rubiX
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Interactive tools
Paste your data. Get instant results.
Upload or paste a dataset — rubiX generates charts, computes statistics, and produces downloadable reports.
Descriptive
Box plot generator
Visualise spread, median, IQR and outliers from your data.
Paste data
CSV upload
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Descriptive
Bar chart generator
Compare categories visually with labelled, styled bar charts.
Paste data
CSV upload
Open tool
Descriptive
Histogram
Explore frequency distributions and detect skewness instantly.
Paste data
Bin size
Open tool
Statistics
Descriptive stats
Get mean, median, mode, SD, variance, skewness & kurtosis.
x̄ = 24.3 | σ = 4.1
Skew = 0.32 | Kurt = 2.8
Paste data
Report
Open tool
Correlation
Scatter & correlation
Plot two variables and compute Pearson or Spearman correlation.
r = 0.74 | p < 0.001
Type: Pearson
X variable
Y variable
Open tool
Inference
Hypothesis testing
Run t-tests, ANOVA, and chi-square with p-values and decisions.
t = 2.45 | p = 0.018
Result:
Reject H₀
Group A
Group B
Open tool
Regression
Linear regression
Fit OLS models, view coefficients, R², residual plots & diagnostics.
y = 3.2x + 1.4
R² = 0.87 | p < 0.001
Predictors
Outcome
Open tool
Causal
Propensity matching
Run PSM, check balance, visualise love plots, and estimate ATT.
ATT = 4.21 | SE = 0.93
Balance:
Achieved
Treatment
Covariates
Open tool
See all tools
12 more tools available
including ANOVA, PCA,
Logistic Regression & more
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Learning pathway
Statistics roadmap
New to statistics? Follow the guided curriculum — from foundations to causal inference. All topics include R code.
Beginner
Descriptive statistics
7 lessons
Types of data
R
Mean, median & mode
R
Variance & standard deviation
R
Skewness & kurtosis
R
Five-number summary & box plots
R
Viz
Frequency tables & histograms
R
Viz
Review & quiz
Quiz
Probability foundations
5 lessons
Basic probability rules
R
Conditional probability & Bayes' theorem
R
Discrete distributions
R
Normal distribution & empirical rule
R
Viz
Z-scores & standardisation
R
Visualisation with ggplot2
5 lessons
Grammar of graphics
R
Bar, histogram & density plots
R
Viz
Box plots & violin plots
R
Viz
Scatter plots & line graphs
R
Viz
Themes & publication-ready plots
R
Capstone: mtcars
3 lessons
EDA in R
R
Full descriptive report
R
Viz
Mini-project
Project
Intermediate
Sampling & estimation
4 lessons
Sampling methods
R
Central limit theorem
R
Viz
Confidence intervals
R
Bootstrap resampling
R
Hypothesis testing
5 lessons
p-values, Type I & II errors
R
One & two-sample t-tests
R
ANOVA & two-way ANOVA
R
Non-parametric tests
R
Chi-square test
R
Correlation & regression
4 lessons
Pearson & Spearman correlation
R
Simple & multiple regression
R
Regression diagnostics
R
Viz
Model selection: AIC, BIC, R²
R
Advanced
Advanced regression
4 lessons
Logistic regression
R
Multicollinearity & VIF
R
Ridge & LASSO
R
Survival analysis
R
Statistical machine learning
4 lessons
Bias-variance tradeoff
R
Cross-validation
R
Random forests
R
PCA & clustering
R
Viz
Causal
Causal inference foundations
3 lessons
Potential outcomes framework
R
DAGs: confounding & colliders
R
Viz
Randomisation & experiments
R
Propensity score methods
4 lessons
Propensity model & visualisation
R
Viz
Nearest neighbour matching
R
Balance check & love plots
R
Viz
Treatment effect estimation
R
Quasi-experimental designs
4 lessons
Difference-in-differences
R
Viz
Regression discontinuity
R
Viz
Instrumental variables
R
Capstone: policy impact evaluation
Project
Curriculum at a glance
Beginner
20
Intermediate
13
Advanced
8
Causal
11
Total lessons
52
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