1. Healthcare Regression Models 5. Healthcare Simple Linear Regression Notes 9. Extensions of the Linear Model in Healthcare 13. Introduction to Classification Problems in Healthcare Data Analysis 17. Leveraging Discriminant Analysis Bayesian Approach 19. Applying Generalized Linear Models for Financial Data Modeling and Analysis 21. Python K-Nearest Neighbors (KNN) for Predicting Shipping Outcomes in a Warehouse 25. The Bootstrap in Bioinformatics 29. Best Subset Selection for Bioinformatics that Enhances Predictive Models 33. Validation and Cross-Validation in Predictive Modeling for Cellular Chemistry 37. Applying Dimension Reduction Techniques in Bioinformatics 41. Fitting Non-Linear Models Using Polynomials and Step Functions for Financial Data Analysis 45. Exploring Python Polynomial Regressions and Step Functions for Healthcare Data Analysis 2. Healthcare Dimensionality and Structed Models 6. Healthcare Hypothesis Testing and Confidence Intervals Notes 10. Linear Regression in Healthcare Data Analysis 18. Logistic Regression in Healthcare 22. Gaussian Discriminant Analysis for eCommerce, A Focus on Single-Variable Classification 26. Quadratic Discriminant Analysis and Naive Bayes in Financial Data Classification 30. Cross-Validation and the Bootstrap in Bioinformatics 34. More on the Bootstrap in Bioinformatics 38. Stepwise Selection in Finance Modeling 42. Shrinkage Methods and Ridge Regression in Biomedical Research 46. Principal Components Regression and Partial Least Squares in Bioinformatics 44. Advanced Non-Linear Modeling Techniques and Piecewise Polynomials and Splines for Biomedical Engineering 51. Optimizing Warehouse Logistics with Tree-Based Methods for Regression and Classification 52. Boosting for Warehouse Logistics Optimization 56. Support Vector Classifier in Bioinformatics 60. Python ROC Curves in Warehouse Logistics Analysis 50. Exploring Python Splines for Healthcare Data Analysis 54. Boosting eCommerce Sales Predictions with Bagging and Classification Trees 58. Python Tree-Based Methods for Logistic Regression Modeling 62. Comparison with Logistic Regression in Business Analytics 3. Healthcare Model Selection and Bias-Variance Tradeoff Notes 7. Healthcare Multiple Linear Regression Notes. 11. Python Multiple Linear Regression Package 16. Multivariate Logistic Regression for Health Risk Factors in the United States 23. Applying Gaussian Discriminant Analysis to Multi-Variable Classification in eCommerce 27. Python Logistic Regression for Predicting Shipping Outcomes in a Warehouse 31. K-Fold Cross-Validation in Bioinformatics 35. Python Cross-Validation in Bioinformatics 39. Backward Stepwise Selection in Healthcare Modeling 47. Implementing Stepwise Regression in Healthcare Data Analysis: A Practical Guide 49. Smoothing Splines for Biomedical Signal Processing and Analysis 53. Optimizing Warehouse Logistics Using Decision Trees and Cost Complexity Pruning for Accurate Predictions 57. Bayesian Additive Regression Trees for Predicting Prescription Drug Outcomes 61. Feature Expansion and the Support Vector Machine (SVM) in Bioinformatics 48. Neural Networks in Warehouse Logistics Optimization 55. Bagging in eCommerce Sales for Wine and Beer 59. Risk-Adjusted EWMA Control Chart Based on Support Vector Machine with Application to Cardiac Surgery Data 63. Python Support Vector Machines for Predictive Modeling in Customer Retention 4. Classification with K-Nearest Neighbors and Decision Boundaries 8. Important Questions in Regression Analysis for Healthcare 12. Python Interactions, Qualitative Predictors in Healthcare Data Analysis 14. Logistic Regression, Case-Control Sampling and Multiclass Applications 15. Applying Gaussian Discriminant Analysis to Multi-Variable Classification 20. Python Linear Discriminant Analysis for Warehouse Ecommerce 24. Cross-Validation the Wrong and Right Way in Bioinformatics 28. Python Bootstrap in Bioinformatics 32. Estimating Test Error by Understanding the Impact of Biological Factors on Environmental Models 36. Tuning Parameter Selection in Ridge Regression and Lasso for Biomedical Research 40. Applying Ridge Regression and Lasso for Healthcare Data Analysis Using Python 43. Exploring Generalized Additive Models (GAMs) and Local Regression Techniques for Non-Linear Data Analysis
