Module

Math, Statistics, and Python Starter

A practical bridge into econometrics with beginner-friendly notation, summary statistics, uncertainty, and Python data workflows.

20 lessons4 to 6 hoursFoundational
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Lessons

1Why do we need math, statistics, and Python for econometrics?How do math, statistics, and Python help us answer economic questions with data?Open lesson2Variables, equations, and functionsHow can we write economic ideas in a compact way?Open lesson3Reading simple mathematical notationHow do we read common symbols without getting lost?Open lesson4Summation notationHow do we write 'add all these values' in one symbol?Open lesson5Mean, median, variance, and standard deviationHow can we describe the center and spread of a variable?Open lesson6Covariance and correlationHow do we describe whether two variables move together?Open lesson7Probability basicsHow do we talk about uncertainty in a disciplined way?Open lesson8Random variablesHow can one variable have uncertain possible values?Open lesson9Normal distributionWhy does the bell curve show up so often?Open lesson10Sampling and sample vs. populationWhat is the difference between the data we have and the group we care about?Open lesson11Hypothesis testing intuitionHow do we decide whether a data pattern is surprising?Open lesson12Confidence intervals intuitionHow can we report uncertainty without pretending to know the exact answer?Open lesson13Introduction to PythonWhy is Python useful for learning econometrics?Open lesson14Python variables and basic calculationsHow does Python remember values and calculate with them?Open lesson15Working with pandas DataFramesHow does Python store a dataset like a spreadsheet?Open lesson16Loading CSV data in PythonHow do we bring a dataset into Python?Open lesson17Descriptive statistics in PythonHow can Python quickly summarize a dataset?Open lesson18Creating simple graphs in PythonHow can graphs make data patterns easier to see?Open lesson19Mini Python practice labCan we combine loading, summarizing, and graphing in one short workflow?Open lesson20Module 0 review quizAre you ready to start econometrics with the basic tools in place?Open lesson

Notebook labs

Scored review quiz

Module review

These questions sample ideas from across the module. Answer all questions, submit once, then review the explanations and score.

0/20 answered

Why math, statistics, and Python?

1. A student wants to know whether education is linked to wages. Which workflow best matches the econometric way of thinking?

Variables, equations, and functions

2. In wage = f(education, experience), what is the best interpretation of the function?

Reading mathematical notation

3. In wage_i, what does the subscript i help us track?

Summation notation

4. If a formula uses a summation over i = 1 to n, what is it asking us to do?

Mean, median, variance, and standard deviation

5. A wage variable has a high standard deviation. What does that most directly tell us?

Covariance and correlation

6. A correlation between education and wage is positive. What is the strongest accurate interpretation at this stage?

Probability basics

7. If P(employed) = 0.92, which statement correctly uses the complement rule?

Random variables

8. Before observing a graduate's first wage offer, why can the wage be treated as a random variable?

Normal distribution

9. Why is the normal distribution useful in an introductory econometrics course?

Sampling and sample vs. population

10. A survey of 1,000 workers is used to learn about all workers in a region. What is the population?

Hypothesis testing intuition

11. In a test of whether education is related to wage, what does H0: beta1 = 0 usually represent?

Confidence intervals intuition

12. Why is a confidence interval often more informative than only reporting one estimate?

Introduction to Python

13. Why is reproducible Python code valuable in data analysis?

Python variables and basic calculations

14. If hourly_wage = 20 and hours = 35, what is Python doing when it calculates hourly_wage * hours?

Working with pandas DataFrames

15. In a DataFrame for wage data, what does one row usually represent?

Loading CSV data in Python

16. After running df = pd.read_csv('wage_sample.csv'), what is df?

Descriptive statistics in Python

17. Why should students run df.describe() before estimating a model?

Creating simple graphs in Python

18. What is the main learning value of a scatter plot of education and wage?

Mini Python practice lab

19. Which sequence best reflects a careful first Python workflow?

Module 0 review

20. What is the main purpose of Module 0 in this course?

Choices are shuffled each time the review starts.