You're reading from Building Statistical Models in Python

Product typeBook

Published inAug 2023

Reading LevelIntermediate

PublisherPackt

ISBN-139781804614280

Edition1st Edition

Languages

Python

Concepts

Statistics

Authors (3):

Huy Hoang Nguyen

Paul N Adams

Stuart J Miller

View More author details

Assumptions of parametric tests

Parametric tests make assumptions about population data that require the statistics practitioner to perform analysis of data prior to modeling, especially when using sample data because the sample statistics are leveraged as estimates for the population parameters when the true population parameters are unknown. These are the three primary assumptions of parametric hypothesis tests:

Normally distributed population data
Samples are independent
Equal population variances (when comparing two or more groups)

In this chapter, we discuss the z-test, t-test, ANOVA, and Pearson’s correlation. These tests are used on continuous data. In addition to these assumptions, Pearson’s correlation requires data to contain paired samples. In other words, there must be an equal number of samples in each group being compared as Pearson’s correlation is based on pairwise comparisons.

While these assumptions are ideal, there are...

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Building Statistical Models in Python

Published in: Aug 2023Publisher: PacktISBN-13: 9781804614280

Authors (3)

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 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 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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Assumptions of parametric tests

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