feat: new article

Author: DiogoRibeiro7

_posts/-_ideas/2039-01-01-statistics.md

@@ -57,6 +57,3 @@ title: Exploring Key Topics in Statistics
 
 - **TODO: Nonparametric Methods: Statistics Without Distribution Assumptions**
    - Learn about nonparametric statistical methods, which are used when the data does not meet the assumptions of parametric tests. The article covers common nonparametric tests like the Mann-Whitney U test, Kruskal-Wallis test, and the Wilcoxon signed-rank test.
-
-- **TODO: Correlation vs. Causation: Understanding Relationships Between Variables**
-   - This article explains the difference between correlation and causation, a common point of confusion in statistical analysis. It discusses how to use correlation coefficients to measure the strength of relationships and how to determine causality using controlled experiments.

_posts/2020-01-01-correlation_vs_causation_understanding_relationships_between_variables.md

@@ -0,0 +1,42 @@
+---
+author_profile: false
+categories:
+- Statistics
+classes: wide
+date: '2020-01-01'
+excerpt: Learn the critical difference between correlation and causation in data analysis,
+  how to interpret correlation coefficients, and why controlled experiments are essential
+  for establishing causality.
+header:
+  image: /assets/images/data_science_13.jpg
+  og_image: /assets/images/data_science_13.jpg
+  overlay_image: /assets/images/data_science_13.jpg
+  show_overlay_excerpt: false
+  teaser: /assets/images/data_science_13.jpg
+  twitter_image: /assets/images/data_science_13.jpg
+keywords:
+- Correlation
+- Causation
+- Statistics
+- Data analysis
+seo_description: Explore the difference between correlation and causation in statistical
+  analysis, including methods for measuring relationships and determining causality.
+seo_title: 'Understanding Correlation vs. Causation: Statistical Analysis Guide'
+seo_type: article
+summary: This article breaks down the essential difference between correlation and
+  causation, covering how correlation coefficients measure relationship strength and
+  how controlled experiments establish causality.
+tags:
+- Correlation
+- Causation
+- Data analysis
+- Statistics
+title: 'Correlation vs. Causation: Understanding Relationships Between Variables'
+---
+
+<p align="center">
+  <img src="/assets/images/biographies/correlation_causation.jpeg" alt="Example Image">
+</p>
+<p align="center"><i>Emmy Noether</i></p>
+
+

_posts/data science/2024-06-08-iot_and_data_science_for_climate_action.md

@@ -4,8 +4,7 @@ categories:
 - Data Science
 classes: wide
 date: '2024-06-08'
-excerpt: IoT and data science together offer powerful tools for monitoring environmental
-  conditions, analyzing climate data, and supporting global climate action initiatives.
+excerpt: IoT and data science together offer powerful tools for monitoring environmental conditions, analyzing climate data, and supporting global climate action initiatives.
 header:
   image: /assets/images/data_science_14.jpg
   og_image: /assets/images/data_science_14.jpg
@@ -21,12 +20,10 @@ keywords:
 - Climate Action
 - Data Science
 - Internet of Things
-seo_description: An in-depth exploration of IoT's role in monitoring climate conditions
-  and how data science transforms this data into actionable insights for climate action.
+seo_description: An in-depth exploration of IoT's role in monitoring climate conditions and how data science transforms this data into actionable insights for climate action.
 seo_title: Using IoT and Data Science for Climate Action
 seo_type: article
-summary: Explore how IoT devices and data science combine to monitor and analyze environmental
-  data, providing essential insights to support climate action and sustainability.
+summary: Explore how IoT devices and data science combine to monitor and analyze environmental data, providing essential insights to support climate action and sustainability.
 tags:
 - Iot
 - Climate change