Statistical Analysis

Unlock the power of data with our comprehensive Statistical Analysis tutorials. Whether you’re a beginner or looking to deepen your understanding, our upcoming content will guide you through essential concepts, advanced techniques, and practical implementations using R and Python.

Upcoming Topics

Stay tuned as we cover a wide range of topics, including:

Intro to Statistical Analysis
- What is statistical analysis?
- Importance and applications
- Overview of statistical tools
Descriptive Statistics
- Measures of central tendency and variability
- Visualization techniques with histograms, boxplots, and scatterplots
- Advanced concepts like skewness, kurtosis, and correlation analysis
Inferential Statistics
- Hypothesis testing: t-tests, chi-square tests, ANOVA, MANOVA
- Confidence intervals and p-values
- Post-Hoc Analysis Techniques
Regression Analysis
- Simple and multiple linear regression
- Logistic regression
- Regularization methods: Ridge, Lasso, Elastic Net
- Regression diagnostics and residual analysis
- Nonlinear regression: Polynomial and spline regression
ANOVA and MANOVA
- Analysis of Variance (ANOVA) basics
- One-Way ANOVA with real-life examples
- Two-Way ANOVA in Python and R
- Multivariate Analysis of Variance (MANOVA)
- Post-Hoc Analysis Techniques
Multivariate Analysis
- Principal Component Analysis (PCA)
- Cluster Analysis: K-Means, Hierarchical Clustering, DBSCAN
- Factor Analysis
- Canonical Correlation Analysis
Time Series Analysis
- Components of time series: trend, seasonality, noise
- ARIMA modeling
- Forecasting techniques
Survival Analysis
- Introduction to survival analysis
- Kaplan-Meier estimation
- Cox Proportional Hazards models
Categorical Data Analysis
- Chi-Square Tests for Independence
- Log-Linear Models for Categorical Data
- Testing Proportions: Binomial and Multinomial Tests
Bayesian Analysis
- Basics of Bayesian Statistics
- Markov Chain Monte Carlo (MCMC) Simulations
- Bayesian Approaches to Regression
Experimental Design
- Introduction to Experimental Design
- Power Analysis: Determining Sample Size and Statistical Power
- Randomized Block Design and Latin Squares
Special Topics
- Meta-Analysis: Combining Results from Multiple Studies
- Handling Missing Data with Imputation Techniques
- Bootstrapping: Resampling Methods for Small Datasets
Applications
- Statistical Methods in Healthcare Research
- Business Decision-Making with Statistics
- Applications in Sociology and Psychology
Tools and Software Guides
- Using R for Statistical Analysis
- Using Python for Statistical Analysis
- Comparing R and Python for Various Statistical Tasks

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Citation

BibTeX citation:

@online{kassambara2025,
  author = {Kassambara, Alboukadel},
  title = {Statistical {Analysis:} {Coming} {Soon}},
  date = {2025-01-26},
  url = {https://www.datanovia.com/{MAIN_TOPIC_PARENT}/statistical-analysis/{SUBCATEGORY}/{PAGE_NAME}.html},
  langid = {en}
}

For attribution, please cite this work as:

Kassambara, Alboukadel. 2025. “Statistical Analysis: Coming Soon.” January 26, 2025. https://www.datanovia.com/{MAIN_TOPIC_PARENT}/statistical-analysis/{SUBCATEGORY}/{PAGE_NAME}.html.