I am currently solving one regression problem using linear regression in which I have a created a regression model in R.

```
model1=lm(Price ~ Year+WinterRain+AGST+HarvestRain+Age+FrancePop,data=wine)
summary(model1)
Call:
lm(formula = Price ~ Year + WinterRain + AGST + HarvestRain + Age + FrancePop, data = wine)
Residuals:
Min 1Q Median 3Q Max
-0.48179 -0.24662 -0.00726 0.22012 0.51987
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.092e-01 1.467e+02 0.005 0.996194
Year -5.847e-04 7.900e-02 -0.007 0.994172
WinterRain 1.043e-03 5.310e-04 1.963 0.064416 .
AGST 6.012e-01 1.030e-01 5.836 1.27e-05 ***
HarvestRain -3.958e-03 8.751e-04 -4.523 0.000233 ***
Age NA NA NA NA
FrancePop -4.953e-05 1.667e-04 -0.297 0.769578
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3019 on 19 degrees of freedom
Multiple R-squared: 0.8294, Adjusted R-squared: 0.7845
F-statistic: 18.47 on 5 and 19 DF, p-value: 1.044e-06
```

I have looked into the significances of the model variable by looking into a number of stars which tells about the significances of the variable .So I have removed one variable french population so I get a better model.

##creating new model

model1=lm(Price ~ Year+WinterRain+AGST+HarvestRain+Age,data=wine)

summary(model1)

```
Call:
lm(formula = Price ~ Year + WinterRain + AGST + HarvestRain + Age, data = wine)
Residuals:
Min 1Q Median 3Q Max
-0.45470 -0.24273 0.00752 0.19773 0.53637
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.0248601 16.4434570 2.677 0.014477 *
Year -0.0239308 0.0080969 -2.956 0.007819 **
WinterRain 0.0010755 0.0005073 2.120 0.046694 *
AGST 0.6072093 0.0987022 6.152 5.2e-06 ***
HarvestRain -0.0039715 0.0008538 -4.652 0.000154 ***
Age NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.295 on 20 degrees of freedom
Multiple R-squared: 0.8286, Adjusted R-squared: 0.7943
F-statistic: 24.17 on 4 and 20 DF, p-value: 2.036e-07
```

After creating a new model, the variable significances has changed from the previous model .I want to know why this happen.