Core AI skill - gives the regression line and the correlation coefficient in one step.
In short: Linear regression finds the line of best fit y = ax + b through paired data, along with the correlation coefficient r. You enter the x and y values in two lists and run the linear regression function. Use the line to predict values within the data range, and use r to judge how strong the linear relationship is.
| TI-84 Plus CE | Casio fx-CG50 and fx-CG100 | TI-Nspire CX | |
|---|---|---|---|
| Key sequence | Turn on DiagnosticOn once (2nd → CATALOG) so r appears; then STAT → CALC → 4:LinReg(ax+b), set Xlist and Ylist, Calculate. | Statistics menu → CALC (F2) → REG (F3) → X (linear); r shows automatically. | Calculator page → menu → Statistics → Stat Calculations → Linear Regression (mx+b). |
Tip: r near ±1 means a strong linear fit; near 0 means weak. r² is the proportion of variation explained.
Tip: r near ±1 means a strong linear fit; near 0 means weak. r² is the proportion of variation explained.
Tip: r near ±1 means a strong linear fit; near 0 means weak. r² is the proportion of variation explained.
Here's a real IB-style question that uses exactly this technique.
For the data pairs (1, 3), (2, 5), (3, 4), (4, 6), (5, 8), find the equation of the regression line y = ax + b and the value of r, each to 3 significant figures.
Core AI skill - gives the regression line and the correlation coefficient in one step. A question gives paired data and asks for the equation of the regression line. You need the correlation coefficient r to comment on the strength of a linear relationship.
1. Enter x-values in one list and y-values in another. 2. Turn on DiagnosticOn once (2nd → CATALOG) so r appears; then STAT → CALC → 4:LinReg(ax+b), set Xlist and Ylist, Calculate. 3. Read a (gradient), b (intercept), r (correlation) and r² (coefficient of determination). 4. Use the equation to predict - but only within the data range (interpolation).
1. Enter x-values in one list and y-values in another. 2. Statistics menu → CALC (F2) → REG (F3) → X (linear); r shows automatically. 3. Read a (gradient), b (intercept), r (correlation) and r² (coefficient of determination). 4. Use the equation to predict - but only within the data range (interpolation).
1. Enter x-values in one list and y-values in another. 2. Calculator page → menu → Statistics → Stat Calculations → Linear Regression (mx+b). 3. Read a (gradient), b (intercept), r (correlation) and r² (coefficient of determination). 4. Use the equation to predict - but only within the data range (interpolation).
r near ±1 means a strong linear fit; near 0 means weak. r² is the proportion of variation explained.
In the worked example on this page (3 marks, Paper 2), the marks are: M1: Enter the data as two linked lists and run linear regression; A1: y = 1.1x + 1.9; A1: r = 0.904.
Questions that need this technique link back to this guide. Try one in practice mode, or see every GDC guide.