# output from regress is as expected Code ols_regress(mpg ~ disp + hp + wt, data = mtcars) Output Model Summary --------------------------------------------------------------- R 0.909 RMSE 2.468 R-Squared 0.827 MSE 6.964 Adj. R-Squared 0.808 Coef. Var 13.135 Pred R-Squared 0.768 AIC 158.643 MAE 1.907 SBC 165.972 --------------------------------------------------------------- RMSE: Root Mean Square Error MSE: Mean Square Error MAE: Mean Absolute Error AIC: Akaike Information Criteria SBC: Schwarz Bayesian Criteria ANOVA -------------------------------------------------------------------- Sum of Squares DF Mean Square F Sig. -------------------------------------------------------------------- Regression 931.057 3 310.352 44.566 0.0000 Residual 194.991 28 6.964 Total 1126.047 31 -------------------------------------------------------------------- Parameter Estimates ---------------------------------------------------------------------------------------- model Beta Std. Error Std. Beta t Sig lower upper ---------------------------------------------------------------------------------------- (Intercept) 37.106 2.111 17.579 0.000 32.782 41.429 disp -0.001 0.010 -0.019 -0.091 0.929 -0.022 0.020 hp -0.031 0.011 -0.354 -2.724 0.011 -0.055 -0.008 wt -3.801 1.066 -0.617 -3.565 0.001 -5.985 -1.617 ---------------------------------------------------------------------------------------- --- Code ols_regress(lm(mpg ~ disp + hp + wt, data = mtcars)) Output Model Summary --------------------------------------------------------------- R 0.909 RMSE 2.468 R-Squared 0.827 MSE 6.964 Adj. R-Squared 0.808 Coef. Var 13.135 Pred R-Squared 0.768 AIC 158.643 MAE 1.907 SBC 165.972 --------------------------------------------------------------- RMSE: Root Mean Square Error MSE: Mean Square Error MAE: Mean Absolute Error AIC: Akaike Information Criteria SBC: Schwarz Bayesian Criteria ANOVA -------------------------------------------------------------------- Sum of Squares DF Mean Square F Sig. -------------------------------------------------------------------- Regression 931.057 3 310.352 44.566 0.0000 Residual 194.991 28 6.964 Total 1126.047 31 -------------------------------------------------------------------- Parameter Estimates ---------------------------------------------------------------------------------------- model Beta Std. Error Std. Beta t Sig lower upper ---------------------------------------------------------------------------------------- (Intercept) 37.106 2.111 17.579 0.000 32.782 41.429 disp -0.001 0.010 -0.019 -0.091 0.929 -0.022 0.020 hp -0.031 0.011 -0.354 -2.724 0.011 -0.055 -0.008 wt -3.801 1.066 -0.617 -3.565 0.001 -5.985 -1.617 ----------------------------------------------------------------------------------------