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This repository contains four different hypothesis testing projects, analyzing real-world data to validate assumptions and drive data-driven decisions. Each project applies statistical tests (e.g., t-tests, chi-square, ANOVA) to uncover insights and support business strategies. Built with Python, Pandas, SciPy, and Statsmodels. πŸš€

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Hypothesis Testing: Problem Statements and Solutions

Welcome to the Hypothesis Testing repository! πŸŽ‰ This repository contains solutions to four distinct problem statements, showcasing the application of various statistical techniques in hypothesis testing using Python.

πŸ“‚ Repository Structure The repository is organized as follows:

Copy code

β”œβ”€β”€ Datasets.zip

β”œβ”€β”€ Problem1.ipynb

β”œβ”€β”€ Problem2.ipynb

β”œβ”€β”€ Problem3.ipynb

β”œβ”€β”€ Problem4.ipynb

β”œβ”€β”€ README.md

Each Python file focuses on solving a unique problem statement using hypothesis testing methods.

πŸ› οΈ Tools and Libraries The solutions use Python and the following libraries:

  • numpy
  • scipy
  • pandas
  • matplotlib (for visualization)

πŸ“Š Insights Each solution includes:

  • Hypothesis formulation (null and alternative hypotheses).
  • Python implementation of the hypothesis test.
  • Interpretation of the results.

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This repository contains four different hypothesis testing projects, analyzing real-world data to validate assumptions and drive data-driven decisions. Each project applies statistical tests (e.g., t-tests, chi-square, ANOVA) to uncover insights and support business strategies. Built with Python, Pandas, SciPy, and Statsmodels. πŸš€

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