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When the organizations’ infrastructure or environment is organized aptly, it will positively influence the employees. Employees are the crucial “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. “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” (Wandera 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. “Karl Pearson correlation coefficient measures quantitatively the extent to which two variables
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All these decisions involve huge investment, the benefits of which will be seen only in the long-term and these decisions are also irreversible in nature.
By nature of these projects, long-term sources of funds become the best suited means of financing.
he regression line cuts the vertical axis whereas the coefficients of the variable represents the slope of the regression line, the coefficient of price is – 22.49817 and this implies that for every increase of 1 in the x-axis the value on the y-axis will change by -22.49817.
The variables dealt in the data are: Sales per square metre which is referred by sales (also the target variable), Number of full timers which is referred by nfull, Number of part timers which is referred by npart, Total number of hours worked
This suggests a weak positive linear relationship between sales per square metre and number of full- timers.
Figure 2 shows the scatterplot between sales per square metre and number of part-timers. As shown in figure 2, there
The necessary transformation in this regression process is the binomial transformation using “mylogit” function. The most important variable in this case is c.vol, because the estimated volume of prostate cancer is the
The most important assumption for the OLS model is that the dat set need to follow a normal distribution. We therefore had to check whether our data set followed a normal distribution. It is clear that the data seems to come from a
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