Statistical Analysis – II

Course details

Building on the concepts from "Statistical Analysis - I", this course delves deeper into the advanced techniques of statistical modeling, multivariate analyses, and non-parametric methods. Students will be equipped to tackle complex data scenarios, understand inter-variable relationships, and make robust, data-driven decisions in multidimensional settings.

Start Date

TBD

Duration

14-15 weeks

Learning Objectives

Enhance skills in advanced statistical techniques.
Understand multivariate relationships and dimensionality issues.
Grasp non-parametric methods for varied data structures.
Implement regression models, deciphering variable impacts.
Contribute effectively to interdisciplinary data projects.

Key Features

In-depth exploration of advanced statistical theories.
Practical sessions to apply multivariate techniques.
Collaborative projects tackling complex, real-world data.
Case studies emphasizing contemporary challenges.
Engaging resources on the LMS for self-paced learning.

Learning path

Unit 1: Regression Analysis (5)
Unit 2: Multivariate Analysis (5)
Unit 3: Advanced Probability and Distributions (4)
Unit 4: Non-parametric Methods (4)
Unit 5: Time Series Analysis (5)
Unit 6: Advanced Statistical Techniques (5)

Topics covered

Simple Linear Regression
Multiple Linear Regression
Assumptions and Diagnostics
Model Selection Methods
Logistic Regression

Introduction to Multivariate Statistics
Principal Component Analysis (PCA)
Factor Analysis
Discriminant Analysis
Cluster Analysis

Joint Distributions
Conditional Expectation and Variance
Moments and Moment Generating Functions
Convergence Concepts

Introduction to Non-parametric Statistics
Wilcoxon Signed-Rank Test
Kruskal-Wallis Test
Spearman Rank Correlation

Introduction to Time Series Data
Stationarity and Differencing
Autocorrelation and Partial Autocorrelation
ARIMA and Seasonal Decomposition
Forecasting Techniques

Bootstrapping and Resampling
Bayesian Statistics and Inference
Survival Analysis
Structural Equation Modeling
Advanced Multilevel Modeling

Grading:

Class Participation: 10%
Bi-weekly Assignments: 30%
Group Analysis Project: 20%
Mid-Term Examination: 20%
Final Examination: 20%

Reading Material: Advanced textbooks detailing statistical analyses, supplemented by seminal and contemporary research articles. Additional curated resources on the LMS.

Assignments: Challenges ranging from theoretical proofs to intricate data analysis, emphasizing practical problem-solving.

Faculty: A seasoned statistician with extensive teaching and practical experience in advanced statistical methodologies.

Teaching Instructors: TBD