Training Course on Advanced Statistical Modeling and Hypothesis Testing

Training Course on Advanced Statistical Modeling & Hypothesis Testing: Regression, ANOVA, Generalized Linear Models

Introduction

Training Course on Advanced Statistical Modeling & Hypothesis Testing offers a deep dive into advanced statistical modeling and rigorous hypothesis testing, equipping participants with the analytical prowess to extract meaningful insights from complex datasets. In today's data-driven landscape, the ability to build robust predictive models, validate assumptions, and make data-informed decisions is paramount for business intelligence and strategic growth. This program moves beyond basic statistics, focusing on practical applications of regression analysis, ANOVA, and generalized linear models (GLMs), essential tools for quantitative analysis and data science excellence.

Participants will gain hands-on experience with cutting-edge statistical software and real-world case studies, fostering a deep understanding of model selection, interpretation, and validation. The curriculum emphasizes problem-solving methodologies and the critical thinking required to navigate diverse data challenges. From understanding causal relationships through multivariate regression to comparing multiple groups with ANOVA and modeling various data types with GLMs, this course is designed to empower professionals to lead analytical initiatives and drive evidence-based strategies within their organizations.

Course Duration

10 days

Course Objectives

1. Develop proficiency in linear, logistic, and nonlinear regression for predictive analytics and forecasting.
2. Apply inferential statistics and various parametric and non-parametric tests to validate assumptions and draw robust conclusions.
3. Utilize Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) for comparing multiple group means and controlling for confounding variables in experimental design.
4. Understand and apply GLMs, including Poisson regression for count data and binomial regression for binary outcomes, extending linear model capabilities.
5. Learn best practices for selecting appropriate statistical models, assessing model fit, and validating results through cross-validation techniques.
6. Identify and address violations of statistical assumptions (e.g., normality, homoscedasticity) to ensure model reliability.
7. Effectively interpret and communicate complex statistical results, including p-values, confidence intervals, and effect sizes, to diverse audiences.
8. Employ advanced data visualization techniques to explore relationships, identify patterns, and present analytical findings clearly.
9. Apply statistical modeling techniques to solve practical problems in finance, healthcare, marketing analytics, and operations research.
10. Gain hands-on experience with industry-standard statistical software (e.g., R, Python, SAS, SPSS, Stata) for data analysis and model building.
11. Develop a foundational understanding of causal inference principles in the context of observational studies and experimental data.
12. Leverage advanced statistical insights to optimize decision-making processes, improve process efficiency, and drive strategic outcomes.
13. Enhance the ability to translate complex statistical analyses into compelling data narratives for stakeholders.

Organizational Benefits

• Employees will be empowered to make more informed and strategic decisions based on rigorous statistical analysis, leading to better outcomes and reduced risk.
• The ability to build and validate advanced statistical models will enable accurate forecasting and predictive analytics, optimizing resource allocation and anticipating market trends.
• By identifying key drivers and relationships within data, organizations can optimize spending, streamline operations, and maximize ROI across various functions.
• Through efficient hypothesis testing and model-driven insights, businesses can identify inefficiencies, mitigate risks, and develop more cost-effective solutions.
• A workforce proficient in advanced statistical modeling gains a significant competitive edge by being able to uncover deeper insights and adapt more quickly to market changes.
• The application of sophisticated statistical methods will bolster R&D efforts, leading to more robust experimental designs and reliable product development.
• By fostering a culture of rigorous data analysis, organizations can spark innovative solutions and discover new opportunities for growth and improvement.
• Advanced statistical techniques enable a more precise assessment and quantification of risks, allowing for proactive mitigation strategies.

Target Audience

1. Data Scientists & Analysts
2. Researchers (Academic & Industry).
3. Business Intelligence Professionals.
4. Marketing & Sales Analysts
5. Financial Analysts & Quants.
6. Healthcare & Pharmaceutical Researchers.
7. Engineers & Quality Control Specialists.
8. Graduate Students & Academics

Course Outline

Module 1: Foundations of Advanced Statistical Inference

• Review of probability, distributions, and descriptive statistics.
• Deep dive into sampling distributions and the Central Limit Theorem.
• Concepts of hypothesis testing: null vs. alternative hypotheses, p-values, Type I and Type II errors.
• Power analysis and sample size determination.
• Case Study: Determining optimal sample size for A/B testing a new website feature to detect a statistically significant conversion rate increase.

Module 2: Simple and Multiple Linear Regression

• Assumptions of linear regression and diagnostics (residuals, leverage, influential points).
• Parameter estimation, interpretation of coefficients, R-squared, and adjusted R-squared.
• Multicollinearity, variable selection techniques (stepwise, AIC/BIC), and model building.
• Interaction terms and dummy variables in regression.
• Case Study: Predicting housing prices based on features like square footage, number of bedrooms, and location. Analyzing the impact of school districts (dummy variable) and the interaction between size and age of the house.

Module 3: Advanced Regression Techniques

• Polynomial regression for modeling curvilinear relationships.
• Ridge and Lasso regression for regularization and feature selection in high-dimensional data.
• Time series regression: modeling trends, seasonality, and autocorrelation.
• Robust regression for handling outliers and non-normal errors.
• Case Study: Forecasting sales for a retail chain over time, incorporating seasonal patterns and promotional efforts, and identifying outliers in sales data.

Module 4: One-Way and Two-Way ANOVA

• Principles of ANOVA: partitioning variance, F-statistic, and post-hoc tests.
• Assumptions of ANOVA and non-parametric alternatives (Kruskal-Wallis).
• Factorial ANOVA: understanding main effects and interaction effects.
• Effect size measures for ANOVA.
• Case Study: Comparing the effectiveness of three different fertilizers on crop yield. Investigating if both fertilizer type and soil type (two-way ANOVA) significantly impact yield.

Module 5: ANCOVA and Repeated Measures ANOVA

• Analysis of Covariance (ANCOVA): adjusting for confounding variables.
• Assumptions and interpretation of ANCOVA.
• Repeated Measures ANOVA for within-subject designs.
• Mixed-effects models for longitudinal data with both fixed and random effects.
• Case Study: Evaluating the impact of a new teaching method on student test scores, while controlling for students' prior academic performance (ANCOVA). Analyzing patient recovery rates measured over several weeks with and without a new drug (repeated measures ANOVA).

Module 6: Introduction to Generalized Linear Models (GLMs)

• Limitations of ordinary least squares (OLS) regression for non-normal data.
• The GLM framework: random component, systematic component, and link function.
• Exponential family of distributions (Normal, Bernoulli, Poisson, Gamma).
• Model fitting and interpretation of GLM coefficients.
• Case Study: Modeling the presence or absence of a disease (binary outcome) in a population based on age and lifestyle factors using a logistic regression.

Module 7: Logistic Regression for Binary Outcomes

• Logit link function and interpretation of odds ratios.
• Model fit assessment for logistic regression (Hosmer-Lemeshow, AUC, McFadden's R-squared).
• Predicting probabilities and classification thresholds.
• Handling imbalanced datasets in logistic regression.
• Case Study: Predicting customer churn based on usage patterns, customer demographics, and service interactions.

Module 8: Poisson and Negative Binomial Regression for Count Data

• Poisson distribution assumptions and the problem of overdispersion.
• Log link function and interpretation of incident rate ratios.
• Negative binomial regression as an alternative for overdispersed count data.
• Zero-inflated models for excess zeros.
• Case Study: Modeling the number of calls received by a customer service center per hour, and accounting for days with unusually high or low call volumes.

Module 9: Model Diagnostics and Validation for GLMs

• Residual analysis for GLMs.
• Deviance and Pearson chi-squared statistics for goodness-of-fit.
• Information criteria (AIC, BIC) for model comparison.
• Cross-validation techniques for model generalization.
• Case Study: Assessing the fit of a logistic regression model predicting loan defaults, and identifying potential issues with model assumptions.

Module 10: Survival Analysis (Introduction)

• Concepts of survival data: censoring, hazard function, survival function.
• Kaplan-Meier survival curves and log-rank test for comparing groups.
• Cox Proportional Hazards model for identifying risk factors.
• Assumptions and interpretation of Cox regression.
• Case Study: Analyzing patient survival times after a specific medical intervention, considering factors like age, disease stage, and comorbidity.

Module 11: Mixed-Effects Models (Hierarchical/Multilevel Models)

• When to use mixed-effects models: correlated data, nested structures, longitudinal data.
• Fixed effects vs. random effects.
• Interpreting random intercepts and random slopes.
• Building and evaluating mixed-effects models in statistical software.
• Case Study: Analyzing student performance across different schools, accounting for the clustering of students within schools and individual student growth over time.

Module 12: Bayesian Statistics (Introduction)

• Fundamentals of Bayesian inference: prior, likelihood, posterior.
• Comparing Bayesian and frequentist approaches to hypothesis testing.
• Markov Chain Monte Carlo (MCMC) methods for parameter estimation.
• Interpreting Bayesian credible intervals.
• Case Study: Estimating the true proportion of defective products in a manufacturing batch using a Bayesian approach, incorporating prior knowledge from historical data.

Module 13: Data Visualization for Statistical Modeling

• Advanced plotting techniques for regression models (e.g., partial residual plots, interaction plots).
• Visualizing ANOVA results (box plots, interaction plots).
• Creating effective graphics for presenting GLM results.
• Communicating statistical findings through compelling data stories.
• Case Study: Developing a series of impactful visualizations to explain the relationship between advertising spend and sales, and the moderating effect of competitor pricing.

Module 14: Practical Implementation with Statistical Software

• Hands-on sessions using R/Python for all covered models.
• Data cleaning, transformation, and preparation for modeling.
• Code walkthroughs and troubleshooting common errors.
• Best practices for reproducible research and statistical programming.
• Case Study: Replicating and extending a published statistical analysis from a research paper using a given dataset and specified software.

Module 15: Advanced Topics & Future Trends

• Brief introduction to Machine Learning algorithms built upon statistical models (e.g., decision trees, random forests, boosting).
• Ethical considerations in statistical modeling and data privacy.
• Big data challenges and scalable statistical methods.
• Emerging trends in statistical learning and causal inference.
• Case Study: Discussing the application of statistical modeling in the context of personalized medicine or algorithmic bias in AI systems.

Training Methodology

This training course employs a blended learning approach designed for maximum engagement and retention:

• Interactive Lectures: Concise and concept-driven presentations with clear explanations of statistical theory.
• Hands-on Software Sessions: Extensive practical exercises using R (or Python, depending on preference) with real-world datasets, ensuring participants gain practical coding skills.
• Case Study Driven Learning: Each module features detailed case studies that illustrate the application of statistical models to solve practical business and research problems.
• Group Discussions & Problem Solving: Collaborative activities encourage participants to discuss challenges, share insights, and develop critical thinking.
• Live Demonstrations: Step-by-step demonstrations of model building, diagnostics, and interpretation using statistical software.
• Q&A Sessions: Dedicated time for addressing participant questions and clarifying complex concepts.
• Post-Training Resources: Access to course materials, code examples, and supplementary readings for continued learning.
• Formative Assessments: Short quizzes or exercises to reinforce learning and gauge understanding throughout the course.

Register as a group from 3 participants for a Discount

Send us an email: [email protected] or call +254724527104 

 

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