Tools for Building OLS Regression Models
Akaike information criterion
Amemiya's prediction criterion
Collinearity diagnostics
Part and partial correlations
Final prediction error
Hadi's influence measure
Hocking's Sp
Launch shiny app
Leverage
Mallow's Cp
MSEP
Added variable plots
Residual plus component plot
Cooks' D bar plot
Cooks' D chart
DFBETAs panel
DFFITS plot
Diagnostics panel
Hadi plot
Observed vs fitted values plot
Simple linear regression line
Residual box plot
Residual fit spread plot
Residual vs fitted plot
Residual histogram
Studentized residuals vs leverage plot
Potential residual plot
Residual QQ plot
Residual vs regressor plot
Standardized residual chart
Deleted studentized residual vs fitted values plot
Studentized residual plot
Response variable profile
Predicted rsquare
Added variable plot data
Cooks' D plot data
Cooks' d outlier data
DFBETAs plot data
DFBETAs plot outliers
Deleted studentized residual plot data
Cooks' D outlier observations
Regress predictor on other predictors
Regress y on other predictors
Residual fit spread plot data
Studentized residual vs leverage plot data
Residual vs regressor plot data
Standardized residual chart data
Studentized residual plot data
PRESS
Lack of fit F test
Ordinary least squares regression
Bayesian information criterion
Sawa's bayesian information criterion
All possible regression variable coefficients
All possible regression
Stepwise Adjusted R-Squared backward regression
Stepwise AIC backward regression
Stepwise backward regression
Stepwise R-Squared backward regression
Stepwise SBC backward regression
Stepwise SBIC backward regression
Best subsets regression
Stepwise Adjusted R-Squared regression
Stepwise AIC regression
Stepwise regression
Stepwise R-Squared regression
Stepwise SBC regression
Stepwise SBIC regression
Stepwise Adjusted R-Squared forward regression
Stepwise AIC forward regression
Stepwise forward regression
Stepwise R-Squared forward regression
Stepwise SBC forward regression
Stepwise SBIC forward regression
Bartlett test
Breusch pagan test
Correlation test for normality
F test
Test for normality
Bonferroni Outlier Test
Score test
olsrr package
Residual vs regressors plot for shiny app
Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.
Useful links