Regression

As our research comes to a close regarding the Wage Data Set that we have used throughout our research courses we take a closer look at two areas that need to be considered: Years of Experience versus Annual Income throughout the clerical industry. We have compared these two areas using tubular graphs and as our research techniques improve we will be using the linear graph, showing either an increase in pay of a decrease in pay.

We are going to take a look at just a few data cells and see if there is an increase or decrease in pay and see if there is a pattern. After reviewing the data we noticed that a person with five years of work experience is making more in annual income than that of an individual that has twenty-two years of experience.

We have included a linear graph for review. The linear graph seen here demonstrates the statistical relationship between years of experience and wages earned for the wage data set that we downloaded in RES 341. This data can be used in conjunction with any type of trend model that would suit the users needs to correlate forecasts of income expected for years of experience. The trend data can be used by both the employer and employee. This is the kind of data one would expect to be asked at an interview; the “loaded question” that most people do not know how to calculate. For example, the interview is going fine, then the potential employer asks, What salary would you expect if you were to get the job in question? Most individuals would opt for a number they can tolerate to live by rather than what is actually statistically correct. If an individual with fourteen years of experience used this data they can confidently say they expect to earn at least thirty-two thousand dollars a year at the position in question and may even provide the data below to show that they are active and knowledgeable in the industry.

Linear regression gives us the opportunity to see how two variable affect each other. In our case we saw how years of experience affected the annual salary of a person in the clerical industry. For anyone looking into moving forward into a new field of employment, linear regression could be a huge tool with the right data to identify where they lie inside a typical salary range. For instance, if some is making $55,000 for ten years of experience versus you are given a offer for $45,000 for the same years of experience you would be able to identify that compared to others in the same field you are being under paid. It looks like it’s time to make a counter offer. From our linear regression equation we have the ability to also predict what the variable for y would be when we have x. For a participant using our equation they can input there number of years experience and then determine what they should make.

While our overall graph does show an increase in annual salary versus the increasing