<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Post on Ahmed Azeez | Portfolio</title><link>https://ahmed-azeez.github.io/categories/post/</link><description>Recent content in Post on Ahmed Azeez | Portfolio</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 04 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ahmed-azeez.github.io/categories/post/index.xml" rel="self" type="application/rss+xml"/><item><title>Estimation Explained: How We Guess Population Values from a Sample</title><link>https://ahmed-azeez.github.io/2026/09/04/estimation-explained/</link><pubDate>Fri, 04 Sep 2026 00:00:00 +0000</pubDate><guid>https://ahmed-azeez.github.io/2026/09/04/estimation-explained/</guid><description>Estimation is one of the two main tools of statistical inference — the process of drawing conclusions about a population based on data collected from a sample. Instead of measuring every single person or item in a population, researchers study a smaller group and use that data to make an educated guess about the whole.
The Problem: We Can't Measure Everyone Imagine trying to find the average blood pressure of an entire country's population.</description></item><item><title>Multicollinearity Explained: Why Correlated Predictors Confuse Your Model</title><link>https://ahmed-azeez.github.io/2026/07/19/multicollinearity-explained/</link><pubDate>Sun, 19 Jul 2026 00:00:00 +0000</pubDate><guid>https://ahmed-azeez.github.io/2026/07/19/multicollinearity-explained/</guid><description>Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with one another. At first glance this may not seem like a problem — after all, if two variables are related, shouldn't they both help explain the outcome? The issue is that the model struggles to tell which variable is actually responsible for the observed association.
The Problem: Two Variables, One Signal Imagine trying to determine whether income or wealth has a stronger relationship with a health outcome.</description></item><item><title>Reporting Standards for Exploratory Factor Analysis: A Guide to Transparency</title><link>https://ahmed-azeez.github.io/2026/07/12/efa-reporting-standards/</link><pubDate>Sun, 12 Jul 2026 00:00:00 +0000</pubDate><guid>https://ahmed-azeez.github.io/2026/07/12/efa-reporting-standards/</guid><description>Exploratory Factor Analysis (EFA) is a multivariate statistical method used to determine the underlying dimensions, factors, or latent variables within a set of observed variables. To ensure your findings are replicable and interpretable, specific technical details must be transparently reported.
The Foundation: Justification &amp;amp; DataBefore diving into the numbers, researchers must justify the use of EFA over other methods like Confirmatory Factor Analysis (CFA). This is typically necessary when the factor structure is previously unknown or when developing a new scale.</description></item><item><title>R0 vs. Rt: Disease Potential vs. Disease Reality</title><link>https://ahmed-azeez.github.io/2026/07/07/r0-vs-rt/</link><pubDate>Tue, 07 Jul 2026 00:00:00 +0000</pubDate><guid>https://ahmed-azeez.github.io/2026/07/07/r0-vs-rt/</guid><description>In epidemiology, R0 and Rt are often used interchangeably — but they capture different moments in an outbreak. Understanding the distinction is critical for tracking outbreaks and guiding public health action.
R0 (Basic Reproduction Number): The Potential R0 is the average number of secondary infections one infected person would cause in a fully susceptible population, with no immunity and no interventions in place. It's a theoretical baseline — a measure of a pathogen's inherent transmissibility under ideal conditions for spread.</description></item><item><title>Statistical Diagnostic Tests Every Researcher Should Know</title><link>https://ahmed-azeez.github.io/2025/05/16/diagnostic-tests/</link><pubDate>Fri, 16 May 2025 00:00:00 +0000</pubDate><guid>https://ahmed-azeez.github.io/2025/05/16/diagnostic-tests/</guid><description>Before you run a single regression or ANOVA, there is a step that separates rigorous analysis from shaky conclusions: diagnostic testing. Think of it as a pre-flight checklist for your data. Skip it, and you risk landing in entirely the wrong place.
Why Diagnostics Matter Most statistical methods rest on assumptions — about how data are distributed, how variables relate to one another, and how errors behave. When those assumptions break down silently, the model keeps running and happily produces numbers that mean very little.</description></item><item><title>Public Health Ethics: Types, Principles and Advantages</title><link>https://ahmed-azeez.github.io/2024/02/10/public-health-ethics/</link><pubDate>Sat, 10 Feb 2024 00:00:00 +0000</pubDate><guid>https://ahmed-azeez.github.io/2024/02/10/public-health-ethics/</guid><description>What is Ethics? What is Public Health Ethics? Principles of Ethical Practice in Public Health Types of Ethics for a Public Health Professional Steps in a Process of Ethical Decision Making Importance of Ethics for Public Health Professionals References and For More Information What is Ethics? Ethics are the set of rules that govern our expectations of our own and others' behavior. The term 'ethics' is derived from the Greek word ethos that can mean custom, tradition, personality or disposition.</description></item></channel></rss>