Statistics Tutorials : Beginner to Advanced

The following links describe a set of data science tutorials which are useful for learning basic, intermediate, advanced Statistics and machine learning algorithms. It covers some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, logistic regression, classification, market basket analysis, random forest, ensemble technique, clustering, and more. It covers predictive modeling with SAS and data science with R tutorials.

Statistics Tutorials
The following is a list of statistics tutorials which are ideal for both beginners and advanced analytics professionals. It's a step by step guide to learn statistics with popular statistical tools such as SAS and R. It would give you an idea how these algorithms works in background and how to perform these statistical techniques with SAS and R. It includes both theoretical as well as technical explanation.
  1. Basic Statistics: Types of Variables
  2. Descriptive Statistics
  3. Significance Testing: Independent T-Test
  4. Linear Regression with R
  5. Logistic Regression with R
  6. Logistic Regression with SAS
  7. Cluster Analysis using SAS
  8. Cluster Analysis with R
  9. Validate Cluster Analysis
  10. Market Basket Analysis with R
  11. Principal Component Analysis with SAS
  12. Decision Tree on Credit Data with R
  13. Variable Selection / Reduction with R
  14. Selecting the Best Linear Regression Model
  15. Ridge Regression with SAS
  16. Mixed Regression Simplified
  17. Time Series Forecasting : ARIMA
  18. Random Forest with R
  19. Variable Clustering (PROC VARCLUS)
  20. Detecting Multicollinearity in Categorical Variables
  21. Detecting Non-Linear and Non-Monotonic Relationship
  22. Model Performance in Logistic Regression
  23. Model Validation in Logistic Regression
  24. Model Monitoring in Logistic Regression
  25. Bootstrapping Logistic Regression
  26. Effect of Oversampling for Rare Events
  27. Weight of Evidence (WOE) and Information Value (IV)
  28. Difference between linear regression and logistic regression
  29. Checking Assumptions of Multiple Linear Regression
  30. Homoscedasticity Explained
  31. Detecting and correcting multicollinearity problem
  32. Detecting and solving outlier problem
  33. Difference between R-Squared and Adjusted R-Squared
  34. Standardized vs Unstandardized Coefficients
  35. Difference between CHAID and CART
  36. Relative Importance Analysis with SPSS
  37. Variable Selection - Wald Chi Square Analysis
  38. Understanding Concordant, Discordant and Tied Pairs
  39. Chi-Square : Variable Reduction Technique
  40. Modeling Myth: General linear model and generalized linear model mean the same thing


Text Mining with R
It includes fundamentals of text mining with practical case studies. It also covers how to visualize results of text mining. The popular techniques of text mining are also described in the following articles.
  1. Text Mining Basics
  2. Creating WordCloud with R
  3. Creating WordCloud by Demographic
  4. Twitter Analytics with R


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