Lesson 12
Practice regression project
Big question
How can students complete a small regression analysis from question to interpretation?
Lesson progress
Complete checkpoints as you learn
Learning objectives
- Explain practice regression project in plain language.
- Use research question correctly in an interpretation.
- Connect the lesson idea to a formula, graph, Python result, or real example.
Simple explanation
A practice project turns the module into a repeatable workflow: choose a question, define y and x, inspect the data, draw a scatter plot, estimate the simple regression, interpret the slope and R-squared, and write a careful conclusion.
Key terms
- Research question
- A focused question that can be connected to measured variables.
- Reproducible workflow
- A set of steps that another student can rerun and verify.
- Regression report
- A short explanation of the question, data, model, results, and caution.
- Causal limitation
- A reason the regression may describe association but not prove cause and effect.
Project model
This is the fitted equation from the local classroom wage sample.
Example
A student can use WAGE1, CEOSAL2, VOTE1, SLEEP75, BWGHT, 401K, MEAP93, or the local wage sample to practice a one-variable regression.
Interactive visual
Mini project checklist
Students complete the full path from question to chart, regression, and interpretation.
Project steps
Choose one outcome y and one explanatory variable x.
Open the dataset card and inspect the variable definitions.
Make a scatter plot before estimating the model.
Estimate the simple regression in Python.
Interpret the slope, R-squared, and one limitation.
Good starter datasets
Reusable project starter
1import pandas as pd2import statsmodels.api as sm3import matplotlib.pyplot as plt4 5df = pd.read_csv("wage_sample.csv")6y_name = "wage"7x_name = "education"8 9plt.scatter(df[x_name], df[y_name])10plt.xlabel(x_name)11plt.ylabel(y_name)12plt.title(f"{y_name} and {x_name}")13plt.show()14 15y = df[y_name]16X = sm.add_constant(df[x_name])17model = sm.OLS(y, X).fit()18 19print(model.summary())20print(f"Interpretation: one more unit of {x_name} is associated with",21 round(model.params[x_name], 2),22 f"more units of {y_name}, on average in this sample.")Python walkthrough
- 1Students can change y_name and x_name to practice with another two-variable question.
- 2The scatter plot comes before the regression so students see the relationship before summarizing it numerically.
- 3The final print statement nudges students to turn the coefficient into a sentence.
Live notebook
Run this lesson as a notebook
Open an editable notebook cell-by-cell, run Python in the browser, and download the `.ipynb` file for later.
Interactive activity
Dataset explorer
Explore wage_sample.csv
Choose y and x, inspect variables, and generate a simple regression summary.
Variable dictionary
Hourly wage
Years of education
Years of labor market experience
Regression output
Intercept: -10.10
Slope: 2.33
R-squared: 0.949
wage and education
Each dot is one observation. The fitted line summarizes the relationship between education and wage.
Try it yourself
Write one plain-English sentence explaining the main idea from this lesson.
Common mistakes
Check these before you move on.
A regression coefficient describes a pattern unless the assumptions or research design support a causal interpretation.
Quick quiz
Which sequence best matches a careful simple regression project?
Quick quiz
What should a student include in a strong final interpretation?
Key takeaway
A strong regression project is not just output; it is a question, a graph, a fitted model, a careful interpretation, and an honest limitation.