Teaching Experience 1

Teaching Summary

As a Teaching Assistant Lecturer in the Department of Statistics, Allama Iqbal Open University (AIOU), Islamabad, I taught several postgraduate-level statistics courses from July 2016 to January 2018.
My responsibilities included lecturing, conducting tutorials, and organizing workshops that emphasized the application of statistical concepts using R software.

Courses Taught (Total: 225 hours)

1. Statistical Methods (45 hours – Autumn Semester 2016)

This course was designed to cover the foundational concepts of statistics for master’s students.
Topics covered:

• Introduction to statistical methods and branches of statistics
• Types of variables and data types
• Measures of central tendency and dispersion
• Statistical inference and hypothesis testing
• Nonparametric statistics

Tutorials and Workshops:
Practical sessions using R to apply theoretical methods to real data.

2. Regression Analysis (45 hours – Autumn Semester 2016)

This course focused on correlation, causality, and regression modeling.
Key topics included:

• Simple and multiple linear regression
• Model assumptions: linearity, independence, homoscedasticity, and normality
• Least squares estimation and goodness-of-fit measures (R², adjusted R²)
• Hypothesis testing with t-tests and F-tests
• Multicollinearity and residual analysis
• Logistic and nonlinear regression

Tutorials and Workshops:
Students practiced model fitting, diagnostics, and interpretation in R.

3. Nonparametric Statistics (45 hours – Spring Semester 2017)

Introduced the importance of nonparametric techniques when parametric assumptions are violated.
Topics covered:

• Distribution-free and rank-based methods
• Wilcoxon signed-rank test, Mann–Whitney U test, Kruskal–Wallis test
• Goodness-of-fit tests: Kolmogorov–Smirnov test, Anderson–Darling test
• Robust alternatives to traditional parametric approaches

Tutorials and Workshops:
Hands-on sessions analyzing real datasets using nonparametric methods in R.

4. Econometrics (45 hours – Spring Semester 2017)

This course explored both fundamental and advanced econometric concepts.
Topics covered:

• Nature and scope of econometrics
• Statistical inference review
• Simple and multiple regression, dummy variable regression
• Model issues: multicollinearity, heteroscedasticity, autocorrelation
• Model specification and diagnostic testing
• Simultaneous equation models and time series econometrics
• Stationary vs. non-stationary processes
• Panel data and qualitative response regression models

Tutorials and Workshops:
Applied econometric analysis in R, emphasizing model building and diagnostics.

5. Research Methods (45 hours – Autumn Semester 2018)

Provided a comprehensive overview of research methodology and statistical research design.
Topics covered:

• Research framework and problem formulation
• Sampling design, measurement, and scaling
• Data collection methods and data processing
• Parametric and nonparametric hypothesis testing
• Interpretation, report writing, and presentation of findings
• Use of computers in research (Word and LaTeX introduction)

Tutorials and Workshops:
Students conducted surveys, analyzed collected data, and wrote research reports.

Tools and Methods

• Software: R, Word, LaTeX
• Teaching Methods: Lectures, workshops, practical tutorials
• Focus: Application of statistical methods to real-world problems