Analyzing the Workplace Regression Analysis

Workplace Regression Analysis

Give an example from your workplace regarding how regression analysis could be used by your leadership to analyze one or multiple outcomes that occur because of what your agency does. Indicate the dependent variable and at least two independent variables

To begin with, regression analysis is defined as the relationship between variables. referring to the example under consideration, the management in the workplace can use regression analysis to analyze the relationship of the tips received in the various servings compared to the corresponding amount of the bill. The dependent variable is the focus of the analysis. On the other hand, the independent variable is the 'variable that influences value of the dependent variables'. In particular, the management can use regression analysis to evaluate the impact of pricing on the behavior (tips offered) of consumers. For example, the management of the workplace can consider altering the prices on a number of products offered on different occasions. In this case, the data from the quantity sold and tips received for every price level can be employed to undertake a regression analysis. The dependent variable will be the quantity of the product sold. The independent variables will include the price of the product as well as the frequency of the consumers at the workplace (Hamel, 2016).

To what extent do you think the relationship between the d.v. and each i.v. is linear? Is it a positive or negative relationship?

There is a great extent of correspondence between the dependent variable, in this case quantity sold, and the independent variable, the price level. In this case, the relationship between the two variables is negative. We observe that Y, the dependent variable decreases as X, the independent variable increases. The possible explanation for such an occurrence is that an increase in prices usually decreases the amount of quantity sold. This is because the consumers find the products to be much more expensive. In a similar manner, there is a great extent of correspondence between the dependent variable, in this case quantity sold, and the independent variable, in this case, the frequency of the consumers. However, different from the first dependent variable (tips), there is a positive relationship between the two variables in the latter case. The explanation for this occurrence is that Y, the dependent variable increases as X, the independent variable increases. The reason being: an increase in the frequency of the consumers implies that more product is bought and therefore increases the total accrual from sales (Hamel, 2016).

Would this regression model be useful as a predictive tool for your organization? Explain

The regression would definitely be useful as a predictive tool for the organization. For one, the regression line attained would be able to help in depicting the magnitude to which the consumers reduce their purchase or consumption of the product as the prices increase. Therefore, this can function as a predictive tool for the organization in terms of pricing decisions in the future. In the same manner, the regression line can enable the organization to ascertain the durations in which more consumers visit their place, while also predicting the specific products requested by most of the consumers. Therefore, it convenient to predict the repeat order frequency and the specific products that will be purchased more compared to the others. For instance, if steak is purchased more compared to fish, then the organization will order more steak compared to fish on a daily basis. In addition, if more consumers frequent the place more during lunch hours, then the organization is able to predict the need for more waiters and employees for providing service at such hours (Hamel, 2016).

Intuitively, do you think multi-collinearity might exist among the independent variables?

Multicollinearity is occurrence when two or more than two predictor variables within a multiple regression model are significantly correlated. This implies that one variable can be linearly predicted from the other variables with a substantial accuracy. Intuitively, I believe that multicollinearity can exist among independent variables. In particular, this takes place when the correlation that exists between two or more independent variables equals 1 or -1. However, this sort of occurrence is not common (Hamel, 2016).

Are any of your variables categorical? If you, how would you structure your dummy variables?