feay: new article

Author: DiogoRibeiro7

_posts/2020-01-08-heteroscedascity_statistical_tests.md

@@ -14,16 +14,16 @@ header:
   twitter_image: /assets/images/data_science_4.jpg
 keywords:
 - Econometrics
-- Regression Diagnostics
-- White Test
+- Regression diagnostics
+- White test
 - Heteroscedasticity
-- Breusch-Pagan Test
+- Breusch-pagan test
 seo_description: Learn about heteroscedasticity, the statistical tests to detect it, and steps to take when it is present in regression analysis.
 seo_title: 'Heteroscedasticity: Statistical Tests and What to Do When Detected'
 seo_type: article
 summary: Explore heteroscedasticity in regression analysis, its consequences, how to test for it, and practical solutions for correcting it when detected.
 tags:
-- Regression Analysis
+- Regression analysis
 - Econometrics
 - Heteroscedasticity
 title: 'Heteroscedasticity: Statistical Tests and Solutions'

_posts/2020-02-17-arimax_time_series.md

@@ -13,23 +13,25 @@ header:
   teaser: /assets/images/data_science_4.jpg
   twitter_image: /assets/images/data_science_4.jpg
 keywords:
-- r
-- Statistical Modeling
-- Exogenous Variables
+- R
+- Statistical modeling
+- Exogenous variables
 - Forecasting
-- Time Series
-- ARIMAX
+- Time series
+- Arimax
+- r
 seo_description: Explore the ARIMAX model, a powerful statistical tool for time series forecasting that incorporates exogenous variables. Learn how ARIMAX builds on ARIMA to improve predictive performance.
 seo_title: 'ARIMAX Time Series Model: An In-Depth Guide'
 seo_type: article
 summary: This article explores the ARIMAX time series model, which enhances ARIMA by incorporating external variables. We'll dive into the model's structure, assumptions, applications, and how it compares to ARIMA.
 tags:
+- R
+- Statistical modeling
+- Machine learning
+- Arima
+- Time series forecasting
+- Arimax
 - r
-- Statistical Modeling
-- Machine Learning
-- ARIMA
-- Time Series Forecasting
-- ARIMAX
 title: 'ARIMAX Time Series: Comprehensive Guide'
 ---
 

_posts/2020-03-30-sustainability_analytics_how_data_science_drives_green_innovation.md

@@ -13,21 +13,21 @@ header:
   teaser: /assets/images/data_science_3.jpg
   twitter_image: /assets/images/data_science_3.jpg
 keywords:
-- sustainability analytics
-- data science
-- green innovation
-- resource optimization
-- supply chain efficiency
+- Sustainability analytics
+- Data science
+- Green innovation
+- Resource optimization
+- Supply chain efficiency
 seo_description: This article explores how companies and organizations are using data science to enhance sustainability practices in areas like resource optimization, waste reduction, and energy efficiency.
 seo_title: How Data Science is Driving Green Innovation through Sustainability Analytics
 seo_type: article
 summary: In this article, we explore the role of data science in driving green innovation through sustainability analytics, examining how companies use data to optimize resources, cut waste, and enhance supply chain efficiency.
 tags:
-- Sustainability Analytics
-- Data Science
-- Green Innovation
-- Resource Optimization
-- Supply Chain Efficiency
+- Sustainability analytics
+- Data science
+- Green innovation
+- Resource optimization
+- Supply chain efficiency
 title: 'Sustainability Analytics: How Data Science Drives Green Innovation'
 ---
 

_posts/2020-04-01-the_friedman_test.md

@@ -13,19 +13,19 @@ header:
   teaser: /assets/images/data_science_9.jpg
   twitter_image: /assets/images/data_science_8.jpg
 keywords:
-- Repeated Measures ANOVA
-- Non-Parametric Test
-- Friedman Test
-- Ordinal Data
+- Repeated measures anova
+- Non-parametric test
+- Friedman test
+- Ordinal data
 seo_description: Learn about the Friedman test, its application as a non-parametric alternative to repeated measures ANOVA, and its use with ordinal data or non-normal distributions.
 seo_title: 'The Friedman Test: A Non-Parametric Alternative to Repeated Measures ANOVA'
 seo_type: article
 summary: This article provides an in-depth explanation of the Friedman test, including its use as a non-parametric alternative to repeated measures ANOVA, when to use it, and practical examples in ranking data and repeated measurements.
 tags:
-- Non-Parametric Tests
-- Repeated Measures ANOVA
-- Friedman Test
-- Ordinal Data
+- Non-parametric tests
+- Repeated measures anova
+- Friedman test
+- Ordinal data
 title: 'The Friedman Test: Non-Parametric Alternative to Repeated Measures ANOVA'
 ---
 

_posts/2020-07-01-cocharan_q_test.md

@@ -14,20 +14,20 @@ header:
   twitter_image: /assets/images/data_science_8.jpg
 keywords:
 - Proportions
-- McNemar's Test
-- Cochran's Q Test
-- Machine Learning
-- Logistic Regression
-- Data Science
+- Mcnemar's test
+- Cochran's q test
+- 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_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.
 tags:
-- Logistic Regression
-- McNemar's Test
-- Non-Parametric Tests
-- Cochran's Q Test
+- Logistic regression
+- Mcnemar's test
+- Non-parametric tests
+- Cochran's q test
 title: 'Cochran’s Q Test: Comparing Three or More Related Proportions'
 ---
 

_posts/2020-09-02-log_rank_test_survival_analysis_comparing_survival_curves.md

@@ -13,21 +13,21 @@ header:
   teaser: /assets/images/data_science_7.jpg
   twitter_image: /assets/images/data_science_7.jpg
 keywords:
-- log-rank test
-- survival analysis
-- survival curves
-- Kaplan-Meier curves
-- p-values
+- Log-rank test
+- Survival analysis
+- Survival curves
+- Kaplan-meier curves
+- P-values
 seo_description: This article explores the log-rank test used in survival analysis, its applications in medical studies to compare survival times, and how to interpret survival curves and p-values.
 seo_title: 'Understanding the Log-Rank Test in Survival Analysis: Comparing Survival Curves'
 seo_type: article
 summary: This article provides a comprehensive guide to the log-rank test in survival analysis, focusing on its use in medical studies to compare survival curves between two or more groups. We explain how to interpret Kaplan-Meier curves, p-values from the log-rank test, and real-world applications in clinical trials.
 tags:
-- Log-Rank Test
-- Survival Analysis
-- Medical Statistics
-- Kaplan-Meier Curves
-- P-Values
+- Log-rank test
+- Survival analysis
+- Medical statistics
+- Kaplan-meier curves
+- P-values
 title: 'Log-Rank Test in Survival Analysis: Comparing Survival Curves'
 ---
 

_posts/2020-09-24-demand_forecast_supply_chain.md

@@ -13,20 +13,22 @@ header:
   teaser: /assets/images/data_science_8.jpg
   twitter_image: /assets/images/data_science_7.jpg
 keywords:
-- Supply Chain
-- Repurchase Model
-- Time Series
-- Demand Forecasting
+- Supply chain
+- Repurchase model
+- Time series
+- Demand forecasting
+- Python
 - python
 seo_description: Explore how using customer behavior and predictive models can improve demand forecasting in the supply chain industry, leveraging the BG/NBD model for better accuracy.
 seo_title: Demand Forecasting in Supply Chain Using Customer Behavior
 seo_type: article
 summary: This article explores the use of customer behavior modeling to improve demand forecasting in the supply chain industry. We demonstrate how the BG/NBD model and the Lifetimes Python library are used to predict repurchases and optimize sales predictions over a future period.
 tags:
-- Customer Behavior
+- Customer behavior
+- Python
+- Demand forecasting
+- Repurchase models
 - python
-- Demand Forecasting
-- Repurchase Models
 title: A Predictive Approach for Demand Forecasting in the Supply Chain Using Customer Behavior Modeling
 ---
 

_posts/2020-10-01-time_series_models_predicting_emergency.md

@@ -13,21 +13,21 @@ header:
   teaser: /assets/images/data_science_5.jpg
   twitter_image: /assets/images/data_science_8.jpg
 keywords:
-- Time Series Models
-- Emergency Department Prediction
-- Gradient Boosted Machines
-- Resource Allocation
-- Random Forest
+- Time series models
+- Emergency department prediction
+- Gradient boosted machines
+- Resource allocation
+- Random forest
 seo_description: This study examines machine learning and univariate time series models for predicting emergency department visit volumes, highlighting the superior predictive accuracy of random forest models.
 seo_title: Comparing Machine Learning and Time Series Models for Predicting ED Visit Volumes
 seo_type: article
 summary: A study comparing machine learning models (random forest, GBM) with univariate time series models (ARIMA, ETS, Prophet) for predicting emergency department visits. Results show machine learning models perform better, though not substantially so.
 tags:
-- Emergency Department
-- Time Series Forecasting
-- Machine Learning
-- Gradient Boosted Machines
-- Random Forest
+- Emergency department
+- Time series forecasting
+- Machine learning
+- Gradient boosted machines
+- Random forest
 title: Machine Learning vs. Univariate Time Series Models in Predicting Emergency Department Visit Volumes
 ---
 

_posts/2020-12-01-predictive_maintenance_data_science.md

@@ -14,20 +14,20 @@ header:
   twitter_image: /assets/images/data_science_6.jpg
 keywords:
 - Clustering
-- Predictive Maintenance
-- Anomaly Detection
+- Predictive maintenance
+- Anomaly detection
 - Regression
-- Machine Learning
-- Data Science
+- Machine learning
+- Data science
 seo_description: Explore the impact of data science on predictive maintenance, including techniques like regression, anomaly detection, and clustering for failure forecasting and optimization of maintenance schedules.
 seo_title: 'Data Science in Predictive Maintenance: Techniques and Applications'
 seo_type: article
 summary: This article delves into the role of data science in predictive maintenance (PdM), explaining how methods such as regression, anomaly detection, and clustering help forecast equipment failures, reduce downtime, and optimize maintenance strategies.
 tags:
-- Data Science
-- Machine Learning
-- Predictive Maintenance
-- Industrial Applications
+- Data science
+- Machine learning
+- Predictive maintenance
+- Industrial applications
 title: The Role of Data Science in Predictive Maintenance
 ---
 

_posts/2021-01-01-pde_data_science.md

@@ -13,20 +13,20 @@ header:
   teaser: /assets/images/data_science_7.jpg
   twitter_image: /assets/images/data_science_2.jpg
 keywords:
-- Partial Differential Equations
-- PDEs
-- Data Science
-- Numerical Solutions
-- Physics-Informed Neural Networks
+- Partial differential equations
+- Pdes
+- Data science
+- Numerical solutions
+- Physics-informed neural networks
 seo_description: Explore the importance of Partial Differential Equations (PDEs) in data science, including their role in machine learning, physics-informed models, and numerical methods.
 seo_title: Partial Differential Equations for Data Scientists
 seo_type: article
 summary: This article explores the role of Partial Differential Equations (PDEs) in data science, including their applications in machine learning, finance, image processing, and environmental modeling. It covers basic classifications of PDEs, solution methods, and why data scientists should care about them.
 tags:
-- Physics-Informed Models
-- Machine Learning
-- PDEs
-- Numerical Methods
+- Physics-informed models
+- Machine learning
+- Pdes
+- Numerical methods
 title: Introduction to Partial Differential Equations (PDEs) from a Data Science Perspective
 ---