"R 단순선형회귀분석"의 두 판 사이의 차이

2024년 1월 3일 (수) 13:01 기준 최신판

1 개요[ | ]

R simple regression
R simple linear regression
R 회귀분석
R 단순회귀분석
R 단순선형회귀분석

2 예시 1: 키와 몸무게[ | ]

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)

→ 회귀식 [math]\displaystyle{ y=61.272x-39.062 }[/math]

→ 결정계수 [math]\displaystyle{ R^2=0.9892 }[/math]

3 예시 2: 대학 불합격[ | ]

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)

→ 회귀식 [math]\displaystyle{ y=0.028902x+22.543353 }[/math]

→ 결정계수 [math]\displaystyle{ R^2=0.6423 }[/math]

4 예시 3: 아이스티 주문[ | ]

df <- read.csv('https://raw.githubusercontent.com/jmnote/ds/main/simple-regression/iced-tea-orders.csv')
head(df)

model <- lm(order ~ high_temperature, data=df)
summary(model)

→ 회귀식 [math]\displaystyle{ y=3.7379x-36.3612 }[/math]

→ 결정계수 [math]\displaystyle{ R^2=0.8225 }[/math]

5 같이 보기[ | ]

@@ 6번째 줄: / 6번째 줄: @@
 ;R 단순선형회귀분석
-==예시 1: mass ~ height==
+==예시 1: 키와 몸무게 ==
-<source lang='r' run>
+<syntaxhighlight lang='r' notebook>
 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)
 )
-lm1 <- lm(mass ~ height, data=df)
+model <- lm(mass ~ height, data=df)
-summary(lm1)
+summary(model)
-</source>
+</syntaxhighlight>
 :→ 회귀식 <math>y=61.272x-39.062</math>
 :→ 결정계수 <math>R^2=0.9892</math>
-==예시 2: 아이스티 주문==
+==예시 2: 대학 불합격==
-<source lang='r' run>
+<syntaxhighlight lang='r' notebook=2>
-options(echo=T)
+df <- data.frame(
-df <- read.csv('https://raw.githubusercontent.com/jmnote/z-dataset/master/simple-regression/iced-tea-orders.csv')
+  student = c(4000,10000,15000,12000,8000,16000,5000,7000,9000,10000),
-df
+  rejection = c(100,400,500,400,300,400,200,100,400,200)
-lm1 <- lm(order ~ high_temperature, data=df)
+)
-summary(lm1)
+model <- lm(rejection ~ student, data=df)
-</source>
+summary(model)
+</syntaxhighlight>
+:→ 회귀식 <math>y=0.028902x+22.543353</math>
+:→ 결정계수 <math>R^2=0.6423</math>
+==예시 3: 아이스티 주문==
+<syntaxhighlight lang='r' notebook>
+df <- read.csv('https://raw.githubusercontent.com/jmnote/ds/main/simple-regression/iced-tea-orders.csv')
+head(df)
+</syntaxhighlight>
+<syntaxhighlight lang='r' notebook>
+model <- lm(order ~ high_temperature, data=df)
+summary(model)
+</syntaxhighlight>
 :→ 회귀식 <math>y=3.7379x-36.3612</math>
 :→ 결정계수 <math>R^2=0.8225</math>
@@ 35번째 줄: / 48번째 줄: @@
 * [[R 로지스틱 회귀분석]]
+[[분류: R 회귀분석]]
 [[분류: R 데이터 분석]]
 [[분류: 회귀분석]]