Analyzing data — sound familiar?
Skari solves these problems.
01 · RECOMMEND
Not sure where to start?
Ask the AI which analyses fit your data.
Upload your data. Skari scans every column. Get every method that fits — no guesswork, no wrong tests.
Recommendation Chat
AI Assistant
Dataset overview
Your dataset contains 428 rows and 12 variables (11 numeric, 1 categorical). Tell me about your research question — I'll recommend the best methods strictly from the catalog.
List every analysis I can run with this dataset.
With 428 observations across 12 variables, here's what fits:
I. Exploration
- • Descriptive Statistics — central tendency, dispersion of numeric variables
- • Correlation Analysis — linear relationships between numeric pairs
II. Hypothesis testing
- • Independent T-Test — compare 2 groups on a numeric outcome
- • One-Way ANOVA — compare 3+ groups (e.g., region × satisfaction)
- • Two-Way ANOVA — age_group × region interaction effect
III. Prediction
- • Multiple Regression — predict satisfaction from income, age, region
02 · SET UP
Every setting, explained
Understand each setting — and validate data before you run.
Analysis setup
Every option comes with a plain-language explanation, so you always know what it does and why it matters.
Outcome variable
satisfactionThe variable you want to explain or predict.
Predictors
age_group, region, incomeThe inputs used to explain the outcome.
Confidence level
95%How sure the interval is expected to contain the true value.
Data validation
Before running, Skari validates your data — target, features, sample size, and missing values — to catch problems early.
Outcome defined
satisfactionPredictors selected
3 selectedSample size adequate
n = 428Obs. per predictor
142.7Missing values
2.3% — imputedReady to run — no blocking errors
03 · RESULTS
Results at a glance
The verdict, and the reasoning behind it.
Every report gives you the verdict and the reasoning behind it. Hover any term for an instant definition.
Result Summary
y ~ x1 + x2 + x3 · n = 51
Key Findings
- • Model fit: R² = 0.997 — strong
- • Overall model significant: F(3,47)=5307.5, p < .001
- • Significant predictors: x1, x2, x3 (3/3)
- • Multicollinearity: Max VIF = 6.2 (caution)
Performance Assessment
Explains a strong share of variance (Adj R² = 0.997). Review multicollinearity before interpreting individual coefficients.
Why This Result?
Understanding the regression results
1How the Analysis Works
OLS estimates each predictor’s partial effect while holding the others constant; the F-test checks the overall model.
2What Drives the Result
Each predictor’s unique contribution after accounting for the others — 3 of 3 contribute significantly here.
3How Reliable Is This
Strong overall fit (p < .001); Adjusted R² penalizes complexity. Watch VIF for multicollinearity.
Quality Dimensions
Every factor that matters — fit, significance, effect size, sample adequacy, assumptions — scored Good / Moderate / Weak at a glance, so strengths and weak spots read instantly. The dimensions adapt to each analysis.
Quality Dimensions
Diagnostic guide & alternatives
Plain thresholds for every metric, plus when a different method fits better — the reasoning behind the grade, not just the number. Every analysis gets its own.
Learn More: Diagnostic Guide & Alternative Methods
Reading each metric
What every score in this result means, in plain language — no need to look it up.
Quality thresholds
The strong / acceptable / weak bands for each metric, so you know where your result stands.
Assumptions & reliability
What this method requires, which assumptions your data meets, and when to treat the result with caution.
Alternative methods
When a different test or model would fit your data better — and exactly which one to try.
04 · DETAIL
Publication-grade statistical reports.
APA tables, publication-ready charts.
Everything a journal reviewer needs — formatted and exportable. Effect sizes, confidence intervals, post-hoc tests, all included.
AI interpretation
Plain-language reading of what was tested, what the data shows, and what it means.
AI Interpretation
The model is highly significant and explains a very large share of the variation in the outcome. All predictors contribute meaningfully.
Statistical Findings
R² = 0.997 · Adj R² = 0.997 · F(3,47) = 5307.5 · p < .001
Results Summary
- • Excellent fit — ~99.7% of variance explained.
- • All predictors significant (p < .001).
- • Positive effects — each raises the outcome.
Assumption checks, built in
Before the result is final, Skari runs the standard assumption tests for that method and checks whether each one holds. If one is violated, it doesn't just flag it — it recommends the right alternative analysis and shows the decision path, while still reporting the original result.
Assumption Checks
Assumption Check (automatic)
Equal-variance assumption holds — the standard test above was used, with no manual review needed.
Model Selection Flow
Tables & charts
Key numbers land in clean, formatted tables — each value paired with a plain-language interpretation — and every report renders the charts that fit the analysis automatically: distributions, fit, residuals, comparisons, publication-ready. (Example: a regression fit summary with diagnostic plots.)
Actual vs Predicted
Residuals vs Fitted
Q-Q Plot
Scale-Location
Insight at the bottom of every card
Each result card closes with a short insight — what the numbers mean and what to watch for — so you know how to read the output, not just what it says.
Of 100 subjects, 64 had an event and 36 were censored (still event-free at follow-up). Read the curve as the share staying event-free over time — the flatter it holds, the better the survival.
05 · EXPORT
Take your analysis anywhere
Word report. R script. Python notebook.
Download a publication-ready Word report. Or grab the R and Python code to reproduce it yourself.
Word
.docx
Publication-ready report with APA tables and charts
R
.R
Tidyverse-style script that reproduces the full analysis
Python
.ipynb
Jupyter notebook with pandas/statsmodels equivalents
06 · HELP
Never stuck
AI chat, method guides, and a built-in glossary.
Ask the AI about your specific results. Method guides and a glossary — never leave the report to search elsewhere.
AI Chat
Answers reference your actual analysis — not a generic textbook.
AI Tutor
onlineWhat does p = 0.023 mean for my data?
Your test returned p = 0.023, which is below the conventional 0.05 threshold. The difference between age_group A and B on satisfaction is statistically significant — only 2.3% probability it's due to chance.
Why did it pick Welch's t-test?
Method Guides
When to use each test, how to interpret results, common pitfalls.
Simple Linear Regression Guide
What is Simple Linear Regression?
Models the relationship between one predictor (X) and one outcome (Y) with the best-fitting straight line.
The equation: Y = β₀ + β₁X + ε
β₀ = intercept, β₁ = slope, ε = error
When Should You Use It?
Use when:
- One continuous predictor and one continuous outcome
- You want to predict Y from X
- You suspect a linear relationship
Don't use when:
- The relationship is clearly curved (use polynomial)
- You have multiple predictors (use multiple regression)
- Your outcome is categorical (use logistic)
Glossary
Statistical terms with concise, dictionary-style definitions — p-value, effect size, confidence interval, and more.
Simple Regression Glossary
Definitions of terms used in simple linear regression
R Squared
Proportion of variance in the outcome explained by the predictor. Ranges 0–1; higher means better fit.
Adjusted R Squared
R² adjusted for the number of predictors. More reliable when comparing models.
RMSE
Root Mean Square Error — average prediction error in the outcome’s units. Lower is better.
F Statistic
Tests whether the overall model is statistically significant. Higher means stronger evidence.
P Value
Probability of observing the data if there were no true relationship. Below 0.05 is typically significant.
07 · CATALOG
Every method you'll ever need.
110+ analyses across 9 categories.
From t-tests to time-series forecasting. Assumption checks and interpretation built into every method.
Exploration
5 analyses
- ›Descriptive Statistics
- ›Frequency Analysis
- ›Correlation Analysis
Comparison
16 analyses
- ›T-Tests
- ›ANOVA
- ›Non-parametric Tests
- ›Bayesian Methods
Relationship
16 analyses
- ›Linear & Logistic Regression
- ›GLM
- ›Ridge & Lasso
- ›Robust Regression
Predictive
16 analyses
- ›Decision Tree
- ›Random Forest
- ›Gradient Boosting
- ›XGBoost
Survival Analysis
11 analyses
- ›Kaplan-Meier
- ›Cox Regression
- ›Competing Risks
- ›RMST
Econometrics
17 analyses
- ›Difference-in-Differences
- ›Propensity Score Matching
- ›Instrumental Variables
- ›Panel Fixed/Random Effects
Structural
14 analyses
- ›Factor Analysis
- ›PCA
- ›Reliability
- ›Mediation & Moderation
Clustering
7 analyses
- ›K-Means
- ›Hierarchical
- ›DBSCAN
- ›HDBSCAN
Time Series
20 analyses
- ›Trend Analysis
- ›ARIMA
- ›VAR/VECM
- ›GARCH Family
ALSO IN YOUR DATA LAB
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