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Model Perspective
Model Perspective
Sep 17, 2023 · Fundamentals

Why Correlation Isn’t Causation: Methods to Reveal True Relationships in Data

This article explains the difference between correlation and causation, illustrates common misconceptions with real‑world examples, and introduces statistical tools such as randomized experiments, instrumental variables, propensity score matching, and difference‑in‑differences that help researchers uncover genuine causal effects in mathematical modeling.

causal inferencecausalitycorrelation
0 likes · 9 min read
Why Correlation Isn’t Causation: Methods to Reveal True Relationships in Data
DaTaobao Tech
DaTaobao Tech
May 22, 2023 · Artificial Intelligence

Statistical and Machine Learning Metrics for Data Analysis

The article presents a practical toolbox of statistical and machine‑learning metrics—including short‑term growth rates, CAGR, Excel forecasting functions, Wilson score adjustment, sigmoid decay weighting, correlation coefficients, KL divergence, elbow detection with KneeLocator, entropy‑based weighting, PCA, and TF‑IDF—offering concise formulas and code snippets for data analysis without deep theory.

PCAcorrelationdata analysis
0 likes · 12 min read
Statistical and Machine Learning Metrics for Data Analysis
Model Perspective
Model Perspective
Dec 1, 2022 · Fundamentals

Mastering One‑Way and Multi‑Factor ANOVA in R: From Theory to Code

This article explains the principles of one‑way, two‑way and multi‑factor ANOVA, demonstrates how to perform them in R using aov(), lm() and anova(), and shows post‑hoc comparisons with TukeyHSD as well as correlation testing with cor() and cor.test().

ANOVARTukey test
0 likes · 10 min read
Mastering One‑Way and Multi‑Factor ANOVA in R: From Theory to Code
Model Perspective
Model Perspective
Aug 27, 2022 · Fundamentals

Understanding Pearson, Spearman, and Kendall Correlation Coefficients with Pandas

Learn how Pearson, Spearman, and Kendall correlation coefficients measure linear and monotonic relationships between variables, explore their mathematical properties, interpret their value ranges, and see practical Python examples using Pandas to compute each coefficient on generated data.

correlationdata analysiskendall
0 likes · 6 min read
Understanding Pearson, Spearman, and Kendall Correlation Coefficients with Pandas
NetEase LeiHuo UX Big Data Technology
NetEase LeiHuo UX Big Data Technology
Jul 20, 2022 · Game Development

Correlation vs Causation in Game Design: How to Avoid Misleading Data Interpretations

The article explains how spurious correlations can mislead game designers and product managers, illustrates the difference between correlation and causation with real‑world examples, and offers practical methods such as focusing on passive variables, ensuring hard stratification, and using behavior‑chain matching to draw more reliable conclusions.

AB testingcausationcorrelation
0 likes · 9 min read
Correlation vs Causation in Game Design: How to Avoid Misleading Data Interpretations
Model Perspective
Model Perspective
Jul 9, 2022 · Fundamentals

How to Compute Key Statistics with NumPy and Pandas DataFrames

This guide shows how to calculate common statistical measures such as mean, median, range, variance, standard deviation, covariance, and correlation using NumPy functions, and demonstrates the equivalent operations with Pandas DataFrames, including a table of useful DataFrame methods for statistical analysis.

NumPyPythoncorrelation
0 likes · 3 min read
How to Compute Key Statistics with NumPy and Pandas DataFrames
Model Perspective
Model Perspective
Jul 8, 2022 · Fundamentals

Unlocking Statistics: Key Concepts from Samples to Skewness Explained

This article introduces the fundamentals of statistical analysis, covering the distinction between populations and samples, constructing frequency tables and histograms, and explaining essential descriptive measures such as mean, median, variance, standard deviation, range, skewness, kurtosis, covariance, and correlation coefficients.

correlationdescriptive statisticshistogram
0 likes · 5 min read
Unlocking Statistics: Key Concepts from Samples to Skewness Explained
DataFunSummit
DataFunSummit
Dec 15, 2021 · Fundamentals

Concrete Strength Data Analysis Using Pandas: A Step‑by‑Step Tutorial

This tutorial walks through a complete pandas‑based workflow for analyzing a concrete‑strength dataset, covering data loading, cleaning, exploratory visualizations, correlation analysis, and targeted sub‑group investigations to uncover factors influencing product strength and suggest improvement measures.

PythonVisualizationconcrete strength
0 likes · 13 min read
Concrete Strength Data Analysis Using Pandas: A Step‑by‑Step Tutorial
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Nov 1, 2021 · Fundamentals

Nine Basic Data Analysis Methods for Business Insights

This article introduces nine fundamental data analysis techniques—including periodic, structural, layered, matrix, decomposition, funnel, correlation, tag, and MECE methods—explaining how to apply each to interpret business metrics, uncover insights, and guide decision‑making without requiring advanced mathematics or complex algorithms.

MEMEbasic methodsbusiness metrics
0 likes · 12 min read
Nine Basic Data Analysis Methods for Business Insights
Code Ape Tech Column
Code Ape Tech Column
Feb 25, 2021 · Fundamentals

What Starbucks Can Teach Us About Two-Phase Commit and Asynchronous Messaging

The article uses Starbucks' coffee‑ordering process to illustrate asynchronous order handling, correlation identifiers, exception‑handling strategies, two‑phase commit, and conversation patterns, showing how real‑world practices inform the design of reliable distributed messaging systems.

Two-Phase Commitasynchronous messagingcorrelation
0 likes · 8 min read
What Starbucks Can Teach Us About Two-Phase Commit and Asynchronous Messaging