Aclass <- c(11,16,19,18,20,23,19,21,24,29)
Bclass <- c(23,26,34,29,33,27,23,36,30,40)

plot(Aclass, Bclass)
abline(lm(Bclass~Aclass),col="skyblue")

cor(Aclass, Bclass)
## [1] 0.6991015

An education researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution (Rank), influence admission into graduate school (Admit).

dta <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")


str(dta)
## 'data.frame':    400 obs. of  4 variables:
##  $ admit: int  0 1 1 1 0 1 1 0 1 0 ...
##  $ gre  : int  380 660 800 640 520 760 560 400 540 700 ...
##  $ gpa  : num  3.61 3.67 4 3.19 2.93 3 2.98 3.08 3.39 3.92 ...
##  $ rank : int  3 3 1 4 4 2 1 2 3 2 ...
tail(dta,10)
##     admit gre  gpa rank
## 391     1 800 3.05    2
## 392     1 660 3.88    2
## 393     1 600 3.38    3
## 394     1 620 3.75    2
## 395     1 460 3.99    3
## 396     0 620 4.00    2
## 397     0 560 3.04    3
## 398     0 460 2.63    2
## 399     0 700 3.65    2
## 400     0 600 3.89    3
plot(dta$gre,dta$gpa)


library(lattice)

xyplot(gre ~ gpa | factor(rank), data = dta, type = c("g","p","r"), layout = c(4, 1))

head(dta,7) str(dta)

library(lattice) xyplot(gre ~ gpa | factor(rank), data = dta, type = c(“g”,“p”,“r”), layout = c(4, 1))

plot(dta\(gre,dta\)gpa)

```

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