Merge pull request #75 from DiogoRibeiro7/feat/reserve_branche

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_posts/-_ideas/2030-01-01-Article Title Ideas for Statistical Tests.md

@@ -0,0 +1,90 @@
+---
+tags: []
+---
+
+## Article Title Ideas for Statistical Tests
+
+### 1. **"Chi-Square Test: Exploring Categorical Data and Goodness-of-Fit"**
+   - Overview of the Chi-Square test for categorical data.
+   - Discuss goodness-of-fit and independence tests.
+   - Applications in survey data, contingency tables, and genetics.
+
+### 2. **"ANOVA vs. Kruskal-Wallis: Comparing Multiple Groups with Parametric and Non-Parametric Tests"**
+   - A comparison between ANOVA (parametric) and Kruskal-Wallis (non-parametric).
+   - When to use each based on assumptions about normality and homogeneity.
+   - Practical examples in clinical trials and market research.
+
+### 3. **"Paired T-Test vs. Wilcoxon Signed-Rank Test: Dependent Samples Analysis"**
+   - Explanation of the paired t-test and the Wilcoxon signed-rank test for dependent samples.
+   - How to decide between parametric and non-parametric methods.
+   - Use cases in before-and-after studies (e.g., medical treatments).
+
+### 4. **"Cochran's Q Test: Analyzing Related Categorical Variables"**
+   - Introduction to Cochran’s Q test for comparing multiple related samples of categorical data.
+   - Practical examples in medical diagnostics and clinical trials with repeated measures.
+
+### 5. **"Friedman Test vs. Repeated Measures ANOVA: Comparing Multiple Dependent Groups"**
+   - Differences between Friedman Test (non-parametric) and Repeated Measures ANOVA (parametric).
+   - When to use each based on assumptions and data characteristics.
+   - Real-world examples in longitudinal studies and repeated measurements.
+
+### 6. **"Z-Test vs. T-Test: When to Use Large-Sample and Small-Sample Hypothesis Testing"**
+   - A comparison between z-tests and t-tests.
+   - How sample size influences the choice of test and assumptions about population variance.
+   - Examples in hypothesis testing for means in manufacturing and quality control.
+
+### 7. **"McNemar's Test: Assessing Changes in Categorical Data for Paired Samples"**
+   - Overview of McNemar’s test for paired nominal data.
+   - Applications in before-and-after studies, clinical research, and binary outcomes.
+
+### 8. **"F-Test for Variance: Comparing Variability Between Two Populations"**
+   - Explanation of the F-test for comparing variances.
+   - Use cases in quality control, financial modeling, and experimental designs.
+
+### 9. **"Kendall's Tau vs. Spearman's Rank Correlation: Measuring Non-Parametric Correlations"**
+   - A comparison between Kendall's Tau and Spearman’s rank correlation for ordinal data.
+   - Use cases in economics, psychology, and market research where data is not normally distributed.
+
+### 10. **"Likelihood Ratio Test: Comparing Statistical Models for Best Fit"**
+   - Introduction to the Likelihood Ratio Test for model comparison.
+   - Applications in logistic regression, survival analysis, and complex models.
+
+### 11. **"Durbin-Watson Test: Detecting Autocorrelation in Regression Models"**
+   - Understanding the Durbin-Watson test for checking autocorrelation in residuals.
+   - Applications in time-series analysis and econometric modeling.
+
+### 12. **"Brown-Forsythe Test vs. Levene's Test: Robust Alternatives for Testing Homogeneity of Variances"**
+   - A comparison of the Brown-Forsythe and Levene’s tests.
+   - Focus on robust methods for testing homogeneity of variances with unequal distributions.
+
+### 13. **"Granger Causality Test: Assessing Temporal Causal Relationships in Time-Series Data"**
+   - Introduction to the Granger causality test for time-series data.
+   - Applications in economics, climate science, and finance.
+
+### 14. **"Shapiro-Wilk Test vs. Anderson-Darling: Checking for Normality in Small vs. Large Samples"**
+   - Comparing two common tests for normality: Shapiro-Wilk and Anderson-Darling.
+   - How sample size and distribution affect the choice of normality test.
+
+### 15. **"Cox Proportional Hazards Model: A Guide to Survival Analysis in Medical Studies"**
+   - Overview of the Cox proportional hazards model for time-to-event data.
+   - Applications in survival analysis and clinical trial data.
+
+### 16. **"Biserial and Point-Biserial Correlation: Analyzing the Relationship Between Continuous and Binary Variables"**
+   - Explanation of biserial and point-biserial correlation methods.
+   - Practical applications in educational testing, psychology, and medical diagnostics.
+
+### 17. **"Multiple Regression vs. Stepwise Regression: Building the Best Predictive Models"**
+   - Comparing multiple regression and stepwise regression methods.
+   - When to use each for predictive modeling in business analytics and scientific research.
+
+### 18. **"G-Test vs. Chi-Square Test: Modern Alternatives for Testing Categorical Data"**
+   - A comparison between the G-test and Chi-square test for categorical data.
+   - Use cases in genetic studies, market research, and large datasets.
+
+### 19. **"Multivariate Analysis of Variance (MANOVA) vs. ANOVA: When to Analyze Multiple Dependent Variables"**
+   - Differences between MANOVA and ANOVA.
+   - Use cases in experimental designs with multiple outcome variables, such as clinical trials.
+
+### 20. **"Wald Test: Hypothesis Testing in Regression Analysis"**
+   - Overview of the Wald test for hypothesis testing in regression models.
+   - Applications in logistic regression, Poisson regression, and complex models.

_posts/-_ideas/2030-01-01-ideas_statistical_tests.md

@@ -58,15 +58,6 @@ Here are some interesting article ideas centered around statistical tests, desig
    - Discuss the relationship between sample size, effect size, and significance level.
    - Provide an overview of how to perform power analysis in different tests (e.g., t-test, ANOVA).
 
-### 5. **"Mann-Whitney U Test vs. Independent T-Test: Non-Parametric Alternatives"**
-   - Comparison between the parametric t-test and the non-parametric Mann-Whitney U test.
-   - Explain when and why to use the Mann-Whitney U test (e.g., non-normal distributions).
-   - Provide practical examples and real-world applications of each test.
-
-### 6. **"One-Way ANOVA vs. Two-Way ANOVA: When to Use Which"**
-   - Overview of the one-way and two-way ANOVA tests.
-   - Discuss scenarios where two-way ANOVA is preferred (interaction effects between two factors).
-   - Explore how to interpret main effects and interactions in a two-way ANOVA.
 
 ### 7. **"Hypothesis Testing with Small Samples: Exploring the Sign Test"**
    - Discuss hypothesis testing for small sample sizes when parametric assumptions are not met.

_posts/-_ideas/NLP and Data Science Article Topic Ideas.md

@@ -25,17 +25,8 @@ title: 'NLP and Data Science: Article Topic Ideas'
 
 Here are a few topic ideas that combine aspects of both Natural Language Processing (NLP) and Data Science, providing a foundation for in-depth articles:
 
-## 1. An Overview of Natural Language Processing in Data Science
-- How NLP fits into the broader field of data science.
-- Common NLP tasks (text classification, sentiment analysis, etc.).
-- Tools and libraries for NLP (e.g., NLTK, SpaCy, Hugging Face).
-- Applications of NLP in real-world data science projects.
 
-## 2. Text Preprocessing Techniques for NLP in Data Science
-- Tokenization, stemming, and lemmatization.
-- Handling stopwords and text normalization.
-- Techniques for handling misspellings, slang, and abbreviations.
-- Use of regex and advanced text cleaning techniques.
+
 
 ## 3. Sentiment Analysis: Techniques and Applications
 - Overview of sentiment analysis and its significance.

_posts/-_ideas/bi_ds_ml_bs.md

@@ -5,12 +5,6 @@ tags: []
 # Article Ideas on the Relationship Between Business Intelligence, Machine Learning, Data Science, and Business Strategy
 
 
-
-### 2. **"How Data Science is Reshaping Business Strategy in the Age of Machine Learning"**
-   - Explore how data-driven decision-making led by data science and ML is becoming integral to business strategy.
-   - Delve into specific use cases like customer segmentation, churn prediction, and recommendation systems that show the value of integrating data science into strategic planning.
-   - Compare traditional decision-making approaches with data science-enhanced methods.
-
 ### 3. **"From Insight to Action: Leveraging Business Intelligence and Machine Learning for Competitive Advantage"**
    - Discuss the shift from descriptive insights (BI) to predictive and prescriptive analytics (ML and data science) and how it empowers businesses to act proactively.
    - Provide examples of companies using BI dashboards and ML-driven insights to stay ahead in competitive markets.

_posts/2020-01-03-assessing_goodness-of-fit_non-parametric_data.md

@@ -2,8 +2,6 @@
 author_profile: false
 categories:
 - Statistics
-- Data Science
-- Machine Learning
 classes: wide
 date: '2020-01-03'
 excerpt: The Kolmogorov-Smirnov test is a powerful tool for assessing goodness-of-fit in non-parametric data. Learn how it works, how it compares to the Shapiro-Wilk test, and explore real-world applications.

_posts/2020-01-04-multiple_comparisons_problem:_bonferroni_correction_other_solutions.md

@@ -2,8 +2,6 @@
 author_profile: false
 categories:
 - Statistics
-- Data Science
-- Hypothesis Testing
 classes: wide
 date: '2020-01-04'
 excerpt: The multiple comparisons problem arises in hypothesis testing when performing multiple tests increases the likelihood of false positives. Learn about the Bonferroni correction and other solutions to control error rates.