Lesson 4.3

The Leading Term Test

In the long run, only one term matters. How to predict the future of any polynomial.

1

Introduction

Prerequisite Connection: In Lesson 4.2, we saw that odd powers go "Down-Up" or "Up-Down", while even powers go "Up-Up" or "Down-Down". This lesson proves that complex polynomials behave exactly like their simpler power function parents when is large.

Today's Increment: The Leading Term Test allows us to ignore everything except the term with the highest exponent. behaves just like in the long run.

Why This Matters for Calculus: Finding limits at infinity () is literally applying the Leading Term Test. This is the foundation of asymptotic analysis.

2

Explanation of Key Concepts

The 4 Possible End Behaviors

Even Degree (+)
↑ ... ↑
Ends both go UP
Like
Even Degree (-)
↓ ... ↓
Ends both go DOWN
Like
Odd Degree (+)
↓ ... ↑
Down Left, Up Right
Like
Odd Degree (-)
↑ ... ↓
Up Left, Down Right
Like
3

Worked Examples

Level: Basic

Example 1: Identifying the Leader

Determine end behavior for .

Step 1: Find Leading Term
It's .
Step 2: Analyze Degree and Sign
  • Degree 4 is EVEN.
  • Coefficient 3 is POSITIVE.
Answer
Up on both sides ( as ).
Level: Intermediate

Example 2: Hidden Leading Term

Describe end behavior of .

Don't let the order fool you! The leading term is .
It is ODD and NEGATIVE.
Start HIGH (left), End LOW (right).
Level: Advanced

Example 3: Summing Degrees

Find end behavior for .

Do NOT expand the whole thing. Just multiply the leading terms of each factor.
Term 1: -2x
Term 2: (x)^2 → x^2
Term 3: (x) → x
Total:
Analysis
is Even and Negative.
The graph points DOWN on both sides.
4

Common Pitfalls

  • Confusing Coefficient Sign with Exponent Sign:

    is not a polynomial (it's ).
    is a polynomial.
    Only look at the sign of the number in front (coefficient), not the power!

  • Adding Degrees in Non-Factored Form:

    In , the degree is 5.
    In , the degree is 8.
    Know when to pick the winner vs. when to combine them.

5

Real-World Application

Computer Science: Algorithm Complexity

Big O Notation ( vs ) describes how code performs as data sets grow huge.

If an algorithm takes milliseconds, we ignore the 1000 and the 50n. Why? Because when n is a billion, the term is the only one that matters. This is the Leading Term Test in action!

6

Practice Quiz

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