Compact print method for objects of class "linear_regression".
Shows R-squared, adjusted R-squared, F statistic, and p-value.
For the full detailed output, use summary().
Usage
# S3 method for class 'linear_regression'
print(x, ...)Arguments
- x
An object of class
"linear_regression"returned bylinear_regression.- ...
Additional arguments (not used).
Examples
result <- linear_regression(survey_data, life_satisfaction ~ age + income)
result # compact one-line overview
#> Linear Regression: life_satisfaction ~ age + income
#> R2 = 0.201, adj.R2 = 0.200, F(2, 2112) = 265.60, p < 0.001 ***, N = 2115
summary(result) # full detailed output
#>
#> Linear Regression Results
#> -------------------------
#> - Formula: life_satisfaction ~ age + income
#> - Method: ENTER (all predictors)
#> - N: 2115
#>
#> Descriptive Statistics
#> ----------------------------------------------------------------------
#> Variable Mean Std.Dev. N
#> ----------------------------------------------------------------------
#> life_satisfaction 3.638 1.148 2115
#> age 50.827 16.995 2115
#> income 3757.683 1430.923 2115
#> ----------------------------------------------------------------------
#>
#> Model Summary
#> ------------------------------------------------------------
#> R 0.448
#> R Square 0.201
#> Adjusted R Square 0.200
#> Std. Error of Estimate 1.026
#> ------------------------------------------------------------
#>
#> ANOVA
#> ------------------------------------------------------------------------------
#> Source Sum of Squares df Mean Square F Sig.
#> ------------------------------------------------------------------------------
#> Regression 559.609 2 279.804 265.598 0.000 ***
#> Residual 2224.965 2112 1.053
#> Total 2784.574 2114
#> ------------------------------------------------------------------------------
#>
#> Coefficients
#> ----------------------------------------------------------------------------------------
#> Term B Std.Error Beta t Sig.
#> ----------------------------------------------------------------------------------------
#> (Intercept) 2.321 0.092 25.237 0.000 ***
#> age -0.001 0.001 -0.010 -0.508 0.611
#> income 0.000 0.000 0.448 23.037 0.000 ***
#> ----------------------------------------------------------------------------------------
#>
#> Collinearity Statistics
#> --------------------------------------------------
#> Term Tolerance VIF
#> --------------------------------------------------
#> age 1.000 1.000
#> income 1.000 1.000
#> --------------------------------------------------
#> VIF > 10 (Tolerance < 0.1) indicates problematic collinearity.
#>
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05