KTKN_3

Câu 1:

Sử dụng tập dữ liệu mtcars, dự đoán biến mpg bằng hồi quy tuyến tính dựa vào hai biến disp và qsec.
Kiểm tra độ phù hợp của mô hình bằng hệ số R bình phương hiệu chỉnh (Adjusted R-squared).

Cau 2: - Dùng tập dữ liệu iris, dự đoán biến Species (chỉ xét hai loài: versicolor và virginica) bằng hồi quy logistic. - Vẽ đường ROC và tính AUC.

mydata<-mtcars
mydata

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

model <- lm(mpg ~ disp + qsec, data = mydata) #du doan so dam di dc dua tren dung tich xi lanh va tg chay dc 1/4 dam

summary(model)

## 
## Call:
## lm(formula = mpg ~ disp + qsec, data = mydata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.0316 -2.0491 -0.9206  1.8265  7.0070 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 25.504508   7.184094   3.550  0.00134 ** 
## disp        -0.039888   0.005288  -7.543 2.58e-08 ***
## qsec         0.212288   0.366776   0.579  0.56720    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.288 on 29 degrees of freedom
## Multiple R-squared:  0.7216, Adjusted R-squared:  0.7024 
## F-statistic: 37.58 on 2 and 29 DF,  p-value: 8.884e-09

data(iris)

iris2 <- subset(iris, Species != "setosa") 

iris2$Species <- factor(iris2$Species) #chuyen species thanh bien phan loai

model_log <- glm(Species ~ Sepal.Length + Sepal.Width +
                 Petal.Length + Petal.Width,
                 data = iris2,
                 family = binomial) #bao cho R biet day la mo hinh hoi quy tt logistic(ko du doan ttiep species, du doan 1 bong hoa la virginica)
summary(model_log)

## 
## Call:
## glm(formula = Species ~ Sepal.Length + Sepal.Width + Petal.Length + 
##     Petal.Width, family = binomial, data = iris2)
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept)   -42.638     25.707  -1.659   0.0972 .
## Sepal.Length   -2.465      2.394  -1.030   0.3032  
## Sepal.Width    -6.681      4.480  -1.491   0.1359  
## Petal.Length    9.429      4.737   1.991   0.0465 *
## Petal.Width    18.286      9.743   1.877   0.0605 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 138.629  on 99  degrees of freedom
## Residual deviance:  11.899  on 95  degrees of freedom
## AIC: 21.899
## 
## Number of Fisher Scoring iterations: 10

library(pROC)
prob <- predict(model_log, type = "response")
roc_obj <- roc(iris2$Species, prob)

plot(roc_obj, col="blue")

auc(roc_obj)

## Area under the curve: 0.9972

KTKN_3

ahn

2026-03-05