Click Copy to grab any formula. Layouts are consistent so students don’t get lost.
| Cell | Meaning |
|---|---|
| B2 | x̄ (sample mean) |
| B3 | σ (population SD, known) |
| B4 | n (sample size) |
| B5 | α (two-sided, e.g., 0.05 for 95%) |
Alt: If you prefer confidence in B5 (e.g., 0.95), set =1-$B$5 as α in another cell.
| Cell | Formula |
|---|---|
| B6 | z-critical → =NORM.S.INV(1 - $B$5/2) |
| B7 | SE → =$B$3/SQRT($B$4) |
| B8 | ME → =$B$6*$B$7 |
| B9 | Lower → =$B$2-$B$8 |
| B10 | Upper → =$B$2+$B$8 |
All-in-one ME: =NORM.S.INV(1-$B$5/2)*$B$3/SQRT($B$4)
=ROUNDUP((NORM.S.INV(1-$B$5/2)*$B$3/$B$11)^2,0)
| Cell | Meaning |
|---|---|
| B2 | x̄ (sample mean) |
| B3 | s (sample SD) |
| B4 | n (sample size) |
| B5 | α (two-sided, e.g., 0.05) |
| Cell | Formula |
|---|---|
| B6 | df → =$B$4-1 |
| B7 | t-critical → =T.INV(1-$B$5/2,$B$6) or =T.INV.2T($B$5,$B$6) |
| B8 | SE → =$B$3/SQRT($B$4) |
| B9 | ME → =$B$7*$B$8 |
| B10 | Lower → =$B$2-$B$9 |
| B11 | Upper → =$B$2+$B$9 |
All-in-one ME: =T.INV(1-$B$5/2,$B$4-1)*$B$3/SQRT($B$4)
| Cell | Meaning |
|---|---|
| B2 | s (sample SD) |
| B3 | n (sample size) |
| B4 | α (two-sided, e.g., 0.05) |
| Cell | Formula |
|---|---|
| B5 | df → =$B$3-1 |
| B6 | χ²α/2 (left-tail CDF) → =CHISQ.INV($B$4/2,$B$5) |
| B7 | χ²1−α/2 (left-tail CDF) → =CHISQ.INV(1-$B$4/2,$B$5) |
| B8 | σ² lower → =$B$5*$B$2^2/$B$7 |
| B9 | σ² upper → =$B$5*$B$2^2/$B$6 |
| B10 | σ lower → =SQRT($B$8) |
| B11 | σ upper → =SQRT($B$9) |
Right-tail alternative (equivalent): =CHISQ.INV.RT(1-$B$4/2,$B$5) and =CHISQ.INV.RT($B$4/2,$B$5).
| Cell | Meaning |
|---|---|
| B2 | x̄ (sample mean) |
| B3 | s (sample SD) |
| B4 | n (sample size) |
| B5 | α (two-sided) |
| Cell | Formula |
|---|---|
| B6 | df → =$B$4-1 |
| B7 | t-critical → =T.INV(1-$B$5/2,$B$6) |
| B8 | PI ME → =$B$7*$B$3*SQRT(1+1/$B$4) |
| B9 | Lower → =$B$2-$B$8 |
| B10 | Upper → =$B$2+$B$8 |
PI > CI (wider) because it predicts an individual future value.
| Cell | Meaning |
|---|---|
| B2 | x̄ |
| B3 | s |
| B4 | n |
| B5 | α (confidence, e.g., 0.05) |
| B6 | γ (coverage, e.g., 0.95) |
Exact k depends on n, α, γ (Howe/Young methods). Below is a normal-based quick approximation.
| Cell | Formula |
|---|---|
| B7 |
zγ → =NORM.S.INV((1+$B$6)/2)
|
| B8 |
tα → =T.INV(1-$B$5,$B$4-1)
|
| B9 |
k (approx) → =($B$7 + $B$8*SQRT(1+1/$B$4))
|
| B10 | Lower → =$B$2-$B$9*$B$3 |
| B11 | Upper → =$B$2+$B$9*$B$3 |
Grading focus: understanding **coverage vs confidence** and when to use TI vs CI/PI.