1 개요[ | ]
- R lm()
- "Linear Models → 선형 모델"
R
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x <- c(151, 174, 138, 186, 128, 136, 179, 163, 152, 131)
y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48)
relation <- lm(y~x)
relation
Call: lm(formula = y ~ x) Coefficients: (Intercept) x -38.4551 0.6746
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summary(relation)
Call: lm(formula = y ~ x) Residuals: Min 1Q Median 3Q Max -6.3002 -1.6629 0.0412 1.8944 3.9775 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -38.45509 8.04901 -4.778 0.00139 ** x 0.67461 0.05191 12.997 1.16e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.253 on 8 degrees of freedom Multiple R-squared: 0.9548, Adjusted R-squared: 0.9491 F-statistic: 168.9 on 1 and 8 DF, p-value: 1.164e-06
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summary(lm(Fertility ~ ., data=swiss))
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library(MASS)
data(hills)
attach(hills)
md <- lm( time ~ dist + climb)
summary(md)
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fit4 <- lm(Fertility ~ Agriculture + Education + Catholic + Infant.Mortality, data = swiss)
summary(fit4)
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data(state)
statdata <- data.frame(state.x77,row.names=state.abb)
g3 <- lm(Life.Exp ~ Illiteracy, data=statdata)
summary(g3)
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m <- lm(dist ~ speed, data=cars)
summary(m)
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2 같이 보기[ | ]
3 참고[ | ]
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