📘 ECON 320 Lab Problem Set 1

• Name : [Your Name]

• Lab Section: [Your Lab Section Here]

Please submit the exercise on Canvas in form of a HTML/PDF file.

---

This assignment builds on:

• Week 1: Descriptive Statistics & Basic Python Coding
• Week 2: Understanding & Presenting Data
• data: J.M. Wooldridge (2019) Introductory Econometrics: A Modern Approach, Cengage Learning, 7th edition.

You will practice summary statistics, basic data cleaning, and choosing appropriate visualizations.

---

🎯 Learning Objectives

By the end of this assignment, you should be able to:

1. Compute and interpret summary statistics.
2. Practice basic data cleaning.
3. Conduct different data visualizations.
4. Reflect on the difference between correlation and causation.

---

📝 Grading (Total = 10 points)

• Q1: Summary statistics — 2 pt
• Q2: Data cleaning — 2 pts
• Q3: Visualizations — 4 pts
• Q5: Critical thinking — 2 pts

---

🔧 Setup

⚠️ Please run the cells in this section before starting the problem set!

---

📦 Download and import required libraries

%pip install wooldridge matplotlib seaborn

import wooldridge as wr
import matplotlib.pyplot as plt
import seaborn as sns

print("✅ Libraries ready.")

---

📥 Load the dataset

• In this problem set, we will use the econmath dataset from the wooldridge package.

• It contains information on students from a large college course in introductory microeconomics.

# Show dataset description (variables, source, sample) for the "econmath" dataset
wr.data("econmath", description=True)

# Load DataFrame for analysis
df_raw = wr.data("econmath")
df_raw.head()

---

❓ Q1 — Summary Statistics (2 pts, 2 sub-questions)

1. Use .describe() to summarize the variables.

# Put your answer here

2. Report mean, std, min, max for score, actmth, and acteng.

# Put your answer here

---

❓ Q2 — Cleaning data (2 pts, 3 sub-questions)

1. Check for missing values.

💡 Hint:

• You can use <dataframe name>.isna().sum() to count missing values in each column.

• In this case, the dataframe name is df_raw.

# Put your answer here

2. For this assignment, assume the missing ACT scores are missing at random.

• Drop all rows with missing ACT scores (actmth or acteng).

• Report how many rows remain after dropping missing values.

# Solution is provided for this step

df_clean = df_raw.dropna(subset=['actmth', 'acteng'])
print(f"Rows remaining after dropping missing ACT scores: {df_clean.shape[0]}")

3. Create a new DataFrame (name it df_analysis) based on df_clean to keep only the following relevant variables for the rest of the assignment:

• score (test score on the introductory microeconomics course)

• actmth (ACT math score)

• acteng (ACT English/verbal score)

# Put your answer here

---

❓ Q3 — Visualizing the Data (6 pts, 5 sub-questions)

Using the Dataframe df_analysis created, generate plots to explore the data:

1. Histogram of score. (1 pt)

# Put your answer here
# 1) Histogram of score

2. Boxplot of score. (1 pt)

# Put your answer here
# 2) Boxplot of score

3. Scatter plot of score vs actmth (with best-fit line). (1 pt)

# Put your answer here
# 3) Scatter: score vs actmth

4. Scatter plot of score vs acteng (with best-fit line). (1 pt)

# Put your answer here
# 4) Scatter: score vs acteng

5. ✍️ Brief interpretation (2–4 sentences): (2 pts, open-ended)

• What do you notice about the distribution of score?
• Are score and ACT scores positively related?
• Any caveats about correlation ≠ causation?

✍️ Put your answer here.

---

End of Problem Set.