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Quanti · v2.8

Statistics
with the methods
on the page.

For people who want the answer and the receipts, without leaving the workspace.

Quanti is a statistical analysis environment designed for researchers who write up what they did. Every model output ships with the assumptions, the residuals, and the citation block ready to drop into the methods section, not buried in a separate plot window.

See it in motion Capabilities Talk to us
Overview

Numbers and the argument they make.

Stats software outputs a number and stops. Quanti outputs the number, the model assumptions, the diagnostic plots, and the prose, ready to paste into a methods section. The whole audit trail, on one page.

For analysts

Run the right test.

Pick the question, not the procedure. Quanti looks at your data shape (paired? skewed? heteroscedastic?) and recommends the right test, with the alternatives one click away. Every recommendation cites the rule that surfaced it.

For writers

Methods, not screenshots.

Output is structured. Each result has a publish-ready methods paragraph in APA-7, MLA or AGLC4 style, with figure callouts that auto-update as you edit. Drop it into Word, Pages, Quarto or LaTeX without re-typing.

For supervisors

An audit trail.

Every run is versioned with its inputs, model, seed, and library version. Hand a Quanti project to a co-author or replication reviewer and they reconstruct your result down to the floating-point digit.

The interface

Three views of one regression.

A multiple regression on a longitudinal cost-of-living dataset, seen as a model run, a methods card, and a data inspector.

quanti.app/projects/cost-of-living-2024/regression-housing
Project
cost-of-living-2024.qproj
Variables
housing_share · num
income_log · num
household_size · int
tenure · cat (4)
region · cat (8)
year · int (2019–24)
Models
m1_baseline · OLS
m2_full · OLS
m3_robust · Huber
Data Run Diagnostics Methods History
m2_full ← lm(housing_share ~ income_log + household_size + tenure + region, data = cost)
Coefficients · m2_full 
                     Estimate  Std.Err   t      Pr(>|t|)
(Intercept)            0.5142   0.0218    23.6   < .001 ***
income_log            -0.0867   0.0044   -19.7   < .001 ***
household_size         0.0094   0.0021     4.5   < .001 ***
tenure[mortgage]      -0.0312   0.0061    -5.1   < .001 ***
tenure[private rent]   0.0721   0.0058    12.4   < .001 ***
tenure[social rent]    0.0418   0.0083     5.0   < .001 ***
region[NSW]            0.0186   0.0049     3.8   < .001 ***
region[VIC]            0.0134   0.0051     2.6   * .009
…  (5 more)

R² = 0.412     Adj. R² = 0.408     F(11, 4220) = 268.4   p < .001
RMSE = 0.063   AIC = -11244   BIC = -11168   n = 4,232   ✓ assumptions met
Recommendation

OLS appropriate. linearity ok homoscedastic vif 2.4 income

Residuals vs fitted
Q-Q · normality of residuals
Methods preview

A multiple linear regression… tab over to write.

Assumption checks and diagnostic plots sit beside the result, not in a separate window.
Diagnostics · Quanti
Capabilities

Tests, models, and the methods they ship with.

Forty-two procedures, every one with a methods card, a diagnostic panel, and a citation block. Validated against R’s reference implementation before each release.

01 · Models

OLS, GLM, mixed effects

Linear, logistic, Poisson, ordinal, multinomial, mixed-effects (lme4) and Bayesian (brms) models. One formula bar, the right backend.

02 · Tests

Parametric & non-parametric

t-tests, Mann-Whitney, Wilcoxon, Kruskal-Wallis, χ², Fisher, ANOVA / ANCOVA / MANOVA, repeated-measures, post-hoc with Tukey / Bonferroni / Holm.

03 · Assumptions

Diagnostics, on every run

Q-Q, residuals, Cook’s D, leverage, VIF, Shapiro-Wilk, Breusch-Pagan, Durbin-Watson, surfaced beside the result, not a separate window.

04 · Recommend

Right-test recommender

State the question. Quanti reads your data shape and proposes the test, with cited reasoning, and the alternatives a click away.

05 · Methods

Publish-ready prose

APA-7, MLA, AGLC4, Vancouver. Citations auto-attached; figure callouts auto-numbered. Drop into Word, Pages, Quarto or LaTeX.

06 · Power

A-priori & post-hoc power

G*Power-equivalent calculations: required n, observed power, effect-size confidence intervals. Plotted across a parameter range.

07 · Import

SPSS, Stata, R, CSV

Native readers for .sav, .dta, .rdata, .csv, .xlsx, Parquet, Arrow. Variable labels, value labels and formats round-trip cleanly.

08 · Replicate

Run hashing & replay

Every run is hashed with inputs, seed and library versions. quanti replay run a91f3e2 reconstructs your result exactly.

09 · Open

Open formats throughout

Project files are plain JSON + CSV. No vendor lock-in. Open the project in any text editor; everything important is human-readable.

Workflow

From CSV to methods section.

How a dataset moves from import to a publishable methods paragraph, with the receipts attached.

  1. 01 · Import

    Bring the dataset in.

    Drop your .sav, .dta, .csv or Parquet onto the workspace. Variable types and labels are inferred; fix or override in seconds.

  2. 02 · Ask

    State the question.

    “Does income predict housing share once tenure is held constant?” The recommender proposes the test, cites the rule, and offers the alternatives.

  3. 03 · Run

    Fit the model.

    One formula bar. Diagnostics surface beside the result. Assumption violations surface as warnings, with the robust alternative one click away.

  4. 04 · Write

    Auto-draft the methods.

    The methods card produces APA-7, MLA, AGLC4 or Vancouver prose. Citations attach automatically; figure callouts auto-number.

  5. 05 · Replicate

    Hash, archive, hand off.

    Every run versioned with seed and library hashes. Hand a Quanti project to a co-author and they reconstruct your result to the digit.