By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups.
Statistical Analysis using Python Numpy
Statistical Analysis using Python Numpy
Instructor: David Dalsveen
Guided Project
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Intermediate level
Recommended experience
2 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.6
(16 reviews)
What you'll learn
Obtain two Numpy arrays from the DataFrame column to represent Female student scores and Male Student scores.
Add the Numpy code to determine the T-value and P-value of the data sets.
Add the function to remove outliers from each set of data, then re-compute the T-value and P-value.
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Taught in English
Guided Project
Learn, practice, and apply job-ready skills with expert guidance
Intermediate level
Recommended experience
2 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning
4.6
(16 reviews)
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Analyze the T-Test problem and use the Python Pandas to read from the CSV into a Data Frame.
Obtain two Numpy arrays from the DataFrame column to represent Female student scores and Male Student scores.
Compute the variance of the two arrays using the standard deviation from each array.
Add the Numpy code to compute the pooled Variance and standard deviation and determine the T-value and P-value of the data sets.
Add a function to remove outliers from each set of data, then re-compute the T-value and P-value.
Recommended experience
Learners should be familiar with some Python; variables and input/output, and the basic data structures.
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Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Reviewed on Jun 22, 2023
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