In [4]:

    

data <- read.csv('173708.csv', header=T)


    

In [5]:

    

data


    

    
Out[5]:





Age.of.Diagnosis
Native.American
European
African

	
1
55
0.478095
0.464391
0.057514
	
2
59
0.253285
0.145752
0.600963
	
3
58
0.554635
0.411777
0.033588
	
4
44
0.444549
0.537177
0.018274
	
5
53
0.510756
0.479137
0.010106
	
6
52
0.452883
0.356077
0.19104

In [6]:

    

fit <- lm(Age.of.Diagnosis ~ Native.American +  European + African, data=data)


    

In [7]:

    

print(summary(fit))


    

    




Call:
lm(formula = Age.of.Diagnosis ~ Native.American + European + 
    African, data = data)

Residuals:
         1          2          3          4          5          6 
 3.944e+00  8.503e-01  5.642e-01 -1.545e+00  1.657e-09 -3.814e+00 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)
(Intercept)      1252532    4707189   0.266    0.815
Native.American -1252432    4707191  -0.266    0.815
European        -1252532    4707189  -0.266    0.815
African         -1252478    4707187  -0.266    0.815

Residual standard error: 4.094 on 2 degrees of freedom
Multiple R-squared:  0.7696,	Adjusted R-squared:  0.4239 
F-statistic: 2.226 on 3 and 2 DF,  p-value: 0.3249

In [8]:

    

layout(matrix(c(1,2,3,4),2,2))
plot(fit)


    

    




Warning message:
: not plotting observations with leverage one:
  5Warning message:
: not plotting observations with leverage one:
  5