Hi,

I want to do a linear Regression. Therefore I built an index of some Likert-scaled variables to make them quasi-metric. Cronbach's Alpha is 0.73. This index should be my dependent variable. But I have no idea how to plot with an index as dependant variable and an ordinal independant variable. My plot looks like the following:

```
> summary(lm(data = dat, Index_individueller.Beitrag ~ Alter.Wahlrecht))
Call:
lm(formula = Index_individueller.Beitrag ~ Alter.Wahlrecht, data = dat)
Residuals:
Min 1Q Median 3Q Max
-15.0773 -2.2372 -0.1372 2.8828 10.8028
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.2772 0.3860 44.76 < 2e-16 ***
Alter.Wahlrecht 0.9600 0.1343 7.15 2.12e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.592 on 728 degrees of freedom
Multiple R-squared: 0.06561, Adjusted R-squared: 0.06433
F-statistic: 51.12 on 1 and 728 DF, p-value: 2.119e-12
> plot(data = dat, Index_individueller.Beitrag ~ Alter.Wahlrecht)
> abline(lm(Index_individueller.Beitrag ~ Alter.Wahlrecht))
```

The plot does not look like there is a linear correlation, but the regression is significant. Is the regression wrong or the way I plot it?

Thanks for any help! I'm really new with R