chore: new article

Author: DiogoRibeiro7

_posts/2020-01-13-rethinking_statistical_test_selection_why_diagrams_failing_us.md

@@ -5,9 +5,7 @@ categories:
 - Statistics
 classes: wide
 date: '2020-01-13'
-excerpt: Most diagrams for choosing statistical tests miss the bigger picture. Here's
-  a bold, practical approach that emphasizes interpretation over mechanistic rules,
-  and cuts through statistical misconceptions like the N>30 rule.
+excerpt: Most diagrams for choosing statistical tests miss the bigger picture. Here's a bold, practical approach that emphasizes interpretation over mechanistic rules, and cuts through statistical misconceptions like the N>30 rule.
 header:
   image: /assets/images/data_science_8.jpg
   og_image: /assets/images/data_science_8.jpg
@@ -21,15 +19,10 @@ keywords:
 - Data Science
 - Hypothesis Testing
 - Nonparametric Tests
-seo_description: A bold take on statistical test selection that challenges common
-  frameworks. Move beyond basic diagrams and N>30 pseudorules, and learn how to focus
-  on meaningful interpretation and robust testing strategies.
+seo_description: A bold take on statistical test selection that challenges common frameworks. Move beyond basic diagrams and N>30 pseudorules, and learn how to focus on meaningful interpretation and robust testing strategies.
 seo_title: 'Rethinking Statistical Test Selection: A Bold Approach to Choosing Tests'
 seo_type: article
-summary: This article critiques popular frameworks for selecting statistical tests,
-  offering a robust, more flexible alternative that emphasizes interpretation and
-  realistic outcomes over pseudorules and data transformations. Learn why techniques
-  like Welch’s t-test and permutation tests are better than many 'classics'.
+summary: This article critiques popular frameworks for selecting statistical tests, offering a robust, more flexible alternative that emphasizes interpretation and realistic outcomes over pseudorules and data transformations. Learn why techniques like Welch’s t-test and permutation tests are better than many 'classics'.
 tags:
 - Statistical Analysis
 - Data Science

_posts/2020-05-01-shapiro_wilk_test.md

@@ -4,9 +4,7 @@ categories:
 - Statistics
 classes: wide
 date: '2020-05-01'
-excerpt: Learn about the Shapiro-Wilk and Anderson-Darling tests for normality, their
-  differences, and how they guide decisions between parametric and non-parametric
-  statistical methods.
+excerpt: Learn about the Shapiro-Wilk and Anderson-Darling tests for normality, their differences, and how they guide decisions between parametric and non-parametric statistical methods.
 header:
   image: /assets/images/data_science_3.jpg
   og_image: /assets/images/data_science_8.jpg
@@ -20,14 +18,10 @@ keywords:
 - Shapiro-wilk test
 - Normality test
 - Parametric methods
-seo_description: Explore the differences between the Shapiro-Wilk and Anderson-Darling
-  tests for checking normality in data. Learn when to use each test and how to interpret
-  the results.
+seo_description: Explore the differences between the Shapiro-Wilk and Anderson-Darling tests for checking normality in data. Learn when to use each test and how to interpret the results.
 seo_title: 'Shapiro-Wilk Test vs. Anderson-Darling Test: Normality Tests Explained'
 seo_type: article
-summary: This article explores two common normality tests—the Shapiro-Wilk test and
-  the Anderson-Darling test—discussing their differences, when to use each, and how
-  to interpret their results to determine the appropriate statistical method.
+summary: This article explores two common normality tests—the Shapiro-Wilk test and the Anderson-Darling test—discussing their differences, when to use each, and how to interpret their results to determine the appropriate statistical method.
 tags:
 - Anderson-darling test
 - Normality tests

_posts/2020-07-01-cocharan_q_test.md

@@ -4,9 +4,7 @@ categories:
 - Statistics
 classes: wide
 date: '2020-07-01'
-excerpt: Understand Cochran’s Q test, a non-parametric test for comparing proportions
-  across related groups, and its applications in binary data and its connection to
-  McNemar's test.
+excerpt: Understand Cochran’s Q test, a non-parametric test for comparing proportions across related groups, and its applications in binary data and its connection to McNemar's test.
 header:
   image: /assets/images/data_science_2.jpg
   og_image: /assets/images/data_science_8.jpg
@@ -21,13 +19,10 @@ keywords:
 - Machine learning
 - Logistic regression
 - Data science
-seo_description: Learn about Cochran’s Q test, its use for comparing proportions across
-  related groups, and its connection with McNemar’s test and logistic regression.
+seo_description: Learn about Cochran’s Q test, its use for comparing proportions across related groups, and its connection with McNemar’s test and logistic regression.
 seo_title: 'Cochran’s Q Test: Comparing Proportions in Related Groups'
 seo_type: article
-summary: This article explores Cochran’s Q test, a non-parametric method for comparing
-  proportions in related groups, particularly in binary data. It also covers the relationship
-  between Cochran's Q, McNemar's test, and logistic regression.
+summary: This article explores Cochran’s Q test, a non-parametric method for comparing proportions in related groups, particularly in binary data. It also covers the relationship between Cochran's Q, McNemar's test, and logistic regression.
 tags:
 - Logistic regression
 - Mcnemar's test

_posts/2021-03-01-polynomial_regression.md

@@ -4,10 +4,7 @@ categories:
 - Machine Learning
 classes: wide
 date: '2021-03-01'
-excerpt: Polynomial regression is a popular extension of linear regression that models
-  nonlinear relationships between the response and explanatory variables. However,
-  despite its name, polynomial regression remains a form of linear regression, as
-  the response variable is still a linear combination of the regression coefficients.
+excerpt: Polynomial regression is a popular extension of linear regression that models nonlinear relationships between the response and explanatory variables. However, despite its name, polynomial regression remains a form of linear regression, as the response variable is still a linear combination of the regression coefficients.
 header:
   image: /assets/images/data_science_4.jpg
   og_image: /assets/images/data_science_2.jpg
@@ -23,16 +20,10 @@ keywords:
 - Machine learning regression
 - Nonlinear regression models
 - Linear regression
-seo_description: Explore why polynomial regression, despite modeling nonlinear relationships
-  between the response and explanatory variables, is mathematically considered a form
-  of linear regression.
+seo_description: Explore why polynomial regression, despite modeling nonlinear relationships between the response and explanatory variables, is mathematically considered a form of linear regression.
 seo_title: 'Polynomial Regression: Why It’s Still Linear Regression'
 seo_type: article
-summary: Polynomial regression models the relationship between the response variable
-  and explanatory variables using a pth-order polynomial. Although this suggests a
-  nonlinear relationship between the response and explanatory variables, it is still
-  linear regression, as the linearity pertains to the relationship between the response
-  variable and the regression coefficients.
+summary: Polynomial regression models the relationship between the response variable and explanatory variables using a pth-order polynomial. Although this suggests a nonlinear relationship between the response and explanatory variables, it is still linear regression, as the linearity pertains to the relationship between the response variable and the regression coefficients.
 tags:
 - Polynomial regression
 - Regression analysis

_posts/2021-04-27-forest_fires_kde.md

@@ -4,9 +4,7 @@ categories:
 - Data Science
 classes: wide
 date: '2021-04-27'
-excerpt: A study using GIS-based techniques for forest fire hotspot identification
-  and analysis, validated with contributory factors like population density, precipitation,
-  elevation, and vegetation cover.
+excerpt: A study using GIS-based techniques for forest fire hotspot identification and analysis, validated with contributory factors like population density, precipitation, elevation, and vegetation cover.
 header:
   image: /assets/images/forest_fire_kde_1.jpg
   og_image: /assets/images/data_science_4.jpg
@@ -28,16 +26,10 @@ keywords:
 - Python
 - bash
 - python
-seo_description: Explore GIS techniques like KDE, Getis-Ord Gi*, and Anselin Local
-  Moran’s I for identifying forest fire hotspots in Southeast Asia, validated by contributory
-  factors.
+seo_description: Explore GIS techniques like KDE, Getis-Ord Gi*, and Anselin Local Moran’s I for identifying forest fire hotspots in Southeast Asia, validated by contributory factors.
 seo_title: GIS-Based Forest Fire Hotspot Identification Using Contributory Factors
 seo_type: article
-summary: This article explores the application of GIS-based techniques, such as Kernel
-  Density Estimation (KDE), Getis-Ord Gi*, and Anselin Local Moran's I, in identifying
-  forest fire hotspots. The study focuses on Belait District, Brunei Darussalam, and
-  validates hotspot results using contributory factors like population density, precipitation,
-  elevation, and vegetation cover.
+summary: This article explores the application of GIS-based techniques, such as Kernel Density Estimation (KDE), Getis-Ord Gi*, and Anselin Local Moran's I, in identifying forest fire hotspots. The study focuses on Belait District, Brunei Darussalam, and validates hotspot results using contributory factors like population density, precipitation, elevation, and vegetation cover.
 tags:
 - Anselin local moran’s i
 - Gis
@@ -48,8 +40,7 @@ tags:
 - Bash
 - bash
 - python
-title: 'GIS-Based Forest Fire Hotspot Identification: A Comprehensive Approach Using
-  Contributory Factors'
+title: 'GIS-Based Forest Fire Hotspot Identification: A Comprehensive Approach Using Contributory Factors'
 ---
 
 ![Example Image](/assets/images/forest_fire_kde_3.png)

_posts/2022-02-17-staff_schedulling.md

@@ -4,8 +4,7 @@ categories:
 - Optimization
 classes: wide
 date: '2022-02-17'
-excerpt: Discover how linear programming and Python's PuLP library can efficiently
-  solve staff scheduling challenges, minimizing costs while meeting operational demands.
+excerpt: Discover how linear programming and Python's PuLP library can efficiently solve staff scheduling challenges, minimizing costs while meeting operational demands.
 header:
   image: /assets/images/data_science_7.jpg
   og_image: /assets/images/data_science_8.jpg
@@ -32,13 +31,10 @@ keywords:
 - Python
 - bash
 - python
-seo_description: Learn how to use linear programming with the PuLP library in Python
-  to optimize staff scheduling and minimize costs in a 24/7 operational environment.
+seo_description: Learn how to use linear programming with the PuLP library in Python to optimize staff scheduling and minimize costs in a 24/7 operational environment.
 seo_title: Staff Scheduling Optimization with Linear Programming in Python
 seo_type: article
-summary: This article discusses using linear programming and Python’s PuLP library
-  to optimize staff scheduling, focusing on cost minimization and meeting operational
-  requirements efficiently.
+summary: This article discusses using linear programming and Python’s PuLP library to optimize staff scheduling, focusing on cost minimization and meeting operational requirements efficiently.
 tags:
 - Linear programming
 - Scheduling

_posts/2022-03-14-levenes_test_vs_bartletts_test_checking_homogeneity_variances.md

@@ -4,9 +4,7 @@ categories:
 - Hypothesis Testing
 classes: wide
 date: '2022-03-14'
-excerpt: Levene's Test and Bartlett's Test are key tools for checking homogeneity
-  of variances in data. Learn when to use each test, based on normality assumptions,
-  and how they relate to tests like ANOVA.
+excerpt: Levene's Test and Bartlett's Test are key tools for checking homogeneity of variances in data. Learn when to use each test, based on normality assumptions, and how they relate to tests like ANOVA.
 header:
   image: /assets/images/data_science_4.jpg
   og_image: /assets/images/data_science_4.jpg
@@ -20,16 +18,10 @@ keywords:
 - Homogeneity of variances
 - Anova
 - Hypothesis testing
-seo_description: This article compares Levene's Test and Bartlett's Test for checking
-  homogeneity of variances, discussing when to use each test based on data normality,
-  and their application in conjunction with ANOVA.
-seo_title: 'Levene''s Test vs. Bartlett’s Test: A Comparison for Testing Homogeneity
-  of Variances'
+seo_description: This article compares Levene's Test and Bartlett's Test for checking homogeneity of variances, discussing when to use each test based on data normality, and their application in conjunction with ANOVA.
+seo_title: 'Levene''s Test vs. Bartlett’s Test: A Comparison for Testing Homogeneity of Variances'
 seo_type: article
-summary: This article provides a detailed comparison between Levene's Test and Bartlett’s
-  Test for assessing the homogeneity of variances in data. It explains the differences
-  in when to use these tests—parametric vs. non-parametric data, normal vs. non-normal
-  data—and their applications alongside statistical tests like ANOVA.
+summary: This article provides a detailed comparison between Levene's Test and Bartlett’s Test for assessing the homogeneity of variances in data. It explains the differences in when to use these tests—parametric vs. non-parametric data, normal vs. non-normal data—and their applications alongside statistical tests like ANOVA.
 tags:
 - Levene's test
 - Bartlett’s test

_posts/2022-12-30-simpsons_paradox.md

@@ -4,19 +4,15 @@ categories:
 - Statistics
 classes: wide
 date: '2022-12-30'
-excerpt: Simpson's Paradox shows how aggregated data can lead to misleading trends.
-  Learn the theory behind this paradox, its practical implications, and how to analyze
-  data rigorously.
+excerpt: Simpson's Paradox shows how aggregated data can lead to misleading trends. Learn the theory behind this paradox, its practical implications, and how to analyze data rigorously.
 header:
   image: /assets/images/data_science_8.jpg
   og_image: /assets/images/data_science_6.jpg
   overlay_image: /assets/images/data_science_8.jpg
   show_overlay_excerpt: false
   teaser: /assets/images/data_science_8.jpg
   twitter_image: /assets/images/data_science_6.jpg
-seo_description: Explore the theoretical foundations of Simpson’s Paradox, its role
-  in data analysis, and how lurking variables and data aggregation lead to contradictory
-  statistical conclusions.
+seo_description: Explore the theoretical foundations of Simpson’s Paradox, its role in data analysis, and how lurking variables and data aggregation lead to contradictory statistical conclusions.
 seo_title: 'Simpson''s Paradox: Theory, Lurking Variables, and Data Aggregation'
 seo_type: article
 tags:

_posts/2023-08-22-paulerdos.md

@@ -4,8 +4,7 @@ categories:
 - Biographies
 classes: wide
 date: '2023-08-22'
-excerpt: Delve into the fascinating life of Paul Erdős, a wandering mathematician
-  whose love for numbers and collaboration reshaped the world of mathematics.
+excerpt: Delve into the fascinating life of Paul Erdős, a wandering mathematician whose love for numbers and collaboration reshaped the world of mathematics.
 header:
   image: /assets/images/data_science_9.jpg
   og_image: /assets/images/data_science_3.jpg
@@ -24,8 +23,7 @@ keywords:
 - Mathematical prodigies
 - Collaborative mathematics
 - Famous mathematicians
-seo_description: Explore the life and legacy of Paul Erdős, a nomadic mathematician
-  who made groundbreaking contributions to number theory and collaborative science.
+seo_description: Explore the life and legacy of Paul Erdős, a nomadic mathematician who made groundbreaking contributions to number theory and collaborative science.
 seo_title: 'Paul Erdős: The Mathematical Prodigy Who Changed Mathematics Forever'
 seo_type: article
 subtitle: A Mathematician for the Ages

_posts/2023-09-26-new_illiteracy_that’s_crippling_our_decisionmaking.md

@@ -4,9 +4,7 @@ categories:
 - Mathematics
 classes: wide
 date: '2023-09-26'
-excerpt: Innumeracy is becoming the new illiteracy, with far-reaching implications
-  for decision-making in various aspects of life. Discover how the inability to understand
-  numbers affects our world and what can be done to address this growing issue.
+excerpt: Innumeracy is becoming the new illiteracy, with far-reaching implications for decision-making in various aspects of life. Discover how the inability to understand numbers affects our world and what can be done to address this growing issue.
 header:
   image: /assets/images/data_science_1.jpg
   og_image: /assets/images/data_science_5.jpg
@@ -25,9 +23,7 @@ keywords:
 - Cognitive bias
 - Public policy
 - Critical thinking
-seo_description: Explore the growing issue of innumeracy—our inability to understand
-  and work with numbers. Learn how this new illiteracy impacts decision-making in
-  society, from corporate boardrooms to public policy.
+seo_description: Explore the growing issue of innumeracy—our inability to understand and work with numbers. Learn how this new illiteracy impacts decision-making in society, from corporate boardrooms to public policy.
 seo_title: 'Innumeracy: The New Illiteracy Crippling Decision-Making'
 seo_type: article
 tags: