> tapply(count,site,mean)
    1     2     3     4     5     6 
33.80 68.72 50.64  9.24 10.00 12.64 
> tapply(count,site,md)
Error in tapply(count, site, md) : object 'md' not found
> tapply(count,site,min)
1 2 3 4 5 6 
0 0 0 0 0 0 
> tapply(count,site,max)
  1   2   3   4   5   6 
233 466 407  82  94  95 
> 
> crab.mod <- lm(count~site)
> summary.lm(crab.mod)

Call:
lm(formula = count ~ site)

Residuals:
   Min     1Q Median     3Q    Max 
-68.72 -33.80  -9.24   0.37 397.28 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)    33.80      14.38   2.350   0.0201 *
site2          34.92      20.34   1.717   0.0881 .
site3          16.84      20.34   0.828   0.4090  
site4         -24.56      20.34  -1.208   0.2292  
site5         -23.80      20.34  -1.170   0.2438  
site6         -21.16      20.34  -1.040   0.2999  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 71.9 on 144 degrees of freedom
Multiple R-squared: 0.0934,     Adjusted R-squared: 0.06192 
F-statistic: 2.967 on 5 and 144 DF,  p-value: 0.01401 

> anova(crab.mod)
Analysis of Variance Table

Response: count
           Df Sum Sq Mean Sq F value  Pr(>F)  
site        5  76695 15339.0  2.9669 0.01401 *
Residuals 144 744493  5170.1                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> 
> ehat <- residuals(crab.mod)
> ybar <- predict(crab.mod)
> 
> qqnorm(ehat)
> qqline(ehat)
> 
> plot(ybar,ehat,main="Residuals vs Site Mean")
> 
> ehat1 <- ehat[site==1]
> ehat2 <- ehat[site==2]
> ehat3 <- ehat[site==3]
> ehat4 <- ehat[site==4]
> ehat5 <- ehat[site==5]
> ehat6 <- ehat[site==6]
> 
> levene1 <- abs(ehat1-median(ehat1))
> levene2 <- abs(ehat2-median(ehat2))
> levene3 <- abs(ehat3-median(ehat3))
> levene4 <- abs(ehat4-median(ehat4))
> levene5 <- abs(ehat5-median(ehat5))
> levene6 <- abs(ehat6-median(ehat6))
> 
> levene <- c(levene1,levene2,levene3,levene4,levene5,levene6)
> levene
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
 17  17   5  14   0  17  17  10   6   6  56  16  17  48   4  27   3  10  31  87 
 21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40 
216  64   5   8  15 405 456   4   4   2  10   7   9   6  45 132   0   8 135   4 
 41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60 
  6   5 114  14 194  10  10  46  10   2   5   5   1   8   0   4   4   1   1  31 
 61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80 
402   5   5  13   1   9   5  19  47 309 240 102   0   1   3   2   2   2   2   0 
 81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 
  0   3   2   0   1   2  10   1  28   2   1  26   0  19   6  80  10   8   0   2 
101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 
  2   1   1   0   0   1   0  27   0   0   2  11   2  17   1   1  24  28   3   2 
121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 
 92   1   7   1   2   4   4   4   2   1   4   4   0   4   1   0  18   4  60   0 
141 142 143 144 145 146 147 148 149 150 
  0  39   1  12  15  91   2  18   4   4 
> 
> crab.lev <- lm(levene~site)
> 
> anova(crab.lev)
Analysis of Variance Table

Response: levene
           Df Sum Sq Mean Sq F value  Pr(>F)  
site        5  71146   14229  2.9278 0.01508 *
Residuals 144 699845    4860                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1