Course overview, grading composition Types of Data (cross-sectional, time series, panel) Research questions and hypotheses Hands-on Gretl installation and navigation

Measures of central tendency (mean, median, mode) Measures of dispersion (variance, standard deviation, range) Introduction to probability distributions (Normal, t, Chi square) Data visualization: histograms, boxplots, scatterplots Hands-on Gretl practice: computing descriptive statistics and generating plots

Types of sampling methods: simple random, stratified, cluster sampling Central Limit Theorem and its importance Construction and interpretation of confidence intervals for means and proportions Hands-on Gretl exercises: generating random samples and constructing confidence intervals. Assignment 1 released (20%)

Formulating null and alternative hypotheses One-tailed and two-tailed tests Type I and Type II errors Conducting t-tests and chi-square tests Gretl exercises: performing t-tests and chi-square tests

Introduction to the simple linear regression model Ordinary Least Squares (OLS) estimation Interpretation of slope and intercept coefficients Goodness of fit: R² and Adjusted R² Gretl exercises: running simple regression models Assignment 1 Due (20%)

Hypothesis testing for regressioncoefficients Confidence intervals for coefficients Residual analysis and model diagnostics Gretl exercises: interpreting regression outputs and residuals

The midterm exam will cover all materials from Weeks 1–6

Extending regression to multiple independent variables Interpretation of multiple regression coefficients Detecting and addressing omitted variable bias Gretl exercises: running and interpreting multiple regressions

Choosing the correct functional form (linear, log-linear, etc.) Creating and interpreting interaction terms Consequences of model misspecification Gretl exercises: testing alternative functional forms Assignment 2 Released (20%)

Multicollinearity: detection (VIF) and remedies Heteroskedasticity: testing (Breusch-Pagan) and correction Introduction to serial correlation Gretl exercises: multicollinearity and heteroskedasticity tests

Incorporating qualitative (categorical) variables Creating and interpreting dummy variables Interaction effects involving dummy variables Gretl exercises: building models with dummy variables Assignment 2 Due (20%)

Understanding time series characteristics: trend, seasonality, stationarity Introduction to autocorrelation Basics of ARIMA modeling (conceptual overview) Gretl exercises: plotting and analyzing time series data

Reserved week to finish any incomplete topics Comprehensive course revision session Addressing student questions and clarifying key concepts Interactive Q&A and revision exercises Mock test questions practice

The final exam will cover all materials from Weeks 1–14