Good Correlation And Regression Essay Example

1. Question A
I suggest using a simple linear regression to determine a relationship between annual salaries of maintenance technicians working at Atlanta International Airport (i.e. dependent variable y) and years spent by these technicians on their education (independent variable x).
The above-mentioned linear regression was built with the help of Excel software and is presented in Figure 1 below in the form of equation y =x * ß + c, where ß – coefficient, c – error term (i.e. ß and c are constant numbers).
Figure 1. Relationship of annual salaries and years spent on education by maintenance technicians working at Atlanta International Airport
According to Figure 1, the relationship between technicians’ annual income at Atlanta International Airport and duration of their educational background is rather strong, as the coefficient of determination is rather high (R2 = 0.85). The trend line also graphically supports that it is a strong relationship, as “the closer the points are to a straight line, the stronger the linear relationship between two variables” (Exlorable.com, n.d.).
Furthermore, the relationship between the presented annual income and duration of the education background is a positive linear relationship (as ß = 3.3648 > 0), meaning that the amount of average annual income rises as the number of years spent on education increases. Thus, according to the received linear regression, on average, every additional year of education increases annual income of a maintenance technician at Atlanta International Airport by 3.3648 thousand US dollars.
It is important to mention that the determined relationship may only be applicable to the maintenance technicians at Atlanta International Airport. Despite the common perception that income directly depends on an education level, the received results may not be directly applicable to the population in general, as the data selection was quite small and did not cover any other group of people except for the maintenance technicians at the selected airport.
2. Question B

Provided that the received linear regression is applicable to William, his expected annual salary will be:

y = 3.3648 * 16 – 10.97 = 42.867 thousand US dollars

The answer is USD 42,867.

3. Question C

Provided that the received linear regression is applicable to John, his expected annual salary will be:

y = 3.3648 * 5 – 10.97 = 5.854 thousand US dollars
The answer is USD 5,854.

References

Exlorable.com. (N.d.). Correlation and regression. Retrieved from https://explorable.com/correlation-and-regression