"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[ | ]

@@ 1번째 줄: / 1번째 줄: @@
 ==개요==
+;R simple regression
 ;R simple linear regression
 ;R 회귀분석
+;R 단순회귀분석
 ;R 단순선형회귀분석
-<source lang='r'>
+==예시 1: 키와 몸무게 ==
+<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)
-## Call:
+</syntaxhighlight>
-## lm(formula = mass ~ height, data = df)
+:→ 회귀식 <math>y=61.272x-39.062</math>
-##
+:→ 결정계수 <math>R^2=0.9892</math>
-## Residuals:
-##      Min       1Q   Median       3Q      Max
+==예시 2: 대학 불합격==
-## -0.88171 -0.64484 -0.06993  0.34095  1.39385
+<syntaxhighlight lang='r' notebook=2>
-##
+df <- data.frame(
-## Coefficients:
+  student = c(4000,10000,15000,12000,8000,16000,5000,7000,9000,10000),
-##             Estimate Std. Error t value Pr(>|t|)
+  rejection = c(100,400,500,400,300,400,200,100,400,200)
-## (Intercept)  -39.062      2.938  -13.29 6.05e-09 ***
+)
-## height        61.272      1.776   34.50 3.60e-14 ***
+model <- lm(rejection ~ student, data=df)
-## ---
+summary(model)
-## Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+</syntaxhighlight>
-##
+:→ 회귀식 <math>y=0.028902x+22.543353</math>
-## Residual standard error: 0.7591 on 13 degrees of freedom
+:→ 결정계수 <math>R^2=0.6423</math>
-## Multiple R-squared:  0.9892,	Adjusted R-squared:  0.9884
-## F-statistic:  1190 on 1 and 13 DF,  p-value: 3.604e-14
+==예시 3: 아이스티 주문==
-</source>
+<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>
 ==같이 보기==
-* [[단순선형회귀분석]]
+* [[단순회귀분석]]
+* [[R 다중회귀분석]]
 * [[R 분산분석]]
 * [[R 로지스틱 회귀분석]]
+[[분류: R 회귀분석]]
 [[분류: R 데이터 분석]]
 [[분류: 회귀분석]]