Statistics Study - Essay Example

several measured or binomial variables Multiple- linear regression or Multiple non-linear regression   Multiple logistic regression Cox proportional hazard regression Source: http://www.graphpad.com/support/faqid/1790/ The multiple regression can be done to portray the impact of both age and gender on the time spent to complete an assignment. Multiple regression tests how age and gender will change and predicts the time spent to complete an assignment. The linear regression equation is as follows: Y=bo+b1X1+b2X2, where Y is the completion time and X1 and X2 are age and gender.

bo is the time when the predictor variable is zero. Values b1 and b2 are the regression coefficients. The regression coefficients show the change in time due to change in gender and age (Bottenbery, 2011). Ballard Integrated Managed Services Inc Pt.2 These sections contain an improved survey of the first one that was well planned and marketed. The survey was based on the importance of understanding the dissatisfaction of the current employees and the increase of their rate of turnover. Another survey tool was designed.

The questionnaires were distributed among the senior managers. The top management was invited to complete the research. The material was read for their understanding and for the flow of real completeness and actual wording (Triola, 2008). The question phrases and new items were incorporated in the design of the survey. The revised questionnaires were distributed among the senior management team. Before using the consensus of the group, they consented to the revised survey saying it was ready to be administered.

With the help of five craft workers, they pre-tested the instrument. The pre-testers were on leave, so they comfortably conducted the review. The craft workers were used to avoid bias. Their inputs were incorporated in the final design of the paper. After consulting it was decided necessary to interview employees who no longer work with the organization. Their information would be useful in determining who would quit the organization in the future (Triola, 2008). Their response would help the company develop a model that would predict the future voluntary termination in the organization.

The goal of the study was to create a statement of regression that would predict future resignation. The information would be useful in identifying the areas that are of concern to those employees that left. Therefore, frequencies and descriptive statistics were necessary. Because the objective of the firm was to improve morale and reduce turnover of employees, the mentioned crucial areas would be core attention to developing internal human