R: From Origins to Enterprise Hosting
Introduction R is a free statistical language first released in August 1993. It descends from S, a language built at
R, SEE
M_Foundation, YEM
Where Models Fail
A conceptual framework – three genuinely different ways a formalized generalization can miss reality A formalization can fail to match
M_Foundation, YEM
Computation – Where the Model Becomes Real, and Where Machine Learning Breaks the Old Rules
A conceptual framework – the third leg, and the one place the earlier framework needs an extra clause Mathematics gives
M_Foundation, YEM
Statistics and Data – Uncertainty and Inference
A conceptual framework – where modeling ends and estimation begins With generalization and formalization established as modeling’s core act, statistics
M_Foundation, YEM
The Machinery – Mathematics as Structure and Logic
A conceptual framework – the vocabulary formalization needs before it can say anything precisely Generalization makes a claim. Formalization gives
M_Foundation, YEM
Generalization and Formalization
A conceptual framework – from a rough sense of a pattern to a structure you can compute with Modeling is
R, SEE
Descriptive Statistics in R: A Practical Approach
Introduction In our previous post, we covered the theory behind descriptive statistics — the formulas, notation, and intuition. Here, we
Python, SEE, uncategorized
Descriptive Statistics in Python: A Practical Analytical Guide
Introduction In our previous post, we covered the theory behind descriptive statistics — the formulas, notation, and intuition. Here, we
S_Foundation, YES
Model Validation, Selection, and Assessment
Data Set Splits in a Statistical Learning Procedure Training Set The subset of data used to fit the model –
S_Foundation, YES
Descriptive Statistics : a glimpse
Notation Numeric / Count Data Central Tendency Mean (Arithmetic Mean) The arithmetic mean is the sum of all observed values