Hypothesis Testing in Python (t-test, ANOVA)
Perform t-tests, chi-square tests, and ANOVA using real data to answer business questions — guided by an AI data analyst.
Open analysis →AI Data Analysis Benchmarks for Statistics
We defined practical analysis workflows from multiple domains, then ran them with AI Data Analyst using different LLM engines. On this page you can browse each workflow, open full notebook conversations, and compare model quality in shared score tables. The overall results show that modern LLMs perform very well on structured data analysis tasks.
Statistics Workflow Examples
Browse reproducible AI data analysis workflows in Statistics. Open any example to review prompts, conversation steps, generated code, outputs, and model-level quality scores.
Linear Regression Analysis in Python
Run simple and multiple linear regression, interpret coefficients, check assumptions, and evaluate model fit using statsmodels and scikit-learn.
Open analysis →Model Comparison for Statistics
Compare LLM performance across workflows in this category. Open any score chip to jump directly to that model run and inspect the full conversation and notebook output.
Average score (0-10)
gemma4:31b
10.00
n=2
glm-5.1
10.00
n=2
gpt-5.4
10.00
n=2
gpt-oss:120b
10.00
n=2
qwen3-coder-next
10.00
n=2
qwen3.5:397b
10.00
n=2
gemma4:31b
Average score: 10.00/10
Scored workflows: 2
glm-5.1
Average score: 10.00/10
Scored workflows: 2
gpt-5.4
Average score: 10.00/10
Scored workflows: 2
gpt-oss:120b
Average score: 10.00/10
Scored workflows: 2
qwen3-coder-next
Average score: 10.00/10
Scored workflows: 2
qwen3.5:397b
Average score: 10.00/10
Scored workflows: 2
Detailed Workflow Comparison Table for Statistics
This table compares model scores for each workflow in Statistics. Open any score chip to jump directly to the selected model conversation and review full prompts, code, outputs, and score cards.
What This Benchmark Shows
We tested the same step-by-step data analysis workflows across multiple LLM models and compared results using a shared scoring rubric. In Statistics, most models produce strong notebook outputs with high task completion and useful analytical reasoning. Use these examples as a reference for prompt design, model selection, and workflow quality before running similar analyses on your own data in MLJAR Studio.
Start using AI for Statistics
MLJAR Studio helps you analyze data with AI, run machine learning workflows, and build reproducible notebook-based results on your own computer.
Runs locally • Supports local LLMs