Could someone explain to the statistically naive what the difference between Multiple R-squared and Adjusted R-squared is? I am doing a single-variate regression analysis as follows:

 v.lm <- lm(epm ~ n_days, data=v)
 print(summary(v.lm))

Results:

Call:
lm(formula = epm ~ n_days, data = v)

Residuals:
    Min      1Q  Median      3Q     Max 
-693.59 -325.79   53.34  302.46  964.95 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2550.39      92.15  27.677   <2e-16 ***
n_days        -13.12       5.39  -2.433   0.0216 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 410.1 on 28 degrees of freedom
Multiple R-squared: 0.1746,     Adjusted R-squared:

0.1451 F-statistic: 5.921 on 1 and 28 DF, p-value: 0.0216

Apologies for the newbiness of this question.