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https://github.com/fabianacampanari/practicalstats-pucsp-2024

Statistical Measures in Python - Age and Salary Analysis
https://github.com/fabianacampanari/practicalstats-pucsp-2024

excel hipoteses-tests hypothesis-testing linear-regression math matplotlib numpy pandas probabilistic-data-structures probability-distribution probability-statistics pythob3 scikit-learn scipy scipy-stats seaborn statisctics statsmodels

Last synced: 11 days ago
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Statistical Measures in Python - Age and Salary Analysis

Host: GitHub
URL: https://github.com/fabianacampanari/practicalstats-pucsp-2024
Owner: FabianaCampanari
License: mit
Created: 2024-10-16T20:13:06.000Z (20 days ago)
Default Branch: main
Last Pushed: 2024-10-26T04:02:29.000Z (11 days ago)
Last Synced: 2024-10-26T14:44:29.911Z (11 days ago)
Topics: excel, hipoteses-tests, hypothesis-testing, linear-regression, math, matplotlib, numpy, pandas, probabilistic-data-structures, probability-distribution, probability-statistics, pythob3, scikit-learn, scipy, scipy-stats, seaborn, statisctics, statsmodels
Language: Jupyter Notebook
Homepage: https://github.com/FabianaCampanari/statisticalMeasures-python-
Size: 11.8 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff

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README

![2129d7ad-6afd-4166-9bf1-a4f9c3b9e5cc](https://github.com/user-attachments/assets/faf3e0a3-3610-4c0b-99bb-afb7a765f28d)

Practical Stats - PUCSP 2nd Semester 2024

**Statistics and Probability**

This repository, created by Fabiana Campanari in the 2nd semester of 2024, consolidates the materials and code developed for the Statistics and Probability course within the Data Science and Artificial Intelligence program at PUC-SP, under the guidance of Professor Erick Bacconni. It is designed to support hands-on learning through exercises, scripts, and datasets.

### Repository Contents:

- **Python Scripts**: This section includes scripts for a wide range of statistical analyses, covering key topics such as distributions, population and sample concepts, and hypothesis testing. Calculations include:
- **Central Tendency**: Mean, median, and mode
- **Dispersion**: Standard deviation, variance, and range
- **Positional Measures**: Percentiles and quartiles (Q1, Q2, Q3)
- **Distribution Shape**: Skewness and kurtosis
- **Confidence Intervals** for estimating population parameters
- **Correlation and Covariance** for bivariate analysis

- **Practical Exercises**: Available in Python and Excel, these exercises provide hands-on practice in calculating statistical measures and applying concepts, including:
- **Analysis of Variance (ANOVA)**: Comparing means across multiple groups
- **Hypothesis Testing**: Null hypothesis (\( H_0 \)) tests, such as T-tests (one-sample, independent, and paired), ANOVA, and Chi-square tests
- **Regression Analysis**: Linear regression models for predictive analysis
- **Probability**: Exercises covering probability distributions, expected value, and variance

- **Statistical Tests**: This section includes implementations of statistical tests tailored to analyze variables like age and salary, categorized by region and educational level. Each test includes the process of setting up and testing the null hypothesis (\( H_0 \)) for statistical significance.

- **Support Materials**: Supplementary documentation on probability, relevant datasets, and homework assignments to reinforce key concepts.

### Study Topics:

This repository provides a comprehensive foundation in core topics, including Descriptive Statistics, Probability Distributions, Population and Sample, Hypothesis Testing I and II (featuring null hypothesis (\( H_0 \)) testing), and Regression Analysis. It serves as a practical tool for building statistical and analytical skills.

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