I am currently solving one regression problem using linear regression in R. I have created a linear regression model after creating the model I have used the summary function on to it. I find two terms Multiple R-squared and Adjusted R-squared.

```
model1=lm(Price ~ HarvestRain+WinterRain,data=wine)
summary(model1)
Call:
lm(formula = Price ~ HarvestRain + WinterRain, data = wine)
Residuals:
Min 1Q Median 3Q Max
-1.0933 -0.3222 -0.1012 0.3871 1.1877
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.865e+00 6.616e-01 11.888 4.76e-11 ***
HarvestRain -4.971e-03 1.601e-03 -3.105 0.00516 **
WinterRain -9.848e-05 9.007e-04 -0.109 0.91392
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.5611 on 22 degrees of freedom
Multiple R-squared: 0.3177, Adjusted R-squared: 0.2557
F-statistic: 5.122 on 2 and 22 DF, p-value: 0.01492
```

I want to know how this two are different from each other and which one of them better tells about the significance of the variable. So that I can select the variable for model accuracy.