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Statistical Inference
UA DATALAB WORKSHOP SERIES, FALL 2023
📓 These notes: https://github.com/ua-datalab/Workshops/wiki/Statistical-Inference/
💻 Codebase with examples:
📹 Link to Zoom recording - (10-17-2023)
(Image credit: Florian Geiser, Towards AI Medium)
Statistical inference in data science refers to the process of drawing conclusions or making predictions about a population based on a sample of data. It plays a crucial role in extracting meaningful insights from data.
Here are key points to understand about statistical inference:
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Sample to Population: Statistical inference involves extrapolating insights from a sample dataset to make statements about the larger population it represents. It addresses questions like, "What can we infer about the entire population based on this sample?"
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Types of Statistical Inference: There are two main types of statistical inference:
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Descriptive Statistics: This aspect involves summarizing and describing data using measures like mean, median, and standard deviation, but it doesn't make predictions or inferences about the population.
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Inferential Statistics: This includes hypothesis testing, confidence intervals, regression analysis, and more. It aims to make predictions, test hypotheses, and draw conclusions about the population.
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Procedure: Statistical inference typically involves several steps, including data collection, hypothesis formulation, statistical analysis, and drawing conclusions.
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Importance in Data Science: Statistical inference is fundamental in data science as it allows data scientists to make data-driven decisions, test hypotheses, and assess the significance of results.
Statistical inference is a cornerstone of data science, enabling professionals to extract meaningful insights and make informed choices from data.
Statsmodels is a Python package that plays a crucial role in statistical modeling and analysis. It offers a wide range of functionalities for estimating various statistical models and conducting statistical analysis.
Here's a concise overview of Statsmodels:
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Statistical Modeling: Statsmodels provides classes and functions for estimating diverse statistical models. These models encompass linear and non-linear regression, generalized linear models, time-series analysis, and more.
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Statistical Testing: It includes advanced functions for statistical testing. This is essential for hypothesis testing, assessing model fit, and making statistical inferences from data.
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Data Exploration: Statsmodels facilitates statistical data exploration. You can use it to explore the relationships between variables, visualize data, and gain insights into the underlying statistical properties.
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Complement to SciPy: While SciPy offers a foundation for scientific computing, Statsmodels complements it by focusing on statistical computations. It's particularly useful for researchers, data scientists, and statisticians.
Statsmodels is a valuable tool for anyone working with data analysis and statistical modeling in Python. You can find detailed documentation and examples on the official Statsmodels website
Created: 10-04-2023; Updated: 10-04-2023
Carlos Lizárraga, Data Lab, Data Science Institute, University of Arizona, 2023.
