# An Assessment of Environmental RF Noise Due to IoT Deployment
## 7 Statistical Analysis  
For a descriptive statistical analysis, this paper focuses on the measures of central tendency and measure of variability, with an interest in statistical metrics, such as mean, standard deviation, variance, kurtosis, and skewness. The kernel density estimate (KDE) function explored the observation distributions. The average, median, and mode represent measures of central tendency. The standard deviation informs of measures of variability or dispersion from the center [37]. The skewness value characterizes the shape of the distribution: it is symmetrical when the skewness is nil and has a right tail or is skewed to the right when the skewness is a positive value. Otherwise, the distribution has a left tail or is skewed to the left when the skewness is negative. A kurtosis value of zero indicates normality. Positive kurtosis relates to a peaked distribution, and negative kurtosis obtains a flatter distribution [38].  
The analysis considered three features: the candidate sites, frequency bands, and transmit rates (from Tx0 to Tx10). This survey produced 165 sets of raw data, each requiring analysis of the mean, standard deviation, variance, kurtosis, skewness, median, first and third quartiles, interquartile range, and minimum and maximum scores of the boxplot. Tables 4 and 5 present these metrics obtained from the NGM-868 MHz case for brevity. Table 4 demonstrates the positive and slightly high values for skewness. High kurtosis expects samples to have slightly right-tailed and peaked distributions. The standard deviations and variations were very close, indicating that the data had minimum deviations from the mean values. An analysis exploited the elements of the boxplot in Table 5 to remove the outliers.  
The initial density and boxplot graphs, not shown in this paper for brevity, demonstrate the presence of outliers in the datasets. To filter out the outliers, the process required an evaluation of the elements of the boxplots, such as the lower fence (LF) or min score, the 1 st quartile at the 25 th percentile, the arithmetic median, the 3 rd quartile at the 75 th  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Site** & **Band** & **Stat.** & **T\(\_\)0** & **Tx1** & **T\(\_\)2** & **Tx3** & **T\(\_\)4** & **T\(\_\)5** & **T\(\_\)6** & **T\(\_\)7** & **T\(\_\)8** & **T\(\_\)9** & **T\(\_\)40** \\ \hline NGM & 868 MHz & Mean & 0.589115 & 0.589247 & 0.589307 & 0.589345 & 0.589376 & 0.589327 & 0.589551 & 0.589254 & 0.589254 & 0.589254 & 0.589254 & 0.589254 \\ \cline{2-11}  & Std & 0.002647 & 0.017835 & 0.021365 & 0.023126 & 0.024879 & 0.022509 & 0.032495 & 0.017900 & 0.017900 & 0.017900 \\ \cline{2-11}  & Var & 0.000007 & 0.000318 & 0.000456 & 0.000535 & 0.000619 & 0.000507 & 0.001056 & 0.000320 & 0.000320 & 0.000320 & 0.000320 \\ \cline{2-11}  & Kurt & 56.083614 & 355.496605 & 265.71541 & 274.107725 & 222.43288 & 856.255615 & 172.717403 & 142.075992 & 142.075992 & 142.075992 \\ \cline{2-11}  & Skew & \(-\)0.109029 & 7.155354 & 6.436797 & 7.073440 & 6.198063 & 142.21488 & 5.956075 & 3.099585 & 3.099585 & 3.099585 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical characteristics of IoT radio noise at NGM-868 MHz.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Site** & **Band** & **Stat.** & **T\(\_\)0** & **Tx1** & **T\(\_\)2** & **Tx3** & **T\(\_\)4** & **T\(\_\)5** & **T\(\_\)6** & **T\(\_\)7** & **T\(\_\)8** & **T\(\_\)9** & **T\(\_\)40** \\ \hline NGM & 868 MHz & Min Score & 0.588320 & 0.583504 & 0.58