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Tag: Python Tutorial

PyFriday Tutorial: How to Make a SIR Model in Python

Posted on April 5, 2024 by Cody Micah Carmichael MPH, CPH

The Susceptible-Infected-Recovered (SIR) model is a fundamental concept in epidemiology, offering insights into how diseases spread and recede in populations over time through a relatively simply set of functions. For

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Python, Tutorial Python Tutorial, SIR Model, Tutorials Leave a comment

PyFriday Tutorial: How to Calculate Relative Risk in Python

Posted on March 22, 2024 by Cody Micah Carmichael MPH, CPH

Introduction to Relative Risk Relative Risk (RR) is one of the most fundamental measures in  public health, offering insights into the strength of association between an exposure (like smoking) and

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Python Python Tutorial, Relative Risk, Tutorials Leave a comment

PyFriday Tutorial: How to Calculate T-Tests in Python

Posted on March 15, 2024 by Cody Micah Carmichael MPH, CPH

T-tests are a fundamental statistical tool used in various fields, including public health, to compare the means of two groups. Essentially, a T-test helps determine whether the observed differences in

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Python, Tutorial Python Tutorial, T-Test, Tutorials Leave a comment

PyFriday Tutorial: How to Calculate Odds Ratio in Python

Posted on March 9, 2024 by Cody Micah Carmichael MPH, CPH

Introduction Odds Ratio (OR) calculations are a cornerstone in public health research, providing insights into the strength of association between an exposure and an outcome. In this tutorial, we’ll explore

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Python, Tutorial Odds Ratio, Python Tutorial, Tutorials Leave a comment

PyFriday: How to Calculate Correlation in Python

Posted on March 1, 2024 by Cody Micah Carmichael MPH, CPH

  Correlation is a statistical measure that describes the extent to which two variables change together. In data analysis tasks, understanding correlation can often help in identifying relationships between variables.

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Python, Tutorial Correlation, Python Tutorial, Tutorials Leave a comment

How to Install Python and a Python IDE

Posted on April 22, 2022 by Cody Micah Carmichael MPH, CPH

Python has quickly gained traction as a competitor and companion to R in the world of data analysis. As such, being able to use this immensely versatile language is quickly

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Python, Tutorial Install, PyCharm, Python Tutorial Leave a comment

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  • CPH Focus: Evidence-Based Approaches to Public Health : Regression Analysis : Logistic Regression July 29, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health : Regression Analysis : Linear Regression July 24, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health : Regression Analysis : Survival Analysis July 22, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health : Hypothesis Testing : Parametric and Non-parametric Tests July 17, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health : Hypothesis Testing : Type I and Type II errors July 15, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health : Hypothesis Testing : Null and Alternative Hypotheses July 10, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health: Biostatistics – Probability: Probability Distributions June 12, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health: Biostatistics – Probability: Basic Probability Concepts June 10, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health: Biostatistics – Inferential Statistics: p-Values and Statistical Significance June 5, 2025
  • CPH Focus: Evidence-Based Approaches to Public Health: Biostatistics – Inferential Statistics: Confidence Intervals June 3, 2025

Recent Posts

  • CPH Focus: Evidence-Based Approaches to Public Health : Regression Analysis : Logistic Regression
  • CPH Focus: Evidence-Based Approaches to Public Health : Regression Analysis : Linear Regression
  • CPH Focus: Evidence-Based Approaches to Public Health : Regression Analysis : Survival Analysis
  • CPH Focus: Evidence-Based Approaches to Public Health : Hypothesis Testing : Parametric and Non-parametric Tests
  • CPH Focus: Evidence-Based Approaches to Public Health : Hypothesis Testing : Type I and Type II errors

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