Exam SRM Written Manual
The Actuarial Nexus offers a comprehensive written course. The course is integrated with the practice questions portion of the platform. Direct links to the relevant practice questions are provided at the end of each chapter, and vice versa.
Introduction and Review
- Introduction
- Exam Information
- Source Material
- R Programming
- Assumed Knowledge and Review
- Matrix Algebra
- Maximum Likelihood Estimation
Basics of Statistical Learning
- Input and Output Variables
- Prediction and Inference
- Parametric Methods
- Non-Parametric Methods
- Supervised vs Unsupervised Learning
- Regression vs Classification
- Mean Squared Error
- Bias-Variance Tradeoff
- Data Collection
- Bayes Classifier
- K-Nearest Neighbors
- The Validation Set Approach
- Leave-One-Out Cross-Validation
- K-Fold Cross-Validation
Linear Models
- Simple Linear Regression
- Bias and Standard Error
- Sum of Squares and R-Squared
- The t-Test
- Confidence and Prediction Interval
- Multiple Linear Regression
- The F-Test
- ANOVA
- Subset Selection
- Choosing the Best Model
- Residual Analysis
- Influential Points
- Collinearity
- Heteroscedasticity
- Ridge Regression
- The Lasso
- Binary Dependent Variables
- Logit and Probit Models
- Nominal Dependent Variables
- Ordinal Dependent Variables
- Poisson Regression
- Other Count Models
- Generalized Linear Models
- Estimation in GLMs
Time Series Models
- Introduction to Time Series
- Stationarity
- Forecast Evaluation
- Autoregressive Models
- Smoothing
- Exponential Smoothing
- Seasonal Adjustments
- Unit Root Test
- ARCH and GARCH Models
Decision Trees
- Introduction to Decision Trees
- Regression Trees
- Binary Splitting
- Pruning
- Classification Trees
- Trees vs Linear Models
- Bagging
- Random Forests
- Boosting