The Linear Regression app creates scatterplots, fits simple linear, logistic or exponential regression models, and conducts inference for model parameters (standard errors, confidence intervals, P-values).
The app computes confidence intervals for the mean response and prediction intervals for a future response. The fitted model and the intervals are visualized on the scatterplot, and you can obtain and plot raw and standardized residuals.
You can color points on the scatterplot according to a third quantitative or categorical variable to reveal additional patterns.
New: For data entry, you can enter your own data via the new Data Editor app, import a CSV file, or choose from several pre-loaded example datasets.
New: Fit a multiple linear regression model with both quantitative and categorical predictors. The app
- creates a Scatterplot Matrix to study pairwise relationships
- clearly displays the fitted regression equation, including dummy variables
- presents a table with all regression coefficients
- obtains summary statistics such has R^2-adjusted
- gives fitted values and (standardized) residuals (which you can download)
- lets you make predictions for your own values of the explanatory variables
- constructs a residual plot to check assumptions and for outliers
The app computes confidence intervals for the mean response and prediction intervals for a future response. The fitted model and the intervals are visualized on the scatterplot, and you can obtain and plot raw and standardized residuals.
You can color points on the scatterplot according to a third quantitative or categorical variable to reveal additional patterns.
New: For data entry, you can enter your own data via the new Data Editor app, import a CSV file, or choose from several pre-loaded example datasets.
New: Fit a multiple linear regression model with both quantitative and categorical predictors. The app
- creates a Scatterplot Matrix to study pairwise relationships
- clearly displays the fitted regression equation, including dummy variables
- presents a table with all regression coefficients
- obtains summary statistics such has R^2-adjusted
- gives fitted values and (standardized) residuals (which you can download)
- lets you make predictions for your own values of the explanatory variables
- constructs a residual plot to check assumptions and for outliers
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