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You're reading from  Building Statistical Models in Python

Product typeBook
Published inAug 2023
Reading LevelIntermediate
PublisherPackt
ISBN-139781804614280
Edition1st Edition
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Authors (3):
Huy Hoang Nguyen
Huy Hoang Nguyen
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Huy Hoang Nguyen

Huy Hoang Nguyen is a Mathematician and a Data Scientist with far-ranging experience, championing advanced mathematics and strategic leadership, and applied machine learning research. He holds a Master's in Data Science and a PhD in Mathematics. His previous work was related to Partial Differential Equations, Functional Analysis and their applications in Fluid Mechanics. He transitioned from academia to the healthcare industry and has performed different Data Science projects from traditional Machine Learning to Deep Learning.
Read more about Huy Hoang Nguyen

Paul N Adams
Paul N Adams
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Paul N Adams

Paul Adams is a Data Scientist with a background primarily in the healthcare industry. Paul applies statistics and machine learning in multiple areas of industry, focusing on projects in process engineering, process improvement, metrics and business rules development, anomaly detection, forecasting, clustering and classification. Paul holds a Master of Science in Data Science from Southern Methodist University.
Read more about Paul N Adams

Stuart J Miller
Stuart J Miller
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Stuart J Miller

Stuart Miller is a Machine Learning Engineer with degrees in Data Science, Electrical Engineering, and Engineering Physics. Stuart has worked at several Fortune 500 companies, including Texas Instruments and StateFarm, where he built software that utilized statistical and machine learning techniques. Stuart is currently an engineer at Toyota Connected helping to build a more modern cockpit experience for drivers using machine learning.
Read more about Stuart J Miller

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Summary

In this chapter, we introduced the concept of a hypothesis test. We started with a basic outline of a hypothesis test with the four key steps:

  • State the hypothesis
  • Perform the test
  • Determine whether to reject or fail to reject the null hypothesis
  • Draw a statistical conclusion with a scope of inference

Then we talked about potential errors that can occur and false positives and false negatives and defined the expected error rate (alpha) of a test and the power (beta) of a test.

We also discussed the statistical procedure called the z-test. This is a type of hypothesis test using sample data assumed to be normally distributed. The z-score and z-statistic were also introduced in the section on different types of z-tests, such as one-sample or two-sample z-tests for means or proportions.

Finally, we discussed the concept and motivation behind the power analysis, which can be used to identify the probability of incorrectly rejecting the null hypothesis...

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Building Statistical Models in Python
Published in: Aug 2023Publisher: PacktISBN-13: 9781804614280

Authors (3)

author image
Huy Hoang Nguyen

Huy Hoang Nguyen is a Mathematician and a Data Scientist with far-ranging experience, championing advanced mathematics and strategic leadership, and applied machine learning research. He holds a Master's in Data Science and a PhD in Mathematics. His previous work was related to Partial Differential Equations, Functional Analysis and their applications in Fluid Mechanics. He transitioned from academia to the healthcare industry and has performed different Data Science projects from traditional Machine Learning to Deep Learning.
Read more about Huy Hoang Nguyen

author image
Paul N Adams

Paul Adams is a Data Scientist with a background primarily in the healthcare industry. Paul applies statistics and machine learning in multiple areas of industry, focusing on projects in process engineering, process improvement, metrics and business rules development, anomaly detection, forecasting, clustering and classification. Paul holds a Master of Science in Data Science from Southern Methodist University.
Read more about Paul N Adams

author image
Stuart J Miller

Stuart Miller is a Machine Learning Engineer with degrees in Data Science, Electrical Engineering, and Engineering Physics. Stuart has worked at several Fortune 500 companies, including Texas Instruments and StateFarm, where he built software that utilized statistical and machine learning techniques. Stuart is currently an engineer at Toyota Connected helping to build a more modern cockpit experience for drivers using machine learning.
Read more about Stuart J Miller