A moderation model with a categorical moderator.
Format
A data frame with 300 rows and 5 variables:
- x
Predictor. Numeric.
- w
Moderator. String. Values: "group1", "group2", "group3"
- y
Outcome variable. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
data(data_mod_cat)
dat <- data_mod_cat
summary(lm_y <- lm(y ~ x*w + c1 + c2, dat))
#>
#> Call:
#> lm(formula = y ~ x * w + c1 + c2, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.51583 -0.64354 -0.00324 0.62382 3.08839
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 3.740541 0.521689 7.170 6.17e-12 ***
#> x 0.811466 0.087904 9.231 < 2e-16 ***
#> wgroup2 -0.127138 0.458910 -0.277 0.78194
#> wgroup3 0.325005 0.415632 0.782 0.43487
#> c1 -0.003431 0.056895 -0.060 0.95196
#> c2 -0.059157 0.056892 -1.040 0.29929
#> x:wgroup2 -0.126358 0.140534 -0.899 0.36933
#> x:wgroup3 -0.376700 0.128669 -2.928 0.00368 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.9806 on 292 degrees of freedom
#> Multiple R-squared: 0.3699, Adjusted R-squared: 0.3548
#> F-statistic: 24.49 on 7 and 292 DF, p-value: < 2.2e-16
#>