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Applied Econometrics: Regression Analysis with Real Data – AI Tutor-Enabled Course Book

Original price was: $29.00.Current price is: $16.00.

AI Tutor-enabled
Self-paced
Intermediate → Advanced

Modules: 10
Study time: About 45 hours
Format: Digital course book (PDF)
Projects: 4 + capstone

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Run, read and defend regressions with confidence, from simple OLS to panel data.

TypeNotes & books

DeliveryInstant download after payment

Instant download after payment
Lifetime access in My Courses
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SKU: CB-ECON Categories: Course Books, Economics Tags: econometrics course, econometrics study guide, OLS, panel data, regression analysis, Stata R Python econometrics, time series

AI Tutor-Enabled Self-Paced Professional Course Book

Run, read and defend regressions with confidence, from simple OLS to panel data.

Applied Econometrics is a self-paced course book for students and analysts who need to estimate and interpret economic relationships with data. You will move from the classical linear regression model to inference, specification problems, instrumental variables, panel data and time series, applying each method in Stata, R or Python.

Is this you?

You can run a regression but struggle to interpret the output
Hypothesis tests and p-values feel mechanical
You are unsure when OLS assumptions fail
Endogeneity and instrumental variables are confusing
Panel and time series data need different tools
Your dissertation needs a defensible empirical chapter

This course book is built to close exactly that gap: clear explanations, worked practice and an AI tutor that guides you instead of just giving answers.

Learn
Practise
Check with your AI Tutor
Build projects
Get assessed

What you’ll learn in 10 practical modules

01 Review of Statistics for EconometricsRefresh the probability and statistics econometrics builds on.

Key concepts

Random variables and distributions
Estimators and sampling distributions
Hypothesis testing

Sample practice

Compute a 95% confidence interval for a mean
Explain bias versus variance

02 Simple Linear RegressionEstimate and interpret a two-variable model.

Key concepts

OLS estimation
Interpretation of slope and intercept
R-squared

Sample practice

Interpret β̂₁ in wage = β₀ + β₁·education
Explain what R² does and does not tell you

03 Multiple RegressionControl for several factors at once.

Key concepts

Ceteris paribus interpretation
Omitted variable bias
Multicollinearity

Sample practice

Predict the bias from omitting ability in a wage equation
Interpret coefficients with a dummy variable

04 Inference in RegressionTest hypotheses about coefficients.

Key concepts

t-tests and confidence intervals
F-tests for joint hypotheses
Robust standard errors

Sample practice

Test H₀: β₁=0 at 5%
Test whether two coefficients are jointly zero

05 Functional Form and SpecificationChoose the right model shape.

Key concepts

Logs, quadratics and interactions
Dummy variables
Specification tests

Sample practice

Interpret a log-log coefficient
Interpret an interaction between female and education

06 Heteroskedasticity and AutocorrelationDetect and fix violated assumptions.

Key concepts

Breusch–Pagan and White tests
Robust and clustered standard errors
Serial correlation

Sample practice

Test for heteroskedasticity in a housing model
Explain when to cluster standard errors

07 Endogeneity and Instrumental VariablesEstimate causal effects when OLS is biased.

Key concepts

Sources of endogeneity
IV and two-stage least squares
Instrument relevance and validity

Sample practice

Explain why distance to college can instrument education
Interpret a first-stage F-statistic

08 Binary Outcome ModelsModel yes/no outcomes.

Key concepts

Linear probability model
Logit and probit
Marginal effects

Sample practice

Interpret a marginal effect in a labour force participation probit
Compare LPM and logit predictions

09 Panel Data MethodsUse data that follows units over time.

Key concepts

Pooled OLS
Fixed effects
Random effects and the Hausman test

Sample practice

Explain what fixed effects remove
Interpret a Hausman test result

10 Time Series EconometricsModel data collected over time.

Key concepts

Stationarity and unit roots
Autoregressive and distributed lag models
Cointegration basics

Sample practice

Interpret an ADF test
Explain spurious regression

Use AI as your personal tutor, not an answer machine

The course book includes ready-to-use prompts for ChatGPT, Copilot, Gemini or any capable AI assistant. Each mode makes the AI teach, guide, check or challenge you, so you build real understanding.

1TEACH ME

Explain [topic] in Applied Econometrics to me step by step at a [beginner / intermediate / advanced] level. Use a simple example first, then a harder one, and ask me a question to check I understood.

2GUIDE ME

I am working on this problem: [paste problem]. Do not give me the answer. Give me one hint at a time and wait for my next step.

3CHECK MY WORK

Here is my solution: [paste working]. Check each step, tell me exactly where any mistake is and why, but let me correct it myself.

4CHALLENGE ME

Give me three exam-style Applied Econometrics questions on [topic], getting harder each time. Wait for my answers, then mark them and explain what I missed.

5HELP ME APPLY IT

Show me how [topic] from Applied Econometrics is used in a real situation, then give me a small task to try it myself.

Verify before you trust

Ask a clear, specific question
Solve it yourself first
Test the answer by substituting or estimating
Check it against the worked examples in the book
Validate with your teacher if the AI and the book disagree

Don’t just study it. Build evidence you can show.

Project 1

Wage Equation Study

Estimate returns to education with controls and interpret the results.

Project 2

Housing Price Model

Build and test a hedonic price regression.

Project 3

Policy Evaluation with IV

Estimate a causal effect with an instrument and discuss validity.

Project 4

Panel Data Investigation

Use fixed effects on country or firm panel data.

Final capstone

Empirical Research Paper

Write a short empirical paper: question, data, model, results, robustness checks and conclusions.

Who this course book is for

Undergraduate economics students
Master’s and PhD students
Students writing a quantitative dissertation
Policy and research analysts
Finance and business analysts
Self-learners in data science

Where these skills lead

Economic research
Policy analysis
Data science
Consulting
Banking and finance
Academia

The course builds skills and evidence; it does not guarantee employment or exam results.

What you receive

10 progressive learning modules
Learning objectives, key concepts and practice problems
Ready-to-use AI Tutor prompts for five learning modes
Practical projects and a capstone
Portfolio guide, assessment rubric and completion checklist
Instant download and lifetime access in My Courses

Format	Digital course book (PDF)
Learning mode	Self-paced + AI Tutor
Level	Intermediate → Advanced
Study time	About 45 hours
Created by	NoteStore Academy
Certificate	Optional project and portfolio assessment
Licence	Personal learning use
Updated	November 2, 2026

Certificate pathway

Want a certificate? Submit your work for optional assessment by NoteStore Academy. Certificates reflect demonstrated work, so they are not issued automatically with purchase.

Module practice work20%

Projects 1–440%

Capstone project30%

Portfolio and reflection10%

Frequently asked questions

Which software does it use?

Examples are explained conceptually and shown with Stata, R or Python commands, so you can use whichever your course requires.

Do I need strong maths?

Basic algebra and introductory statistics are enough. Matrix algebra is optional and explained where it appears.

Will it help with my dissertation?

Yes. The capstone follows the structure of an empirical dissertation chapter.

Is it for beginners?

It suits students who have taken introductory statistics. Module 1 reviews what you need.

How do I get the course book after paying?

Your download link appears on the order confirmation page straight away, is emailed to you, and stays in My Courses in your account.

Which AI assistant can I use?

Any capable assistant works, such as ChatGPT, Microsoft Copilot or Google Gemini. The prompts are written to work with all of them.

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