Education Expenditure Data — education • robustbase

Education Expenditure Data, from Chatterjee and Price (1977, p.108). This data set, representing the education expenditure variables in the 50 US states, providing an interesting example of heteroscedacity.

data(education, package="robustbase")

Format

A data frame with 50 observations on the following 6 variables.

State: State
Region: Region (1=Northeastern, 2=North central, 3=Southern, 4=Western)
X1: Number of residents per thousand residing in urban areas in 1970
X2: Per capita personal income in 1973
X3: Number of residents per thousand under 18 years of age in 1974
Y: Per capita expenditure on public education in a state, projected for 1975

Source

P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection; Wiley, p.110, table 16.

Examples

data(education)
education.x <- data.matrix(education[, 3:5])


summary(lm.education <- lm(Y ~ Region + X1+X2+X3, data=education))
#> 
#> Call:
#> lm(formula = Y ~ Region + X1 + X2 + X3, data = education)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -80.143 -24.595  -4.734  15.016  95.274 
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -522.77813  126.45851  -4.134 0.000153 ***
#> Region         7.02248    6.24147   1.125 0.266499    
#> X1            -0.01803    0.05269  -0.342 0.733707    
#> X2             0.07509    0.01182   6.355 9.27e-08 ***
#> X3             1.37998    0.34905   3.953 0.000270 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 40.36 on 45 degrees of freedom
#> Multiple R-squared:  0.6025,	Adjusted R-squared:  0.5672 
#> F-statistic: 17.05 on 4 and 45 DF,  p-value: 1.406e-08
#> 


## See  example(lmrob.M.S) # for how robust regression is used