DataWiz Offline

Graph-first visual data analysis — fully local
Created by Waleed JavaidImport CSV, TSV, TXT, JSON, or XLSX to begin.

Data Selector

No data loaded.
Local import: CSV, TSV, TXT, JSON, and standard XLSX workbooks (first populated worksheet). Export: visible CSV, model-scored CSV, interactive Excel scenario workbook, DataWiz project JSON, and PNG/JPEG graphs.
Import note: CSV is the most reliable local format. XLSX remains local/offline, but Safari may not support every workbook-compression path; if XLSX does not calculate, save/export the workbook as CSV and re-import.

Visual Summary

Select a variable after importing data.

Distribution

Interpretation

Select a variable to see distribution details.

Relationship Explorer

Automatic ranking uses the absolute Pearson correlation for every complete numeric pair. Click any card: the variables are selected and the chart is drawn automatically.

Import data to discover relationships.
55%

Focused record / facility shared

No record highlight selected.
Focused-record legend details (optional)
Default: compact focus label only. Select variables below only if you want extra values in legends.
Choose two variables or click a discovered relationship above.

Scatterplot and fitted trend

Distribution Lab

Advanced single-variable exploration with selectable graph types. Choose a numeric variable, optionally compare by a category, and switch between histogram, box plot, violin plot, kernel density, and normal-curve views.

Choose a numeric variable to view advanced distribution statistics and graph options.

Focused record / facility shared

No record highlight selected.
Focused-record legend details (optional)
Default: compact focus label only. Select fields below only if you want extra values in legends.

Distribution graph

Statistics and interpretation

This panel will show the mean, median, mode, standard deviation, confidence interval, outliers, and percentiles.

Compare & Trend

Aggregate a selected metric across time, facilities, people, services, units, locations, or any other field in your data. DataWiz chooses a sensible graph by context, while you can override the graph type, benchmark, aggregation, and time interval.

Select a numeric metric and a comparison field. Choose a date column for a trend, or a facility, person, unit, or location field for a benchmark comparison.

Focused record / facility shared

No record highlight selected.

Comparison graph

Benchmarks

A report-aware workspace that activates for facility, entity, service-line, or period benchmark files with volume, observed/expected, rate, index, or significance fields.
Import a report-style dataset to detect an entity, period, denominator, observed/expected measures, indices, and source significance flags.
Confirm or adjust detected report fields
The confirmation layer is optional. It changes only this workspace; the original imported columns remain available in every existing tab.

Facility & Interval Benchmarking

Use multi-interval trends, entity trajectories, selected-interval peer ranking, matched-interval change, or a volume-aware funnel display. Standard tabs remain unchanged.
Benchmark graph layers / colors
Focused record metric trend controls the selected record line/markers. Focused record mean line is the selected record's average across the selected intervals. Peer mean and mode are optional; mode is estimated from binned peer values for continuous metrics. Peer aggregate remains optional and includes the focused record by default.
Interval behavior: Multi-period displays use every interval from Start through Stop. Single interval hides Stop and uses the selected reference interval. Range mean summarizes each record across the selected interval range.
Select a report-style dataset to begin.

Benchmark graph

Storyboard

A separate executive-style benchmark view. It does not change the existing Benchmarks tab. Use it to show distribution, rank, SD/z-score position, trend, and comparison panels on one canvas.

Import a compatible benchmark report to activate this storyboard.
Choose a benchmark metric and build the storyboard.

Benchmark storyboard

Advanced Multivariable Modeler

Build a model from an outcome and selected predictors. This fully local version provides linear regression, binary logistic regression, and Poisson count regression. It automatically creates indicator variables for categories and exposes diagnostics visually.

Import data to activate Modeler. After import, DataWiz will suggest an outcome and predictor set you can revise before fitting.

Additional specialized model families—negative binomial, proportional hazards, multinomial logit, ordered probit, clustered/robust standard errors, and mixed-effects models—are not yet implemented in this offline build. The interface is structured to add them without changing your workflow.

Model Results and Equation

Choose an outcome, a model family, and predictors.

Model Diagnostics

Prediction Studio

Step 1: Fit a model in Modeler. Step 2: choose a baseline case. Step 3: change only the inputs you want to test, then calculate the projected outcome and compare it to similar observed cases.

Fit a model first.

Prediction Evidence

Fit a model and calculate a prediction.

Prediction vs similar observed cases

Saved Scenarios

No saved scenarios.