Lesson 5
What does beta1 mean?
Big question
How do we turn the slope coefficient into an economic sentence?
Lesson progress
Complete checkpoints as you learn
Learning objectives
- Explain what does beta1 mean? in plain language.
- Use beta1 correctly in an interpretation.
- Connect the lesson idea to a formula, graph, Python result, or real example.
Simple explanation
The slope tells how much the predicted outcome changes when the explanatory variable increases by one unit. The units matter: if y is dollars per hour and x is years of education, the slope is dollars per hour per additional year.
Key terms
- beta1
- The population slope in the simple regression model.
- Slope estimate
- The sample estimate of beta1, usually written beta1-hat.
- Unit change
- A one-unit increase in the explanatory variable.
- Coefficient
- An estimated number multiplying a variable in a regression equation.
Estimated slope
The numerator measures how x and y move together. The denominator scales by variation in x.
Example
If the estimated slope is 2.33, a worker with one more year of education has a predicted wage about $2.33 higher in this sample.
Interactive visual
Slope as a one-unit change
Move the slope and watch the predicted wage change for one more year of education.
Adjust the slope
Moving from 16 to 17 years of education changes predicted wage from $27.18 to $29.51.
Interactive activity
Slope and intercept diagram
The intercept anchors the line. The slope controls how steeply predicted y changes as x rises.
Coefficient interpreter
For wage = 1.20 + 0.60 education, what does 0.60 mean?
Prediction calculator
Use wage-hat = 1.20 + 0.60 education.
Predicted wage = 1.20 + 0.60(12) = 8.40
Try it yourself
Write one plain-English sentence explaining the main idea from this lesson.
Common mistakes
Check these before you move on.
Always say one more unit of x is associated with how many more units of y.
Quick quiz
How should a slope of 2.33 be interpreted when y is hourly wage and x is years of education?
Quick quiz
What should always appear in a clear coefficient interpretation?
Key takeaway
The slope is the heart of simple regression interpretation: one more x, how much different is predicted y?