YA
Yisau Abdulwariz AyomipoInteractive Analytics Portfolio · Decision-ready dashboard
Interactive dashboard · Filter the data, compare performance, inspect evidence and export the filtered view.
PROJECT 02_world_happiness · Geographic + Heatmap

World Happiness

Interactive decision intelligence · built by Yisau Abdulwariz

Yisau Abdulwariz — Data Analyst workspace
ANALYSTYisau AbdulwarizData Analyst · BI · AI Automation

Story

Finding 1. Average happiness is 4.98 across 1,800 observations.

Finding 2. The Advanced development band has the highest average happiness score at 5.54.

Finding 3. The observed happiness range spans 4.23 points, indicating meaningful variation across the population.

Decision flow

Filter → compare KPIs → inspect charts → pivot → export.

Dataset: 1,800 rows · 9 fields.

DECISION BRIEF · PROJECT 02

What the data is telling the decision-maker

Loading a data-grounded finding…

Dataset
1,800 records · 9 fields
Business question
Which regional and socioeconomic factors align with higher reported happiness?
Workflow
Filter → compare KPIs → inspect charts → pivot → export.
KPI wallVisual analysisPivot explorerData tableRecommendations
All records · 0 active filters

Region by Survey Year

How the primary measure is distributed across the leading dimension.

Income Tier mix

Composition of the active population across the comparison dimension.

Survey Year trend

Directional movement of the primary measure across time or sequence.

Survey Year vs Happiness Score

Relationship between the primary measure and a supporting performance metric.

Value distribution

Distribution of the primary measure across the filtered population.

Distribution of the primary analytical measure across the active population.

Category performance

Where the primary measure is concentrated across the second analytical dimension.

Supporting metric efficiency

Average supporting performance by the third analytical dimension.

Performance trajectory

A second perspective on movement over the available time or sequence field.

Interactive Pivot Explorer

Re-group the filtered population by rows, columns, measure and aggregation.

Exploration Table

Top 500 filtered rows · CSV export

DECISION ACTIONS

Recommendations

KPI DRILL-DOWN

Metric

← Back to portfolioBack to top
EXPERIMENT DESIGN LAB

A/B Test Planner & Decision Readout

Estimate a two-sided, two-proportion test sample size before launching a controlled experiment. Planning estimates are not a substitute for a pre-registered analysis plan.

01 / EXPERIMENTATION
Target conversion11.50%
Sample / variant—
Total sample—
Test setup2-sided · 1:1

Assumes independent users, a binary outcome and equal allocation. Adjust for attrition, clustering, multiple comparisons and practical significance before launch.

Experiment design lab

A/B test planner: quantify the decision before rollout

Use this planning calculator to estimate a simple two-arm test. Inputs are illustrative planning assumptions—not observed results or a substitute for a formal power analysis.

Variant A—participants
Variant B—participants
Total—participants
Target rate B—planning target

Planning method: normal approximation for two independent proportions, equal allocation. Validate assumptions with a statistical package before running a consequential experiment; account for clustering, multiple tests, attrition and business guardrails.