Annual salary analysis.
Introduction
The main aim of this paper is to provide an analysis of a sample of annual salaries for recently hired plant operators at a chemical manufacturing company. The data set used in this study was retrieved from the annual salaries of 12 hired plant operators.
Descriptive Statistics Analysis
This section describes the data set in terms of measures of central tendencies as well as measures of variation. The information enables us to get more meaning information from the raw data collected from the research study (Bickel & Lehmann, 2012).
The table below gives a summary of descriptive statistics.
| Annual Salary | |
| Mean | 75195.91667 |
| Standard Error | 1352.863083 |
| Median | 74840 |
| Mode | #N/A |
| Standard Deviation | 4686.455192 |
| Sample Variance | 21962862.27 |
| Kurtosis | -1.436042927 |
| Skewness | -0.092260443 |
| Range | 13699 |
| Minimum | 67956 |
| Maximum | 81655 |
| Sum | 902351 |
| Count | 12 |
Table 1: Descriptive Statistics for Annual Salary
- Measures of central tendency
This entails; mean, median, and mode.
From the results in the table above, the mean and median of annual salaries of hired plant operators are 75195.92 and 74840, respectively. The data set has no mode.
The results show that the distribution of the data set is negatively skewed.
- Measures of variation
This comprises of variance, standard deviation, and range.
From the results in table 1 above, we can see that the difference between the salary of the plant operator who earns the highest amount annually and counterpart who earns the lowest annually (range) is 13699. The variance and standard deviation are 21962862.27 and 4686.46, respectively. The standard deviation gives information about how data set spreads around the mean. From the obtained results, we can see that the standard deviation value is large; therefore, it tells us that the data set is widely spread out from the mean.
Conclusion
The aim of writing this paper was to get more information about the dataset of 12 hired plant operators of a chemical manufacturing company from the analysis of descriptive statistics of the data. The results show that the data set is negatively distributed and data values widely spread out away from the mean. Therefore, the use of large sample size is recommended in future studies on the same subject in order to obtain more accurate results.
Reference
Bickel, P. J., & Lehmann, E. L. (2012). Descriptive statistics for nonparametric models. III. Dispersion. Selected works of EL Lehmann (pp. 499-518). Springer, Boston, MA