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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

Performing parametric tests

The hypothesis is the main core topic of inferential statistics. In this section, we will focus on parametric tests. The basic assumption of a parametric test is the underlying statistical distribution. Most elementary statistical methods are parametric in nature. Parametric tests are used for quantitative and continuous data. Parameters are numeric quantities that represent the whole population. Parametric tests are more powerful and reliable than non-parametric tests. The hypothesis is developed on the parameters of the population distribution. Here are some examples of parametric tests:

  • A t-test is a kind of parametric test that is used for checking if there is a significant difference between the means of the two groups concerned. It is the most commonly used inferential statistic that follows the normal distribution. A t-test has two types: a one-sample t-test and a two-sample t-test. A one-sample t-test is used for checking if there is a significant...
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