1 개요[ | ]
- R simple regression
- R simple linear regression
- R 회귀분석
- R 단순회귀분석
- R 단순선형회귀분석
2 예시 1: 키와 몸무게[ | ]
R
CPU
2.5s
MEM
95M
37.6s
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df <- data.frame(
height = c(1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63, 1.65, 1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83),
mass = c(52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93, 61.29, 63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46)
)
model <- lm(mass ~ height, data=df)
summary(model)
Call: lm(formula = mass ~ height, data = df) Residuals: Min 1Q Median 3Q Max -0.88171 -0.64484 -0.06993 0.34095 1.39385 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -39.062 2.938 -13.29 6.05e-09 *** height 61.272 1.776 34.50 3.60e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.7591 on 13 degrees of freedom Multiple R-squared: 0.9892, Adjusted R-squared: 0.9884 F-statistic: 1190 on 1 and 13 DF, p-value: 3.604e-14
- → 회귀식 [math]\displaystyle{ y=61.272x-39.062 }[/math]
- → 결정계수 [math]\displaystyle{ R^2=0.9892 }[/math]
3 예시 2: 대학 불합격[ | ]
R
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df <- data.frame(
student = c(4000,10000,15000,12000,8000,16000,5000,7000,9000,10000),
rejection = c(100,400,500,400,300,400,200,100,400,200)
)
model <- lm(rejection ~ student, data=df)
summary(model)
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- → 회귀식 [math]\displaystyle{ y=0.028902x+22.543353 }[/math]
- → 결정계수 [math]\displaystyle{ R^2=0.6423 }[/math]
4 예시 3: 아이스티 주문[ | ]
R
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df <- read.csv('https://raw.githubusercontent.com/jmnote/ds/main/simple-regression/iced-tea-orders.csv')
head(df)
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model <- lm(order ~ high_temperature, data=df)
summary(model)
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- → 회귀식 [math]\displaystyle{ y=3.7379x-36.3612 }[/math]
- → 결정계수 [math]\displaystyle{ R^2=0.8225 }[/math]
5 같이 보기[ | ]
편집자 Jmnote
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