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Applying Math with Python

You're reading from   Applying Math with Python Practical recipes for solving computational math problems using Python programming and its libraries

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838989750
Length 358 pages
Edition 1st Edition
Languages
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Authors (2):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (12) Chapters Close

Preface 1. Basic Packages, Functions, and Concepts 2. Mathematical Plotting with Matplotlib FREE CHAPTER 3. Calculus and Differential Equations 4. Working with Randomness and Probability 5. Working with Trees and Networks 6. Working with Data and Statistics 7. Regression and Forecasting 8. Geometric Problems 9. Finding Optimal Solutions 10. Miscellaneous Topics 11. Other Books You May Enjoy

Testing hypotheses using t-tests

One of the most common tasks in statistics is to test the validity of a hypothesis about the mean of a normally distributed population given that you have collected sample data from that population. For example, in quality control, we might wish to test that the thickness of a sheet produced at a mill is 2 mm. To test this, we would randomly select sample sheets and measure the thickness to obtain our sample data. Then, we can use a t-test to test our null hypothesis, H0, that the mean paper thickness is 2 mm, against the alternative hypothesis, H1, that the mean paper thickness is not 2 mm. We use the SciPy stats module to compute a t statisticand a p value. If the p value is below 0.05, then we accept the null hypothesis with 5% significance (95% confidence). If the p value is larger than 0.05, then we must reject the null hypothesis in favor of our alternative hypothesis.

In this recipe, we will see how to use a t-test to test whether...

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