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Paired t-test

The following function conducts a paired t-test:

python

This process resembles the one used for independent samples, but here we do not need to check the homogeneity of variance. The paired t-test explicitly does not assume that variances are equal.

Keep in mind that for a paired t-test, it's crucial that the sample sizes are equal.

With this information in mind, you can proceed to the task of conducting a paired t-test.

Here, you have data regarding the number of downloads for a particular app. Take a look at the samples: the mean values are nearly identical.


              123456789101112
            
import pandas as pd
import matplotlib.pyplot as plt

# Read the data
before = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/before.csv').squeeze()
after = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/after.csv').squeeze()
# Plot histograms
plt.hist(before, alpha=0.7)
plt.hist(after, alpha=0.7)
# Plot the means
plt.axvline(before.mean(), color='blue', linestyle='dashed')
plt.axvline(after.mean(), color='gold', linestyle='dashed')

Taak

Swipe to start coding

Hypotheses are established:

H₀: The mean number of downloads before and after the changes is the same;
Hₐ: The mean number of downloads is greater after the modifications.

Conduct a paired t-test with this alternative hypothesis, using before and after as the samples.

Oplossing

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Paired t-test

The following function conducts a paired t-test:

python

This process resembles the one used for independent samples, but here we do not need to check the homogeneity of variance. The paired t-test explicitly does not assume that variances are equal.

Keep in mind that for a paired t-test, it's crucial that the sample sizes are equal.

With this information in mind, you can proceed to the task of conducting a paired t-test.

Here, you have data regarding the number of downloads for a particular app. Take a look at the samples: the mean values are nearly identical.


              123456789101112
            
import pandas as pd
import matplotlib.pyplot as plt

# Read the data
before = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/before.csv').squeeze()
after = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/after.csv').squeeze()
# Plot histograms
plt.hist(before, alpha=0.7)
plt.hist(after, alpha=0.7)
# Plot the means
plt.axvline(before.mean(), color='blue', linestyle='dashed')
plt.axvline(after.mean(), color='gold', linestyle='dashed')

Taak

Swipe to start coding

Hypotheses are established:

H₀: The mean number of downloads before and after the changes is the same;
Hₐ: The mean number of downloads is greater after the modifications.

Conduct a paired t-test with this alternative hypothesis, using before and after as the samples.

Oplossing

Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties

Was alles duidelijk?

Bedankt voor je feedback!

Sectie 6. Hoofdstuk 8

Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties