R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> v1= c(7,6,7,5,8,6,7,6,4,6)
> t.test(v1)

	One Sample t-test

data:  v1 
t = 17.2697, df = 9, p-value = 3.3e-08
alternative hypothesis: true mean is not equal to 0 
95 percent confidence interval:
 5.387861 7.012139 
sample estimates:
mean of x 
      6.2 

> ?t.test
starting httpd help server ... done
> t.test(v1, mu=6)

	One Sample t-test

data:  v1 
t = 0.5571, df = 9, p-value = 0.591
alternative hypothesis: true mean is not equal to 6 
95 percent confidence interval:
 5.387861 7.012139 
sample estimates:
mean of x 
      6.2 

> v2= c(4,6,5,7,4,3,6,5,4,6)
> t.test(v1,v2)

	Welch Two Sample t-test

data:  v1 and v2 
t = 2.25, df = 17.843, p-value = 0.03731
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 0.07880192 2.32119808 
sample estimates:
mean of x mean of y 
      6.2       5.0 

> t.test(v1,v2, paired=TRUE)

	Paired t-test

data:  v1 and v2 
t = 2.0925, df = 9, p-value = 0.06592
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -0.09732078  2.49732078 
sample estimates:
mean of the differences 
                    1.2 

> v3 = c(3,7,5,3,6,4,6,5,3,7)
> mydata = data.frame(v1,v2,v3)
> mydata
   v1 v2 v3
1   7  4  3
2   6  6  7
3   7  5  5
4   5  7  3
5   8  4  6
6   6  3  4
7   7  6  6
8   6  5  5
9   4  4  3
10  6  6  7
> mydata = stack(mydata)
> mydata
   values ind
1       7  v1
2       6  v1
3       7  v1
4       5  v1
5       8  v1
6       6  v1
7       7  v1
8       6  v1
9       4  v1
10      6  v1
11      4  v2
12      6  v2
13      5  v2
14      7  v2
15      4  v2
16      3  v2
17      6  v2
18      5  v2
19      4  v2
20      6  v2
21      3  v3
22      7  v3
23      5  v3
24      3  v3
25      6  v3
26      4  v3
27      6  v3
28      5  v3
29      3  v3
30      7  v3
> oneway.test(values ~ ind, data=mydata)

	One-way analysis of means (not assuming equal
	variances)

data:  values and ind 
F = 3.2874, num df = 2.000, denom df = 17.694, p-value =
0.06109

> ow=oneway.test(values ~ ind, data=mydata)
> summary(ow)
          Length Class  Mode     
statistic 1      -none- numeric  
parameter 2      -none- numeric  
p.value   1      -none- numeric  
method    1      -none- character
data.name 1      -none- character
> dicedata = c(312,297,302,311,299,307)
> probs = c(1/6,1/6,1/6,1/6,1/6,1/6)
> chisq.test(dicedata, p=probs)

	Chi-squared test for given probabilities

data:  dicedata 
X-squared = 0.6477, df = 5, p-value = 0.9857

> dicedata2 = c(1000,20,20,20,20,1000)
> chisq.test(dicedata2, p=probs)

	Chi-squared test for given probabilities

data:  dicedata2 
X-squared = 3693.846, df = 5, p-value < 2.2e-16

> mymat = matrix(c(43,23,63,45,34,18), ncol=2)
> mymat
     [,1] [,2]
[1,]   43   45
[2,]   23   34
[3,]   63   18
> chisq.test(mymat)

	Pearson's Chi-squared test

data:  mymat 
X-squared = 23.1004, df = 2, p-value = 9.634e-06

>