ASCVIT V1 aims to make data analysis easier by automating statistical calculations, visualizations, and interpretations.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
A Python package for the statistical analysis of A/B tests featuring Student's t-test, Z-test, Bootstrap, and quantile metrics out of the box.
There’s a reason you’re confused
one can never really accept the null hypothesis
The author details their process of calibrating DHT11 sensors for use in measuring temperature and humidity in different locations. They discuss how they obtained the sensors, installed and used a library, collected data, cleaned and normalized the data, and visually inspected and correlated the results. The author concludes that the sensors are close enough in performance for their intended use.
The author details their process of calibrating DHT11 sensors for use in measuring temperature and humidity in different locations. They discuss how they obtained the sensors, installed and used a library, collected data, cleaned and normalized the data, and visually inspected and correlated the results. The author concludes that the sensors are close enough in performance for their intended use.
