The paper "Statistics of Employees " is a perfect example of a statistics case study. This paper employees various statistical tools to study information on 474 employees working for a large organization in Wales. It emerged that most employees are clerks while those working in administration, technical and security make up 8%, 7%, and 6% respectively of the total employees. In the same line, only 2% of the employees are executive members. The distribution of the beginning salary of Apple’ s employees is positively skewed with most people earning low salaries while few individuals earn an extremely high beginning salary.
Additionally, there is a negative and weak relationship between salary now and age given a correlation of However a correlation coefficient of -0.0096 between beginning salary and age paints a picture that the two variables do not have any relationship. Further analysis of salary now and salary at the beginning shows that the variables are positively related. In conclusion, the gender of employees in the company and educational level are interdependent such that males in the company tend to hold higher educational qualifications compared with their female counterparts. Introduction The first task is to summarize the distribution of the data using pie charts, bar graphs, and frequency distribution curves.
In a bid to understand the shape of the distribution, histograms and polygons will be constructed. The second section of the paper will comprise the construction of scatterplots and coefficients of correlation between various pairs of variables. The third task is to utilize probability to draw inferences on the interdependence between Educational Level and Sex in the company. Lastly, the concept of normal distribution will be employed in finding the probability now of earning at least £ 15,000 for males as well as for females. Task 1: Summarizing Data The table below gives the classification of each variable and states whether the variable is quantitative or qualitative (Glaser, 2005). Variable Classification Quantitative or Qualitative IDNUM Ordinal scale Categorical variable SALBEG Ratio scale Quantitative variable, it is possible to measure and order proportions SEX Nominal scale because they are discrete in nature Qualitative variable AGE Ratio scale Quantitative variable, it is possible to measure and order proportions
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