Perform regression analysis on your data
testing
Need to analyze data? Let a Llama-3.1 agent do it for you!
Analyze datasets and generate insights
Accepts CSV, XLS, XLSX, JSON, XML, TXT. Try our demo!
Analysed the attached data
Predict crop yields based on weather, soil conditions, and a
Transform data into visual insights
Explore and analyze data with insights and visualizations
Analyze CSV data and generate reports
Analyze data and generate charts by querying
Load and analyze CSV data using Pandas
Conversational AI agent built for Retail Domain
Regression analysis is a statistical method used to establish a mathematical relationship between two or more variables. It helps in understanding how the value of a dependent variable changes when any of the independent variables change. Commonly used in predictive modeling, regression analysis is widely applied in fields like economics, finance, engineering, and social sciences.
• Support for Multiple Data Formats: Easily process CSV and other common data formats. • Linear and Non-Linear Models: Build both simple and complex regression models, including linear, polynomial, and logistic regression. • Statistical Measures: Access key metrics such as R-squared, coefficients, and p-values to evaluate model performance. • Customizable: Adjust variables, add polynomial terms, and apply transformations to refine your analysis. • Data Visualization: Generate plots to visualize relationships and residuals for better interpretation.
What is the difference between linear and logistic regression?
Linear regression is used for predicting continuous outcomes, while logistic regression is used for predicting binary outcomes.
What is R-squared in regression analysis?
R-squared (coefficient of determination) measures the proportion of variance in the dependent variable explained by the model. A higher R-squared indicates a better fit.
How do I improve the accuracy of my regression model?
You can improve accuracy by adding relevant features, handling multicollinearity, removing outliers, and using techniques like regularization or cross-validation.