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college <- read.csv("http://commres.net/wiki/_media/r/college.csv")
attach(college)
str(college)
head(college)
salary <- salary / 1000
public<-factor(public, c(0,1), labels=c('Private', 'Public'))
location<-factor(location, c(1,2,3,4), labels=c('S', 'MW','NE', 'W'))
m1 <- lm(salary~public+location)
m2 <- lm(salary~public*location)
summary(m1)
summary(m2)
# ###################
college ← read.csv(“http://commres.net/wiki/_media/r/college.csv”)
attach(college)
The following object is masked from acne.re (pos = 36):
id
The following object is masked from acne.re (pos = 37):
id
str(college)
'data.frame': 85 obs. of 6 variables:
$ id : int 1 2 3 4 5 6 7 8 9 10 …
$ name : chr “Massachusetts Institute of Technology (MIT)” “Harvard University” “Dartmouth College” “Princeton University” …
$ salary : num 119000 121000 123000 123000 110000 112000 111000 117000 111000 104000 …
$ cost : int 189300 189600 188400 188700 194200 181900 191300 187600 180400 184900 …
$ public : int 0 0 0 0 0 0 0 0 0 0 …
$ location: int 3 3 3 3 3 2 3 1 3 3 …
head(college)id name salary cost public location1 1 Massachusetts Institute of Technology (MIT) 119000 189300 0 3
2 2 Harvard University 121000 189600 0 3
3 3 Dartmouth College 123000 188400 0 3
4 4 Princeton University 123000 188700 0 3
5 5 Yale University 110000 194200 0 3
6 6 University of Notre Dame 112000 181900 0 2
salary ← salary / 1000
public←factor(public, c(0,1), labels=c('Private', 'Public'))
location←factor(location, c(1,2,3,4), labels=c('S', 'MW','NE', 'W'))
m1 ← lm(salary~public+location)
m2 ← lm(salary~public*location)
summary(m1)
Call:
lm(formula = salary ~ public + location)
Residuals:
Min 1Q Median 3Q Max
-17.15 -4.75 -0.35 2.85 31.67
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.56 1.90 52.30 < 2e-16 *
publicPublic -7.30 1.83 -3.99 0.00014 *
locationMW -2.40 2.52 -0.95 0.34366
locationNE 8.79 2.39 3.68 0.00042 *
locationW -10.93 2.49 -4.39 3.4e-05 *
—
Signif. codes: 0 ‘*’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.86 on 80 degrees of freedom
Multiple R-squared: 0.587, Adjusted R-squared: 0.566
F-statistic: 28.4 on 4 and 80 DF, p-value: 1.06e-14
summary(m2)
Call:
lm(formula = salary ~ public * location)
Residuals:
Min 1Q Median 3Q Max
-11.19 -4.88 -0.65 2.49 27.55
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.492 2.006 49.60 < 2e-16 *
publicPublic -7.142 3.172 -2.25 0.0272 *
locationMW -1.982 2.975 -0.67 0.5074
locationNE 11.393 2.537 4.49 2.5e-05 *
locationW -17.554 3.172 -5.53 4.1e-07 *
publicPublic:locationMW -0.913 4.500 -0.20 0.8398
publicPublic:locationNE -12.843 4.704 -2.73 0.0078
publicPublic:locationW 10.650 4.451 2.39 0.0192 *
—
Signif. codes: 0 ‘*’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.95 on 77 degrees of freedom
Multiple R-squared: 0.689, Adjusted R-squared: 0.661
F-statistic: 24.4 on 7 and 77 DF, p-value: <2e-16