difference = D [OK]

Hint: p-value below 0.05 means significant difference [OK]

Common Mistakes:

• Thinking p-value < 0.05 means no difference
• Assuming all means are equal if p-value is low
• Misinterpreting significance level