Assess the use of various decision support tools
A decision support tool is a computerized application that is meant for assisting analysis experts within an organization to decide on the different activities of the organization. These tools usually rely on data, communication support, documents, models and knowledge so as to facilitate the decision making procedures (Albright & Winston, 2016). There are several decision support tools and we are going to discuss a number of them (their categories).
Data driven tools
These are aimed at assisting the managerial personnel in making certain decisions. Basically, since these tools need data to perform their function, a data warehouse or database should be available from where the data will be drawn. These tools should have the capability to request for stored data from a database.
Communication driven tools
This category of tools involves the application of the client server or web support to facilitate the communications. They are usually meant to be used internally to assist in facilitating meetings and unifying teams (Albright & Winston, 2016). Examples of communication driven tools are messaging applications, chat forums, teleconferencing and online team-activities.
Knowledge driven tools
For these tools, there is a knowledge base that the tool relies on, and they usually target diverse users and information systems within a firm. These tools are effectual in providing a way of advising the management and also in enabling the best choice of services and products. They rely on server applications and applications that are supported as a standalone.
Model driven tools
These are more advanced tools that assist in selecting the best options and analyzing the different presented decisions (Albright & Winston, 2016). The use of the respective model driven tool solely depends on the how the respective model has been designed. For instance, a model can be meant for forming schedules. They are used over the web or server/client application systems.
Explain why outliers are sometimes called influential observations
Outliers are usually points (values of data) that are off the overall design/plot (best fit) of a given data set. The reason as to why they are occasionally termed as influential observations is that their magnitude influences the gradient for a given regression design/pattern/equation.
Discuss what could happen to the slope of a regression of Y versus a single X when an outlier is included versus when it is not included. Will this necessarily happen when a point is an outlier?
When an outlier is let to be part of the regression pattern, the resulting slope for the plot is flatter as compared to when the outliers are not part of the plot. As such, it becomes evident that outliers influence the regression models (Albright & Winston, 2016). For example, a model with outliers can have a slope of -3 while that does not have outliers have a slope of -2. In order to know whether outliers really affect the regression design, we can remove them and compare the gradient values with/without outliers. The moment a significant value from the given data set is included in the design, the slope changes. It is also important to understand that the extent of influence outliers on the given regression plot depends on the size of the data-set. When the set has many values, the effect reduces and vice versa. The same effect is not significant when a point is among the outliers.
Albright, S., & Winston, W. (2016). Business Analytics: Data Analysis & Decision Making. Boston, MA: Cengage Learning.