Samples Science Relationship Between Two or More Independent Variables

# Relationship Between Two or More Independent Variables

887 words 3 page(s)

In science, independent variables are the variable in an experiment or study that affect the dependent variables. More than a single independent variable can be employed in a study to attempt to affect the independent variable. Regression analysis is used to predict or determine how changing one or more independent variables will affect a dependent variable. This can be done in several ways. Most often, data is conducted considering the effect on a dependent variable if the independent variable or variables remain fixed. It is also possible to determine how much an independent variable or variable must be altered to change the value of the dependent variable. This is known as the Necessary Condition Analysis (NCA) and it measures how much the independent variable or variables will have to be altered to make a scientifically significant change in the dependent variable.

Regression analysis can be used in predicting and analyzing alterations in the dependent variable, and also to describe how the independent variable affect the dependent variable both independently and together. The accuracy of regression analysis methods being utilized will be contingent on what it is meant to measure and how well that relationship is capable of being measured scientifically.

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An example of combining independent and dependent variables that could be tested in a business setting would include rate of hire (dependent variable) and quality of resume (independent variable) and quality of interview (independent variable). It can be assumed that a certain number of people will be hired to fill the given positions offered by a company—this is a dependent variable. What might determine how many people are hired might depend on the quality of resumes received and the quality of the interviews performed (independent variables). By conducting a regression analysis it could be determined that the higher the quality of resumes and the better the quality of interviews, the more potential hires there might be for that position, and the more people that might be hired.

Another potential set of dependent and independent variables in a business setting would be the quality of the questions on an application test (independent variable) and the quality of the people who wind up being interviewed for the position based on passing the test (dependent variable). Based on this scenario, a regression analysis could determine how important it is that the questions on an application test reflect the responsibilities of the job in question. If the questions do not accurately reflect the duties of the position, it can be assumed that the best candidates will not be recruited for the position based on their test results. Instead, the best possible candidates for the position might have been overlooked based on faulty test questions.

Both of these scenarios could be important in the business world, where hiring the best possible candidate for every position can save both time and money for the company. Replacing a new hire who is unfit for the job at hand costs a company not only time but also money to train a new person, update computer files, and possibly pay compensation to the first person who was hired.

In a previous position, I worked closely with people who tested application questionnaires to determine that they reflected the responsibilities of the job to be filled in the best possible manner. If a test question was determined to not reflect the actual necessary abilities of the job being filled, it was reworded or replaced. The entire application was accessed in this manner until it was determined that the test application best reflected the required abilities of the position to be filled.

Understanding the importance of dependent and independent variables and how best to use a regression analysis saves corporations both time and money. When in the midst of the hiring process, understanding how to choose the best candidates for the position being filled is of optimal importance. Regression analysis can be useful in this task by determining how the independent variables (accuracy of test questions, resume quality, and interview quality) affect the fit of the new candidate in the position he is hired to perform. This is far preferable to simply guessing at the results or hoping for the best when hiring a new candidate. Many aspects of business can be defined in scientific terms such as dependent variable and independent variable. It only makes sense that those in charge of businesses would use these tools to ensure the best possible outcome for all aspects of their companies. This does not end at the hiring process, of course, but can be used in almost every area of business, such as manufacturing, sales, and determining profit margins.

Science has long been an important part of running a business, and as more and more businesses opt to take advantage of it, more of them will benefit from it. Understanding the concepts of dependent and independent variables and how to use a regression analysis can not only simplify business but also make it more profitable and less time-consuming and labor-consumptive. What takes many employers to do can now be performed by employing relatively simple calculations that determine how various aspects of the business could and should be run. Understanding these facts can only simplify and improve the performance of businesses, provided they are used correctly and at the right times.

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