Description
Introduction of Statistical Data Modeling
Statistical data modeling is a powerful approach to understanding data, making predictions, and uncovering relationships between variables. This course provides a comprehensive introduction to statistical models, covering their application in various fields, including business, healthcare, finance, and more. Participants will gain knowledge in creating, interpreting, and validating statistical models, allowing them to apply these techniques in real-world scenarios.
Prerequisites
- Basic understanding of statistics and probability.
- Familiarity with data handling using software such as Python, R, or Excel.
- Understanding of algebra and calculus (optional but beneficial).
Table of Contents
1: Introduction to Statistical Data Modeling
1.1 What is statistical data modeling? Overview and significance.
1.2 Types of data: Categorical vs. numerical.
1.3 Basics of probability theory and distributions.
1.4 Key concepts: Population, sample, and inference.
1.5 Hands-on: Exploratory data analysis using Python or R.
2: Linear Regression Models
2.1 Simple linear regression: Concept, assumptions, and interpretation.
2.2 Multiple linear regression: Dealing with multiple predictors.
2.3 Model diagnostics: Residuals, R-squared, and goodness-of-fit.
2.4 Hands-on: Building a linear regression model using real-world data.
2.5 Regularization techniques: Ridge and Lasso regression.
3: Generalized Linear Models (GLM)
3.1 Introduction to GLMs: Expanding linear models for diverse data types.
3.2 Logistic regression for binary outcomes.
3.3 Poisson regression for count data.
3.4 Hands-on: Implementing GLMs for classification and count data.
3.5 Interpreting coefficients and understanding model output.
4: Time Series Modeling
4.1 Introduction to time series data and its characteristics.
4.2 Autoregressive models: AR, MA, and ARMA.
4.3 Seasonality and trends: Incorporating them into models.
4.4 Forecasting with ARIMA models.
4.5 Hands-on: Time series analysis and forecasting with Python or R.
5: Advanced Statistical Models and Validation Techniques
5.1 Mixed models and hierarchical models: Applications and interpretations.
5.2 Non-parametric models: Kernel density estimation, spline models.
5.3 Model selection: Cross-validation, AIC, BIC, and other metrics.
5.4 Addressing overfitting and underfitting in models.
5.5 Hands-on: Model validation and performance evaluation.
6: Applications and Case Studies
6.1 Use of statistical models in finance, marketing, healthcare, etc.
6.2 Predictive modeling for classification and regression tasks.
6.3 Ethical considerations in statistical modeling.
6.4 Hands-on: Applying statistical models to solve business problems.
6.5 Final project: Build, validate, and present a statistical model using real data.
To conclude; Each session will include hands-on exercises to allow participants to build, assess, and improve statistical models using real-world data. The course also emphasizes model interpretation and application.
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