Banner tables that
trace to the respondents
Weighted crosstabs, nested banners, significance letters, Excel and PowerPoint — for mid-size research agencies comparing Tensr to Displayr and Q, not IBM.
| # | Player | Pos | Age | MP | FG% | TRB | AST | PTS |
|---|---|---|---|---|---|---|---|---|
| 1 | Precious Achiuwa | C | 24 | 20.7 | .503 | 6.6 | 1.0 | 7.6 |
| 2 | Bam Adebayo | C | 26 | 34.0 | .521 | 10.4 | 3.9 | 19.3 |
| 3 | Ochai Agbaji | SG | 23 | 21.0 | .444 | 2.8 | 1.2 | 5.8 |
| 4 | Santi Aldama | PF | 23 | 26.4 | .470 | 5.8 | 2.3 | 10.7 |
| 5 | Nickeil Alexander‑W. | SG | 25 | 28.9 | .461 | 3.4 | 2.9 | 12.4 |
| 6 | Grayson Allen | SG | 28 | 33.5 | .498 | 3.9 | 3.1 | 13.5 |
| 7 | Jarrett Allen | C | 25 | 32.6 | .633 | 10.5 | 2.7 | 16.5 |
| 8 | Jose Alvarado | PG | 25 | 21.7 | .435 | 2.3 | 3.6 | 7.1 |
| 9 | Kyle Anderson | PF | 30 | 22.9 | .461 | 4.3 | 4.2 | 6.4 |
| 10 | Giannis Antetokounmpo | PF | 29 | 35.2 | .611 | 11.5 | 6.5 | 30.4 |
| 11 | OG Anunoby | SF | 26 | 34.1 | .492 | 4.2 | 2.1 | 14.7 |
| 12 | Deni Avdija | SF | 23 | 30.1 | .489 | 7.2 | 3.8 | 14.7 |
| 13 | Deandre Ayton | C | 25 | 32.4 | .570 | 11.1 | 1.6 | 16.7 |
| 14 | Marvin Bagley III | PF | 24 | 21.1 | .586 | 6.3 | 1.0 | 11.7 |
| 15 | LaMelo Ball | PG | 22 | 32.3 | .433 | 5.1 | 8.0 | 23.9 |
| 16 | Paolo Banchero | PF | 21 | 35.0 | .455 | 6.9 | 5.4 | 22.6 |
| 17 | Desmond Bane | SG | 25 | 34.4 | .464 | 4.4 | 5.5 | 24.0 |
| 18 | Scottie Barnes | SF | 22 | 34.9 | .475 | 8.2 | 6.1 | 19.9 |
| 19 | RJ Barrett | SG | 23 | 31.7 | .495 | 5.4 | 3.3 | 21.8 |
| 20 | Nicolas Batum | SF | 35 | 25.5 | .453 | 3.8 | 2.1 | 5.3 |
Tensr finds01 signal in your data, understands02 what's happening, and owns resolution03 from first import to published insight.
| # | Player | Pos | MP | PTS |
|---|---|---|---|---|
| 1 | Bam Adebayo | C | 34.0 | 19.3 |
| 2 | Santi Aldama | PF | 26.4 | 10.7 |
| 3 | Giannis Antetokounmpo | PF | 35.2 | 30.4 |
| 4 | Ochai Agbaji | SG | 21.0 | 5.8 |
| 5 | Precious Achiuwa | C | 20.7 | 7.6 |
Build the banner, not a test menu
Weighted crosstabs and nested banners with significance letters — the book a mid-size agency already delivers, not a list of 80 procedures.
Every number traces to the respondents
Cell counts, weights, and significance sit on the original row identities. When a client asks where a letter came from, you can show them.
| Source | SS | df | F | p |
|---|---|---|---|---|
| Position | 1842.6 | 4 | 9.41 | <.001 |
| Residual | 27340.1 | 500 | — | — |
| R² | 0.442 | — | — | — |
Excel and PowerPoint are the deliverable
Export the same tables you signed off in the grid. Compare our book against the one you already ship — that is the trial.
Every hour spent wrestling with tools is time away from the research that matters. Tensr gives your team one workspace to analyse, interpret, and ship.
Build the banner, not a test menu
Weighted crosstabs and nested banners with significance letters — the book a mid-size agency already delivers.
Banner engine
.sav / .dta / CSV
Rim weighting
Nested banners
Significance letters
Low-base flags
Provenance
| # | Player | Pos | Age | MP | FG% | TRB | AST | PTS |
|---|---|---|---|---|---|---|---|---|
| 1 | Precious Achiuwa | C | 24 | 20.7 | .503 | 6.6 | 1.0 | 7.6 |
| 2 | Bam Adebayo | C | 26 | 34.0 | .521 | 10.4 | 3.9 | 19.3 |
| 3 | Ochai Agbaji | SG | 23 | 21.0 | .444 | 2.8 | 1.2 | 5.8 |
| 4 | Santi Aldama | PF | 23 | 26.4 | .470 | 5.8 | 2.3 | 10.7 |
| 5 | Nickeil Alexander‑W. | SG | 25 | 28.9 | .461 | 3.4 | 2.9 | 12.4 |
| 6 | Grayson Allen | SG | 28 | 33.5 | .498 | 3.9 | 3.1 | 13.5 |
| 7 | Jarrett Allen | C | 25 | 32.6 | .633 | 10.5 | 2.7 | 16.5 |
| 8 | Jose Alvarado | PG | 25 | 21.7 | .435 | 2.3 | 3.6 | 7.1 |
| 9 | Kyle Anderson | PF | 30 | 22.9 | .461 | 4.3 | 4.2 | 6.4 |
| 10 | Giannis Antetokounmpo | PF | 29 | 35.2 | .611 | 11.5 | 6.5 | 30.4 |
| 11 | OG Anunoby | SF | 26 | 34.1 | .492 | 4.2 | 2.1 | 14.7 |
| 12 | Deni Avdija | SF | 23 | 30.1 | .489 | 7.2 | 3.8 | 14.7 |
| 13 | Deandre Ayton | C | 25 | 32.4 | .570 | 11.1 | 1.6 | 16.7 |
| 14 | Marvin Bagley III | PF | 24 | 21.1 | .586 | 6.3 | 1.0 | 11.7 |
| 15 | LaMelo Ball | PG | 22 | 32.3 | .433 | 5.1 | 8.0 | 23.9 |
| 16 | Paolo Banchero | PF | 21 | 35.0 | .455 | 6.9 | 5.4 | 22.6 |
| 17 | Desmond Bane | SG | 25 | 34.4 | .464 | 4.4 | 5.5 | 24.0 |
| 18 | Scottie Barnes | SF | 22 | 34.9 | .475 | 8.2 | 6.1 | 19.9 |
| 19 | RJ Barrett | SG | 23 | 31.7 | .495 | 5.4 | 3.3 | 21.8 |
| 20 | Nicolas Batum | SF | 35 | 25.5 | .453 | 3.8 | 2.1 | 5.3 |
Every number traces to the respondents
Cell counts, weights, and significance sit on the original row identities. When a client asks where a letter came from, you can show them.
Provenance, not a black box
Row-level provenance
Weight vectors
Column letters
Excel export
PowerPoint export
Methodology appendix
Points scored by position
PTS · Pos · one-way ANOVA
| Source | SS | df | MS | F | p | η² |
|---|---|---|---|---|---|---|
| Position | 1842.6 | 4 | 460.7 | 8.41 | <.001 sig | .063 |
| Residual | 27384.1 | 500 | 54.8 | — | — | — |
Scoring differs significantly by position, F(4, 500) = 8.41, p < .001. The effect is small‑to‑moderate (η² = .063).
Ship the book your client already expects
Compare Tensr against Displayr or Q on a live job. Upload your .sav, build one banner, export the deck. That is the first table that matters.
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