The below information contains the factors that influences absenteeism in the workplace that we are dealing with in this practical. The regression standard format that we will also provide with these factors helps us to understand technically these factors and to make a clear meaning of these factors economically. The randomly selected sample of 100 (one hundred) companies are going to help us to save time and money to actually use it as an estimate for the entire companies (population). This is the estimate of a regression model to examine the factors that influence employee absenteeism. The data was collected from 100 randomly selected companies. The key definitions are as follows.
Y = Average number of days absent per employee, X2 = Average employee wage, X3 = percentage of part time employees in a company, X4 = percentage of unionized employees in a company, D5 = 1 if shift work is available in a company (= 0 if it there is no shift work), D6 = 1 if union management relationship is good (=0 if it is not)
The regression standard format that helps us understand the above data economically is as follows.
Y = ß1 + ß2 X2+ ß3 X3 + ß4 X4+ ß 5D5+ ß6 D6 +ui
Towards the end of this report we shall see the effectiveness of this regression standard format. This standard format will work more effectively on the interpretation of the regression analysis. For now we are going to concentrate on Descriptive statistics. We firstly start with the interpretation of our scatterplots that we have generated from the observation of the factors of absenteeism. For a good and clear interpretation these scatterplots we shall include them in the appendix. However, we will briefly interpret their meaning in the body of this report.
Literature review on the economics of absenteeism
There are a lot of factors that influence absenteeism in the workplace, could it be the treatment they get from their bosses, the wages they get paid and the general atmosphere at work.... [continues]
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