Curvilinear Regression – Problems & Solutions
Curvilinear Regression – Problems & Solutions
Curvilinear regression is used when the relationship between variables is not linear (not a straight line).
Common forms:
-
Quadratic (Parabolic):
-
Exponential:
-
Power function:
🔵 Problem 1: Quadratic (Parabolic) Regression
🔹 Given Data
| X | -2 | -1 | 0 | 1 | 2 |
|---|---|---|---|---|---|
| Y | 7 | 2 | 1 | 2 | 7 |
Fit equation:
✅ Step 1: Normal Equations
For quadratic regression:
✅ Step 2: Compute Required Sums
Prepare table:
| X | Y | X² | X³ | X⁴ | XY | X²Y |
|---|---|---|---|---|---|---|
| -2 | 7 | 4 | -8 | 16 | -14 | 28 |
| -1 | 2 | 1 | -1 | 1 | -2 | 2 |
| 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 1 | 2 | 1 | 1 | 1 | 2 | 2 |
| 2 | 7 | 4 | 8 | 16 | 14 | 28 |
Now totals:
✅ Step 3: Substitute into Normal Equations
✅ Step 4: Solve Remaining Equations
From (1):
From (3):
Multiply (1) by 2:
Subtract from (3):
Substitute into (1):
✅ Final Equation
(Since )
🔵 Problem 2: Exponential Regression
🔹 Given
| X | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| Y | 2 | 4 | 8 | 16 |
Fit:
✅ Step 1: Take Logarithm
Let:
Now we fit a straight line:
Compute log values
| X | Y | log Y |
|---|---|---|
| 1 | 2 | 0.301 |
| 2 | 4 | 0.602 |
| 3 | 8 | 0.903 |
| 4 | 16 | 1.204 |
We can see:
Slope
Intercept
So:
✅ Final Equation
🔑 Key Points for Exams
-
Quadratic regression requires 3 normal equations
-
If data is symmetric around zero →
-
Exponential & power models are converted into linear form using logarithm
-
Used when scatter diagram shows curve pattern
good
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