Hi
I have a dataset of the type attached.
Here's my code thus far.
dataset <-data.frame(read.delim("data", sep="\t", header=TRUE));
newData<-subset(dataset, select = c(Price, Reliability, Mileage, Weight,
Disp, HP));
cor(newData, method="pearson");
Results are
Price Reliability Mileage Weight Disp
HP
Price 1.0000000 NA -0.6537541 0.7017999 0.4856769
0.6536433
Reliability NA 1 NA NA NA
NA
Mileage -0.6537541 NA 1.0000000 -0.8478541 -0.6931928
-0.6667146
Weight 0.7017999 NA -0.8478541 1.0000000 0.8032804
0.7629322
Disp 0.4856769 NA -0.6931928 0.8032804 1.0000000
0.8181881
HP 0.6536433 NA -0.6667146 0.7629322 0.8181881
1.0000000
It appears that Wt and Price, Wt and Disp, Wt and HP, Disp and HP, HP and
Price are strongly correlated.
To find the statistical significance,
I am trying sample.correln<-cor.test(newData$Disp, newData$HP,
method="kendall", exact=NULL)
Kendall's rank correlation tau
data: newx$Disp and newx$HP
z = 7.2192, p-value = 5.229e-13
alternative hypothesis: true tau is not equal to 0
sample estimates:
tau
0.6563871
If I try the same with
sample.correln<-cor.test(newData$Disp, newData$HP, method="pearson",
exact=NULL)
I get Warning message:
In cor.test.default(newx$Disp, newx$HP, method = "spearman", exact = NULL) :
Cannot compute exact p-value with ties
> sample.correln
Spearman's rank correlation rho
data: newx$Disp and newx$HP
S = 5716.8, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.8411566
I am not sure how to interpret these values.
Basically, I am trying to figure out which combination of factors
influences efficiency.
Thanks
Lalitha
Price Country Reliability Mileage Type Weight Disp. HP
8895 USA 4 33 Small 2560 97 113
7402 USA 2 33 Small 2345 114 90
6319 Korea 4 37 Small 1845 81 63
6635 Japan/USA 5 32 Small 2260 91 92
6599 Japan 5 32 Small 2440 113 103
8672 Mexico 4 26 Small 2285 97 82
7399 Japan/USA 5 33 Small 2275 97 90
7254 Korea 1 28 Small 2350 98 74
9599 Japan 5 25 Small 2295 109 90
5866 Japan NA 34 Small 1900 73 73
8748 Japan/USA 5 29 Small 2390 97 102
6488 Japan 5 35 Small 2075 89 78
9995 Germany 3 26 Small 2330 109 100
11545 USA 1 20 Sporty 3320 305 170
9745 USA 1 27 Sporty 2885 153 100
12164 USA 1 19 Sporty 3310 302 225
11470 USA 3 30 Sporty 2695 133 110
9410 Japan 5 33 Sporty 2170 97 108
13945 Japan 5 27 Sporty 2710 125 140
13249 Japan 3 24 Sporty 2775 146 140
10855 USA NA 26 Sporty 2840 107 92
13071 Japan NA 28 Sporty 2485 109 97
18900 Germany NA 27 Compact 2670 121 108
10565 USA 2 23 Compact 2640 151 110
10320 USA 1 26 Compact 2655 133 95
10945 USA 4 25 Compact 3065 181 141
9483 USA 2 24 Compact 2750 141 98
12145 Japan/USA 5 26 Compact 2920 132 125
12459 Japan/USA 4 24 Compact 2780 133 110
10989 Japan 5 25 Compact 2745 122 102
17879 Japan 4 21 Compact 3110 181 142
11650 Japan 5 21 Compact 2920 146 138
9995 USA 2 23 Compact 2645 151 110
15930 France NA 24 Compact 2575 116 120
11499 Japan/USA 5 23 Compact 2935 135 130
11588 Japan/USA 5 27 Compact 2920 122 115
18450 Sweden 3 23 Compact 2985 141 114
24760 Japan 5 20 Medium 3265 163 160
13150 USA 3 21 Medium 2880 151 110
12495 USA 2 22 Medium 2975 153 150
16342 USA 3 22 Medium 3450 202 147
15350 USA 2 22 Medium 3145 180 150
13195 USA 3 22 Medium 3190 182 140
14980 USA 1 23 Medium 3610 232 140
9999 Korea NA 23 Medium 2885 143 110
23300 Japan 5 21 Medium 3480 180 158
17899 Japan 5 22 Medium 3200 180 160
13150 USA 2 21 Medium 2765 151 110
14495 USA NA 21 Medium 3220 189 135
21498 Japan 3 23 Medium 3480 180 190
16145 USA 3 23 Large 3325 231 165
14525 USA 1 18 Large 3855 305 170
17257 USA 3 20 Large 3850 302 150
13995 USA NA 18 Van 3195 151 110
15395 USA 3 18 Van 3735 202 150
12267 USA 3 18 Van 3665 182 145
14944 Japan 5 19 Van 3735 181 150
14929 Japan NA 20 Van 3415 143 107
13949 Japan NA 20 Van 3185 146 138
14799 Japan NA 19 Van 3690 146 106
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