Aixys runs the workhorses of applied research — ANOVA, regression, correlation, hypothesis tests, contingency analysis — on tabular data and structured study records. Every result is paired with assumptions checked, residual diagnostics, and a short narrative explaining what the statistic means in the context of the study. Workflows feel like SAS or R for analysts who do not write code daily.
| Source | df | Sum Sq | Mean Sq | F | Pr(>F) |
|---|---|---|---|---|---|
| Cultivar | 2 | 14.82 | 7.41 | 38.21 | <0.001 |
| Fertilizer (N) | 3 | 21.66 | 7.22 | 37.24 | <0.001 |
| Cultivar × N | 6 | 2.41 | 0.402 | 2.07 | 0.072 |
| Block | 3 | 1.18 | 0.393 | 2.03 | 0.121 |
| Residual | 33 | 6.40 | 0.194 | — | — |
| Total | 47 | 46.47 | — | — | — |
Click any card to expand. Every action carries provenance — what data was used, what was decided, when, by whom.
Every test ships with the diagnostics that determine whether the test was the right one — residual normality, variance homogeneity, outlier flags — and the post-hoc grouping that follows.
| Group | n | Mean yield (t/ha) | SE | 95% CI | LSD |
|---|---|---|---|---|---|
| C2 · N3 | 4 | 6.84 | 0.22 | 6.42 – 7.26 | a |
| C2 · N2 | 4 | 6.32 | 0.22 | 5.90 – 6.74 | a b |
| C1 · N3 | 4 | 6.10 | 0.22 | 5.68 – 6.52 | b |
| C3 · N2 | 4 | 5.74 | 0.22 | 5.32 – 6.16 | b c |
| C1 · N1 | 4 | 5.21 | 0.22 | 4.79 – 5.63 | c |
| C3 · N0 | 4 | 4.22 | 0.22 | 3.80 – 4.64 | d |
Three representative scenarios drawn from real research workflows. Click any to expand the follow-up that the team typically runs next.
Industries where this module is most directly relevant. The underlying engine is general — sector templates accelerate the work.
A 30-minute working session with the Aixys studio. Bring a real dataset; leave with an analysis, a plan, and answers to your team's hardest research-ops questions.