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One-way Anova Using R
蝎子 发表于 2008-01-21 15:48:39
INPUT DATA:
> g1=c(12,10,7,8,9,14)
> g2=c(12,16,15,9)
> g3=c(9,7,6,11,7)
> g4=c(12,8,8,10)
> data=data.frame(level=c(g1,g2,g3,g4),
group=factor(c(rep("1",6),rep("2",4),rep("3",5),rep("4",4))))
> data
level group
1 12 1
2 10 1
3 7 1
4 8 1
5 9 1
6 14 1
7 12 2
8 16 2
9 15 2
10 9 2
11 9 3
12 7 3
13 6 3
14 11 3
15 7 3
16 12 4
17 8 4
18 8 4
19 10 4
SIMPLE STATISTICS USING R:
> sapply(split(data$level,data$group),mean)
1 2 3 4
10.0 13.0 8.0 9.5
> sapply(split(data$level,data$group),var)
1 2 3 4
6.800000 10.000000 4.000000 3.666667
> sapply(split(data$level,data$group),sum)
1 2 3 4
60 52 40 38
THE FOLLOWING DISPLAYS THE FITTED COEF AND T-TESTS:
> analysis=lm(level~group,data=data)
> summary(analysis)
Call:
lm(formula = level ~ group, data = data)
Residuals:
Min 1Q Median 3Q Max
-4.0 -1.5 -1.0 2.0 4.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.000 1.006 9.945 5.37e-08 ***
group2 3.000 1.590 1.887 0.0787 .
group3 -2.000 1.492 -1.341 0.1999
group4 -0.500 1.590 -0.314 0.7575
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.463 on 15 degrees of freedom
Multiple R-Squared: 0.3851, Adjusted R-squared: 0.2622
F-statistic: 3.132 on 3 and 15 DF, p-value: 0.05699
ONEWAY ANOVA:
> anova(analysis)
Analysis of Variance Table
Response: level
Df Sum Sq Mean Sq F value Pr(>F)
group 3 57.000 19.000 3.1319 0.05699 .
Residuals 15 91.000 6.067
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
HERE IS THE ANO VA TABLE, AND P=0.057
> g1=c(12,10,7,8,9,14)
> g2=c(12,16,15,9)
> g3=c(9,7,6,11,7)
> g4=c(12,8,8,10)
> data=data.frame(level=c(g1,g2,g3,g4),
group=factor(c(rep("1",6),rep("2",4),rep("3",5),rep("4",4))))
> data
level group
1 12 1
2 10 1
3 7 1
4 8 1
5 9 1
6 14 1
7 12 2
8 16 2
9 15 2
10 9 2
11 9 3
12 7 3
13 6 3
14 11 3
15 7 3
16 12 4
17 8 4
18 8 4
19 10 4
SIMPLE STATISTICS USING R:
> sapply(split(data$level,data$group),mean)
1 2 3 4
10.0 13.0 8.0 9.5
> sapply(split(data$level,data$group),var)
1 2 3 4
6.800000 10.000000 4.000000 3.666667
> sapply(split(data$level,data$group),sum)
1 2 3 4
60 52 40 38
THE FOLLOWING DISPLAYS THE FITTED COEF AND T-TESTS:
> analysis=lm(level~group,data=data)
> summary(analysis)
Call:
lm(formula = level ~ group, data = data)
Residuals:
Min 1Q Median 3Q Max
-4.0 -1.5 -1.0 2.0 4.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.000 1.006 9.945 5.37e-08 ***
group2 3.000 1.590 1.887 0.0787 .
group3 -2.000 1.492 -1.341 0.1999
group4 -0.500 1.590 -0.314 0.7575
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.463 on 15 degrees of freedom
Multiple R-Squared: 0.3851, Adjusted R-squared: 0.2622
F-statistic: 3.132 on 3 and 15 DF, p-value: 0.05699
ONEWAY ANOVA:
> anova(analysis)
Analysis of Variance Table
Response: level
Df Sum Sq Mean Sq F value Pr(>F)
group 3 57.000 19.000 3.1319 0.05699 .
Residuals 15 91.000 6.067
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
HERE IS THE ANO VA TABLE, AND P=0.057
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