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Numbers in a regression table tell you that an association exists. A map tells you where it exists — and where it is worst. In maternal health research across South Asia and Sub-Saharan Africa, this geographic dimension is not just interesting; it is essential for policy.
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A random variable is a function that maps outcomes of a random experiment to numbers. It is the bridge between probability theory and data.
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Bayes’ Theorem is the mathematical foundation of rational belief update. It is arguably the most important equation in statistics and modern ML.
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Conditional probability is the probability of an event given that another event has occurred. It is perhaps the single most important concept in applied statistics and ML.
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Probability is the mathematical language of uncertainty. Every ML model — from logistic regression to deep neural networks — is built on probability theory.
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Covariance and correlation measure the linear relationship between two variables — the foundation of regression, PCA, and feature selection.
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Mean and variance describe location and spread. Skewness and kurtosis describe the shape of a distribution — critical for choosing the right model.
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Two datasets can have identical means but completely different spreads. Dispersion measures capture how spread out values are around the center.
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A measure of central tendency summarises an entire distribution with a single representative value. Choosing the wrong one can completely mislead your analysis.
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Understanding data types is the most fundamental step before any analysis. Choosing the wrong statistical test or ML algorithm because you misidentified your data type is one of the most common mistakes in practice.
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By Md Salek Miah — Statistician & ML Researcher | SUST, Bangladesh | saleksta@gmail.com
Teaching statistics for ML to researchers, epidemiologists, and data scientists — from first principles to advanced methods.
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In recent years, machine learning (ML) has transformed how researchers analyze complex health data. From predicting skilled birth attendance to classifying air quality, algorithms like XGBoost and Random Forest now routinely outperform traditional statistical models. But in public health, accuracy alone is not enough — we need to understand why a model makes a prediction.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.