Table 2. Tests of normality results.
Tests of normality | ||||||||
---|---|---|---|---|---|---|---|---|
NK cell values | Kolmogorov-Smirnova | Shapiro-Wilk | ||||||
Statistic | df | Sig. | Statistic | df | Sig. | |||
1 | 0.15 | 71.00 | 0.00 | 0.85 | 71.00 | 0.00 | ||
2 | 0.11 | 71.00 | 0.03 | 0.89 | 71.00 | 0.00 | ||
3 | 0.17 | 71.00 | 0.00 | 0.79 | 71.00 | 0.00 | ||
4 | 0.19 | 71.00 | 0.00 | 0.82 | 71.00 | 0.00 | ||
5 | 0.15 | 71.00 | 0.00 | 0.91 | 71.00 | 0.00 | ||
6 | 0.13 | 71.00 | 0.00 | 0.88 | 71.00 | 0.00 | ||
7 | 0.15 | 71.00 | 0.00 | 0.87 | 71.00 | 0.00 | ||
8 | 0.14 | 71.00 | 0.00 | 0.85 | 71.00 | 0.00 | ||
9 | 0.16 | 71.00 | 0.00 | 0.86 | 71.00 | 0.00 | ||
10 | 0.15 | 71.00 | 0.00 | 0.91 | 71.00 | 0.00 | ||
11 | 0.10 | 71.00 | 0.06 | 0.98 | 71.00 | 0.17 | ||
12 | 0.10 | 71.00 | 0.06 | 0.98 | 71.00 | 0.17 | ||
13 | 0.09 | 71.00 | 0.20* | 0.98 | 71.00 | 0.24 | ||
14 | 0.19 | 71.00 | 0.00 | 0.82 | 71.00 | 0.00 | ||
15 | 0.21 | 71.00 | 0.00 | 0.81 | 71.00 | 0.00 | ||
16 | 0.21 | 71.00 | 0.00 | 0.81 | 71.00 | 0.00 | ||
17 | 0.08 | 71.00 | 0.20* | 0.98 | 71.00 | 0.52 | ||
18 | 0.07 | 71.00 | 0.20* | 0.99 | 71.00 | 0.57 | ||
19 | 0.23 | 71.00 | 0.00 | 0.56 | 71.00 | 0.00 | ||
20 | 0.23 | 71.00 | 0.00 | 0.55 | 71.00 | 0.00 | ||
21 | 0.19 | 71.00 | 0.00 | 0.84 | 71.00 | 0.00 | ||
22 | 0.18 | 71.00 | 0.00 | 0.85 | 71.00 | 0.00 | ||
23 | 0.18 | 71.00 | 0.00 | 0.76 | 71.00 | 0.00 | ||
24 | 0.17 | 71.00 | 0.00 | 0.78 | 71.00 | 0.00 | ||
25 | 0.11 | 71.00 | 0.05 | 0.96 | 71.00 | 0.02 | ||
26 | 0.10 | 71.00 | 0.07 | 0.96 | 71.00 | 0.02 | ||
27 | 0.43 | 71.00 | 0.00 | 0.14 | 71.00 | 0.00 | ||
28 | 0.43 | 71.00 | 0.00 | 0.14 | 71.00 | 0.00 | ||
29 | 0.09 | 71.00 | 0.20* | 0.97 | 71.00 | 0.11 | ||
30 | 0.08 | 71.00 | 0.20* | 0.97 | 71.00 | 0.10 | ||
31 | 0.23 | 71.00 | 0.00 | 0.75 | 71.00 | 0.00 | ||
32 | 0.23 | 71.00 | 0.00 | 0.75 | 71.00 | 0.00 |
*. This is a lower bound of the true significance.
Those values in bold are of those features whose data is normally distributed.
If the , we can accept the null hypothesis, that there is no statistically significant difference between the data and the normal distribution, hence we can presume that the data of those features are normally distributed.
If the , we can reject the null hypothesis because there is a statistically significant difference between the data and the normal distribution, hence we can presume that the data of those features are not normally distributed.