[100% Off] Certified Statistical Modelling &Amp; Inference
Statistical Modelling & Inference | Linear Regression, Generalized Linear Models (GLMs), Hypothesis Testing & Practical
What you’ll learn
- Master the assumptions and implementation of Ordinary Least Squares (OLS) and various advanced linear regression models.
- Perform rigorous diagnostic checks on models to identify heteroscedasticity
- multicollinearity
- and influential observations.
- Apply regularization techniques (Ridge
- Lasso
- Elastic Net) effectively to prevent overfitting and improve model robustness.
- Successfully build and interpret Generalized Linear Models (GLMs)
- including logistic regression for binary data and Poisson regression for count data.
- Execute comprehensive hypothesis testing (t-tests
- ANOVA) and correctly interpret p-values and confidence intervals.
Requirements
- Familiarity with foundational probability theory and basic descriptive statistics.
- Basic working knowledge of either Python (Pandas
- NumPy) or R programming language.
- Access to a computer capable of running statistical software (Python/R environments).
Description
This certification course is meticulously designed to transform you into a highly competent statistical modeller ready to tackle complex data science problems. We move beyond simple descriptive statistics to focus on rigorous predictive and inferential frameworks required in professional quantitative roles.
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Comprehensive Modelling Frameworks We start with a deep dive into the theoretical underpinnings of Ordinary Least Squares (OLS) regression, addressing assumptions, diagnostics, and regularization techniques (Ridge, Lasso). The course then expands into Generalized Linear Models (GLMs), providing practical mastery over models essential for non-normal data, such as Logistic Regression for binary outcomes and Poisson Regression for count data.
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Rigorous Inference and Interpretation A statistical model is only as valuable as the conclusions drawn from it. We dedicate significant time to hypothesis testing, confidence interval construction, and rigorous p-value interpretation, ensuring you can communicate model findings clearly and accurately to both technical and non-technical stakeholders. We also introduce the core concepts of Bayesian inference as a modern complementary approach.
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Practical, Certification-Focused Learning The course emphasizes practical application using industry-standard tools (primarily Python/R). Every major section includes real-world case studies and coding assignments designed to simulate professional challenges. Upon completion, you will possess the certified knowledge base required for roles in quantitative analysis, data science, and academic research.
