Five steps: enter → choose → run → read → use: Every GDC regression question follows the same five-step workflow. Practice this until it is automatic — in an exam you have no time to experiment.
GDC regression workflow (TI-84)
- STAT → EDIT → enter x-values in L1, y-values in L2.
- STAT → CALC → choose the correct regression type (LinReg, QuadReg, ExpReg, PwrReg, SinReg…).
- Confirm lists L1 and L2, then press ENTER.
- Read the model coefficients (a, b, c, r or R²).
- Store to Y1 via "RegEQ" (or type manually) to evaluate and graph.
| TI-84 menu option | Casio equivalent | Model type |
|---|---|---|
| LinReg(ax+b) | Reg → Linear | y = ax + b (linear) |
| QuadReg | Reg → Quadratic | y = ax² + bx + c |
| CubicReg | Reg → Cubic | y = ax³ + bx² + cx + d |
| ExpReg | Reg → Exponential | y = abx |
| PwrReg | Reg → Power | y = axb |
| SinReg | Reg → Sinusoidal | y = a sin(bx + c) + d |
Look at the scatter plot shape first: Before running any regression, plot the data (STAT PLOT on TI; StatGraph on Casio) and look at the shape. The shape tells you which model to try.
| Scatter plot shape | Model to try | Key signal in question |
|---|---|---|
| Straight line | Linear (LinReg) | "constant rate", "per unit", r close to ±1 |
| Single peak or valley | Quadratic (QuadReg) | "maximum", "minimum", projectile |
| Rapid increase, levels off | Exponential (ExpReg) | "percentage growth/decay", "doubles every..." |
| Curve through origin, increasing | Power (PwrReg) | "proportional to square/cube", "directly proportional to" |
| Repeating up-down pattern | Sinusoidal (SinReg) | "tide", "temperature cycle", "Ferris wheel" |
The question often tells you the model type: IB questions usually say "The data can be modelled by y = aebx" or "use a quadratic regression". If the type is given, just run that regression — no need to guess. When not given, use the scatter plot and context clues.
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Copy coefficients exactly — then write the equation: After running regression, the GDC displays coefficients. Write the full equation immediately — do not rely on memory. Round coefficients to 3 significant figures unless the question specifies otherwise.
Xavie collects apartment prices y (millions $) and distances x (km) from city centre. GDC LinReg gives: a = −0.0693, b = 3.10, r = −0.998. Write the regression equation and use it to predict y when x = 15.
Step by step
- Write the linear regression equation.
- Comment on r: r = −0.998 is very close to −1 → strong negative linear correlation. The linear model is appropriate.
- Predict y when x = 15 (within data range, so interpolation).
Final answer
y = −0.0693x + 3.10. Predicted price at 15 km from centre ≈ $2.06 million.
r (Pearson) for linear; R² for all other models: The Pearson correlation coefficient r measures how well a LINEAR model fits. For non-linear models, use R² (coefficient of determination). R² close to 1 means the model explains the data well.
| Statistic | Range | Meaning of value near ±1 or 1 |
|---|---|---|
| r | −1 to +1 | Strong linear relationship. r = +1: perfect positive line. r = −1: perfect negative line. |
| R² | 0 to 1 | Proportion of variation explained by the model. R² = 0.97 → 97% of variation explained. |
What to write when commenting on r: IB mark scheme expects: (1) state the value of r, (2) describe the strength (strong / moderate / weak), (3) state the direction (positive / negative). Example: "r = −0.998 indicates a strong negative linear correlation between distance and apartment price."