Regression problems arise in many contexts and industries. Understanding when and how to use regression techniques is important because these types of analyses can estimate the strength and direction of a relationship between two or more variables. Therefore, the potential business leader should be familiar with the appropriate applications and limitations of regression analysis in order to create reasonable analysis plans and communicate results to leadership to guide decision making. In this assignment, you will create and apply a regression model and address techniques used to construct the regression model, summarize the performance of the model, and suggest application of the model to improve product quality.
General Requirements:
Use the following information to ensure successful completion of the assignment:
Refer to “Wine Quality Data Set,” located in the Course Add-Ons for this course.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center. An abstract is not required.
This assignment requires that at least three scholarly research sources related to this topic, and at least one in-text citation from each source be included. Support for decisions should include appropriate current (within the last 3 years) or foundational, peer reviewed, and professional research.
You are required to submit this assignment to LopesWrite. Refer to the directions in the Student Success Center.
Directions:
Review “Wine Quality Data Set.”
Clean, prepare, and then split the data into data sets titled “Training” and “Test.”
Build a regression model in SPSS Modeler using the “Training” data set.
Write a paper (750-1,000 words) that addresses techniques used to construct the regression model, summarizes the performance of the model, and suggests application of the model to improve product quality. Include the following in your paper:
A summary of the model creation. Which regression technique did you use? Why? What variables are used in the model? Why? How well does the model fit the data? Explain.
A prediction of the wine quality that will be observed in the “Test” data set. What do you expect to find based on the regression model you created? Why?
An assessment of the performance of the regression model. If you compare the actual wine quality scores in the “Test” data set with your predicted wine quality scores based on the regression model, how well did the model perform? Was the prediction accurate? Is the prediction generalizable to other situations? Why or why not?