wine<-read .scv="" comment----="">('wine.csv')
str(wine)
## 'data.frame': 25 obs. of 7 variables:
## $ Year : int 1952 1953 1955 1957 1958 1959 1960 1961 1962 1963 ...
## $ Price : num 7.5 8.04 7.69 6.98 6.78 ...
## $ WinterRain : int 600 690 502 420 582 485 763 830 697 608 ...
## $ AGST : num 17.1 16.7 17.1 16.1 16.4 ...
## $ HarvestRain: int 160 80 130 110 187 187 290 38 52 155 ...
## $ Age : int 31 30 28 26 25 24 23 22 21 20 ...
## $ FrancePop : num 43184 43495 44218 45152 45654 ...
summary(wine)
## Year Price WinterRain AGST
## Min. :1952 Min. :6.205 Min. :376.0 Min. :14.98
## 1st Qu.:1960 1st Qu.:6.519 1st Qu.:536.0 1st Qu.:16.20
## Median :1966 Median :7.121 Median :600.0 Median :16.53
## Mean :1966 Mean :7.067 Mean :605.3 Mean :16.51
## 3rd Qu.:1972 3rd Qu.:7.495 3rd Qu.:697.0 3rd Qu.:17.07
## Max. :1978 Max. :8.494 Max. :830.0 Max. :17.65
## HarvestRain Age FrancePop
## Min. : 38.0 Min. : 5.0 Min. :43184
## 1st Qu.: 89.0 1st Qu.:11.0 1st Qu.:46584
## Median :130.0 Median :17.0 Median :50255
## Mean :148.6 Mean :17.2 Mean :49694
## 3rd Qu.:187.0 3rd Qu.:23.0 3rd Qu.:52894
## Max. :292.0 Max. :31.0 Max. :54602
model1<- class="identifier" color="#000000" font="">lm(Price~AGST,data=wine)
summary(model1)
##
## Call:
## lm(formula = Price ~ AGST, data = wine)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.78450 -0.23882 -0.03727 0.38992 0.90318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.4178 2.4935 -1.371 0.183710
## AGST 0.6351 0.1509 4.208 0.000335 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4993 on 23 degrees of freedom
## Multiple R-squared: 0.435, Adjusted R-squared: 0.4105
## F-statistic: 17.71 on 1 and 23 DF, p-value: 0.000335
model1$residuals
## 1 2 3 4 5 6
## 0.04204258 0.82983774 0.21169394 0.15609432 -0.23119140 0.38991701
## 7 8 9 10 11 12
## -0.48959140 0.90318115 0.45372410 0.14887461 -0.23882157 -0.08974238
## 13 14 15 16 17 18
## 0.66185660 -0.05211511 -0.62726647 -0.74714947 0.42113502 -0.03727441
## 19 20 21 22 23 24
## 0.10685278 -0.78450270 -0.64017590 -0.05508720 -0.67055321 -0.22040381
## 25
## 0.55866518
SSE<- class="identifier" color="#000000" font="">sum(model1$residuals^2)
SSE
## [1] 5.734875
model2<- class="identifier" color="#000000" font="">lm(Price~AGST+HarvestRain,data=wine)
summary(model2)
##
## Call:
## lm(formula = Price ~ AGST + HarvestRain, data = wine)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88321 -0.19600 0.06178 0.15379 0.59722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.20265 1.85443 -1.188 0.247585
## AGST 0.60262 0.11128 5.415 1.94e-05 ***
## HarvestRain -0.00457 0.00101 -4.525 0.000167 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3674 on 22 degrees of freedom
## Multiple R-squared: 0.7074, Adjusted R-squared: 0.6808
## F-statistic: 26.59 on 2 and 22 DF, p-value: 1.347e-06
SSE<- class="identifier" color="#000000" font="">sum(model2$residuals^2)
SSE
## [1] 2.970373
model3<- class="identifier" color="#000000" font="">lm(Price~AGST+HarvestRain+WinterRain+Age+FrancePop,data=wine)
model3
##
## Call:
## lm(formula = Price ~ AGST + HarvestRain + WinterRain + Age +
## FrancePop, data = wine)
##
## Coefficients:
## (Intercept) AGST HarvestRain WinterRain Age
## -4.504e-01 6.012e-01 -3.958e-03 1.043e-03 5.847e-04
## FrancePop
## -4.953e-05
SSE<- class="identifier" color="#000000" font="">sum(model3$residuals^2)
SSE
## [1] 1.732113
summary(model3)
##
## Call:
## lm(formula = Price ~ AGST + HarvestRain + WinterRain + Age +
## FrancePop, data = wine)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.48179 -0.24662 -0.00726 0.22012 0.51987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.504e-01 1.019e+01 -0.044 0.965202
## AGST 6.012e-01 1.030e-01 5.836 1.27e-05 ***
## HarvestRain -3.958e-03 8.751e-04 -4.523 0.000233 ***
## WinterRain 1.043e-03 5.310e-04 1.963 0.064416 .
## Age 5.847e-04 7.900e-02 0.007 0.994172
## 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
model4<- class="identifier" color="#000000" font="">lm(Price~AGST+HarvestRain+WinterRain+Age,data=wine)
summary(model4)
##
## Call:
## lm(formula = Price ~ AGST + HarvestRain + WinterRain + Age, data = wine)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45470 -0.24273 0.00752 0.19773 0.53637
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.4299802 1.7658975 -1.942 0.066311 .
## AGST 0.6072093 0.0987022 6.152 5.2e-06 ***
## HarvestRain -0.0039715 0.0008538 -4.652 0.000154 ***
## WinterRain 0.0010755 0.0005073 2.120 0.046694 *
## Age 0.0239308 0.0080969 2.956 0.007819 **
## ---
## 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
cor(wine$WinterRain,wine$Price)
## [1] 0.1366505
cor(wine$Age,wine$FrancePop)
## [1] -0.9944851
cor(wine)
## Year Price WinterRain AGST HarvestRain
## Year 1.00000000 -0.4477679 0.016970024 -0.24691585 0.02800907
## Price -0.44776786 1.0000000 0.136650547 0.65956286 -0.56332190
## WinterRain 0.01697002 0.1366505 1.000000000 -0.32109061 -0.27544085
## AGST -0.24691585 0.6595629 -0.321090611 1.00000000 -0.06449593
## HarvestRain 0.02800907 -0.5633219 -0.275440854 -0.06449593 1.00000000
## Age -1.00000000 0.4477679 -0.016970024 0.24691585 -0.02800907
## FrancePop 0.99448510 -0.4668616 -0.001621627 -0.25916227 0.04126439
## Age FrancePop
## Year -1.00000000 0.994485097
## Price 0.44776786 -0.466861641
## WinterRain -0.01697002 -0.001621627
## AGST 0.24691585 -0.259162274
## HarvestRain -0.02800907 0.041264394
## Age 1.00000000 -0.994485097
## FrancePop -0.99448510 1.000000000
model5<-lm>(Price~AGST+HarvestRain+WinterRain,data=wine)
summary(model5)
##
## Call:
## lm(formula = Price ~ AGST + HarvestRain + WinterRain, data = wine)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67472 -0.12958 0.01973 0.20751 0.63846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.3016263 2.0366743 -2.112 0.046831 *
## AGST 0.6810242 0.1117011 6.097 4.75e-06 ***
## HarvestRain -0.0039481 0.0009987 -3.953 0.000726 ***
## WinterRain 0.0011765 0.0005920 1.987 0.060097 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.345 on 21 degrees of freedom
## Multiple R-squared: 0.7537, Adjusted R-squared: 0.7185
## F-statistic: 21.42 on 3 and 21 DF, p-value: 1.359e-06
wineTest<- class="identifier" color="#000000" font="">read.csv('Wine_test.csv')
str(wineTest)
## 'data.frame': 2 obs. of 7 variables:
## $ Year : int 1979 1980
## $ Price : num 6.95 6.5
## $ WinterRain : int 717 578
## $ AGST : num 16.2 16
## $ HarvestRain: int 122 74
## $ Age : int 4 3
## $ FrancePop : num 54836 55110
predictTest<- span=""> predict(model4,newdata=wineTest)
predictTest
## 1 2
## 6.768925 6.684910
SST<- span=""> sum((wineTest$Price-mean(wine$Price))^2)
1-SSE/SST
## [1] -4.140916