BUSN311 – Quantitative Methods and Analysis



Different combinations of variables are tested using regression analysis to determine the relationship between them. The analysis produces different types of results which can be interpreted to gain insight. Such insight can be used by organizations to predict future outcomes thus ensuring efficiency in operations and ultimately, success (Chatterjee and Hadi, 2015).

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Unit 5 Individual Project


            Regression analysis is significant in determining whether there is an existing relationship between different variables. If such a relationship exists, then an equation can be derived which can be used for future predictions (Chatterjee and Hadi, 2015). The results obtained from this analysis can be applied across many fields such as in different organizations.


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Using Regression

When it comes to regression analysis, there are two types of variables involved which include the dependent and independent variable. These variables are combined to determine the relationship that exists between them (Shyu, Grosse, & Cleveland, 2017).

One of such combination of variables that I can test using regression is the amount of food that I eat and weight. Regression, in this case, can be used to examine the relationship that exists between the amounts of foods that one takes and their weight. The dependent variable here is weight while the independent or manipulated variable is the amount of food. In this example, it is expected that an increase in the food taken would lead to an increase in the weight and vice versa. The type of result is thus a linear correlation. A collected set of data on the two variables can, therefore, be used to come up with a linear equation containing the necessary coefficients and the margin of error associated with the variables. I can then use this information to predict how much food I need to eat to get to a certain weight, or even how much I need to reduce my food intake to reduce to a certain weight. However, such a correlation assumes that I hold all the factors that affect my weight as a constant. Thus for my regression to hold, factors such as the amount of exercise and quality of food must be kept at a constant.

At times, an event can be affected by more than one variable rather than just one as in the above example (Shyu, Grosse, & Cleveland, 2017). For instance, my cost of living can be affected by various factors including whether I am married and my age. In this case, there are two different independent valuables which are age and marital status. The dependent variable will be the cost of living. It is expected that an increase in age will cause an increase in the cost of living while a change in marital status leads to a change in the cost of living. This type of result is also a linear correlation but unlike in the first example, the linear equation will contain two independent varies and more than one coefficient. I can find this information useful while attempting to predict how my expenses will change as I progress with life. Such insights can assist me to make meaningful decisions in life and predict how much my expenses will be at a given time in the future. This can come in handy in making significant decisions such as saving plans.

A different combination is between employee salary and sales volume. Given that the salary is the independent variable while sales are the dependent variable. As a manager, I might be interested to know how varying the salary of my employees will vary sales volume. It can be expected that when employees’ salary is increased, then they are likely to be motivated to make more sales. From this derived relationship, as a manager, I can make informed decisions on whether to increase the salary and additionally, by how much in order to attain my projected sales.

Thus, it can be seen that results from regression analysis can be used for gaining insight that is valuable to the running of an organization or even for individual purposes. Also, the identification of errors is made much simpler hence avoiding losses. Future opportunities can also be seized through this analysis while risks can be predicted. The areas that require some level of improvement can also be identified thus enhancing the efficiency of a company.

Organizational application

One of such applications in an organization is in sales and advertising. In this case, a regression analysis can be applied to find out how sales relate to advertising. In other words, the question is how does advertising affect volumes of sales? Thus, the dependent valuable in this case is sales and the independent one is advertising. Deriving from this, advertising can be manipulated to predict how sales respond. The type of result from such an analysis is a linear correlation. It is clear that if all other factors are held constant, then an increase in advertising is likely to lead to an increase in sales volume, hence, a linear equation as a result. First of all, from this outcome, I am able to establish that there indeed is a relationship between these variables. I am also able to come up with a mathematical equation that I can compute in order to attain the sales outcome. An example of such a linear equation is Y= c + mX+ e where Y is the value of sales (the dependent variable), X is the value of advertising (independent variable), m is a gradient coefficient, c is a constant value, while e represents the margin of error (Lewis-Beck & Lewis-Beck, 2015). Therefore, given the constant value, the ‘m’ coefficient, the error value, and the value of advertising, I can easily predict the value of sales (Y). Consequently, I can be able to predict how much money they can put into advertising costs in order to attain certain volumes of sales.

Making Prediction

I will depict a situation in a company that I am quite familiar with and that has raised a lot of controversy around the world. Facebook is one of the leading global organizations and it is being faced with a situation that it needs to attentively look into. The scandal facing this company is that of data privacy which a very sensitive topic among its users is. It was noted that following the scandal, the company’s share prices dropped considerably depicting a huge problem (Sanders and Patterson, 2018). Given that the scandal is as a result of data privacy issues, it can, therefore, be suggested that there are variables which in this case have a relation. These are data privacy and profits. It appears that a change in the amount of privacy offered by Facebook triggers a response on the amount of the organization’s profit. Thus taking profits as the dependent variable, and data privacy as the independent variable, the company can come up with a useful result. Expectedly, the result of this analysis would be a linear correlation. A linear equation can be obtained which will help to predict future profits given the amount of privacy, given that all the other factors are held at a constant.

Results from this regression analysis would help the organization to make strategic decisions on their data policies since they will gain valuable insights about the issue at hand i.e. the issue of privacy. This will, in turn, ensure that the company avoids such mistakes and scandals in the future. If they choose to ignore such an analysis, then I have insight that Facebook will end up losing a lot of its users which will have a negative impact both in the short term and the long term on the company’s profits.


The importance of regression analysis cannot be underestimated. It offers a means to predict future outcomes which facilitate informed decision making across many fields. It can be said that regression is a major tool for success in any organization.


Chatterjee, S., & Hadi, A. S. (2015). Regression analysis by example. John Wiley & Sons.

Lewis-Beck, C., & Lewis-Beck, M. (2015). Applied regression: An introduction (Vol. 22). Sage publications.

Sanders, J., & Patterson, D. (2018). Facebook Data Privacy Scandal: A Cheat Sheet. TechRepublic. Retrieved from https://www.techrepublic.com/article/facebook-data-privacy-scandal-a-cheat-sheet/

Shyu, W. M., Grosse, E., & Cleveland, W. S. (2017). Local regression models. In Statistical models in S (pp. 309-376). Routledge.


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