Project Statistics Example | Topics and Well Written Essays - 2000 words - 1

Statistics Project Example When the organizations’ infrastructure or environment is organized aptly, it will positively influence the employees. Employees are the crucial â€Å"cog† for the organizational functioning and success. This significance of employees was put forward by Mayhew (2014) who stated that the objective of any organization is profitability; and that profitability and thereby organizations success depends on the employees performance, with poor performance by the employees being detrimental to the companys success. Employees work in an organization on full-time basis as well as short-term basis. Although, full-time employees are the majority in any organization, employment of short-term employees are also on the rise. â€Å"The use of temporary workers is growing rapidly, with the number of companies using temporary workers on the increase as global competition increased and the urge to cut down on costs of undertaking businesses in order to remain competitive rises† (Wan dera 2011). This role of both full-time and short-term workers brings in focus the number of hours they contribute to the organization (Simeon 2013). So, the report will focus on the data collected from 400 fashion stores located in the Netherlands thereby discussing those stores’ infrastructure, employees including full-timers and part-timers, the hours contributed by them and others. As above-mentioned, the data is regarding the study of direct annual sales of 400 Dutch fashion stores in the year 1990. The quantitative variables used are: Total Sales (tsales), Sales per square meter (sales), Number of full-times (nfull), Number of part-times (npart), Total number of hours worked (hoursw) and Sales floor space of the store in square metres (ssize). Since all of them are quantitative variables, the Karl Pearson correlation coefficient for continuous variables is calculated and tested for its significance. â€Å"Karl Pearson correlation coefficient measures quantitatively the extent to which two variables