Preface

These notes were written for the Econometrics and Business Statistics major in the Bachelor of Business and Commerce undergraduate programme at Monash University Malaysia. The course sits at the intersection of applied statistics, econometrics, and empirical research methods. It is designed for students who are ready to move from describing data to building, estimating, and critically evaluating models that answer real research questions.

Who these notes are for.
The assumed background is introductory statistics and a working familiarity with R and RStudio. If you can run a t-test, interpret a confidence interval, and produce a scatter plot in R, you have everything you need to begin. What you will find here is not a repetition of that material. This course picks up where introductory statistics leaves off and takes you to the frontier of what a well-trained quantitative analyst can do: specifying regression models, estimating them by ordinary least squares, diagnosing violations of the classical assumptions, correcting those violations, and extending the framework to dynamic time series settings.

The course is especially suited to students who intend to undertake a research project, dissertation, or honours thesis, and need a rigorous but accessible grounding in the methods they will actually use. By the end of the twelve chapters you will be capable of reading empirical papers critically, replicating their core results in R, and conducting original quantitative analysis of your own.

What these notes cover.
The twelve chapters follow a deliberate progression. Chapters 1 and 2 establish foundations: what quantitative methods are for, how statistical thinking connects to causal inference, and the distributional and algebraic tools the rest of the course draws on. Chapters 3 through 7 develop regression analysis systematically — the simple linear model, its geometric and algebraic derivation, the Gauss-Markov theorem, hypothesis testing, multiple regression, and extensions to non-linear functional forms, dummy variables, and interaction effects. Chapters 8 through 12 address the real-world complications that arise when classical assumptions fail — heteroskedasticity, serial correlation, distributed lag dynamics, non-stationarity, and unit roots — and the robust inference strategies that modern applied econometrics uses in response.

How to use these notes.
Each chapter begins with a concise list of learning objectives. Theory is presented first, motivated with economic or business applications, and then demonstrated with R code using real datasets. Every output shown in these notes is fully reproducible: run the code yourself, change an input, and observe what changes. That practice — not passive reading — is how quantitative intuition is built.

Tutorials appear throughout each chapter. Solutions are provided in collapsible panels: attempt each question before opening the solution. The tutorials are calibrated to class and examination difficulty, and working through them carefully is the single most effective form of exam preparation.

These notes are a companion to, not a replacement for, the weekly lectures and tutorials. Attend both; the lectures develop intuition and motivation that a written document cannot replicate, and the tutorials give you supervised practice under time pressure.

Software.
All code uses R, run in RStudio. The core packages — tidyverse, broom, lmtest, sandwich, estimatr, dynlm, tseries, and wooldridge — are freely available from CRAN and are standard in applied econometric work. The wooldridge package provides the datasets from Jeffrey Wooldridge’s Introductory Econometrics: A Modern Approach, the primary textbook for this course, so you can cross-reference examples directly.

I hope you find these notes useful — both in QMT 2026 and in the research that follows.

\ Department of Econometrics and Business Statistics\ School of Business, Monash University Malaysia\ 2026