Correlation and Regression: Illustrated Examples

The learning materials were very resourceful for variable analysis and also in imparting skills required by a learner in conducting linear correlation and regression analysis. The lecture has managed to present adequate information in a simple, logical, and precise way. Following and understanding the content discussed has been highly enhanced through illustrated examples that guide each step. 

Most of the issues and principles have been well explained and illustrated. However, there are a few areas that were unclear. The first issue is the use of excel functions in computing significant values. The lecture has only addressed the use of a data analysis tool pack. Most of the versions used today have inbuilt formulas that can be applied and give similar results. Having a multidimensional approach is essential. Statistical tests used depend on the objective and nature of data. Inbuilt excel function can be useful in managing written content. 

Since performance rating and service have a significant effect on returns and revenue, linear regression of salary can be a viable tool to evaluate employer commitment to legal requirements, and the welfare of their employees. 

Multiple r for the analysis is 0.45 indicating the two variables can explain only 45% of salary-related issues. However, adjusted and standardized value for R-square is 0.21, accounting for 21% of the possible effect on salary. Considering the significance of revenue from sustainable performance and value for investment quality of services and pay, the test statistics obtained are meager. The government and involved institutions should be more engaged in ensuring policies are implemented.