Multiple regression for predictive modeling | D208 | Western Governors University

As a data analyst, you will assess continuous data sources for their relevance to specific research questions throughout your career.

In your previous coursework, you have performed data cleaning and exploratory data analysis on your data. You have seen basic trends and patterns and now can start building more sophisticated statistical models. In this course, you will use and explore both multiple regression and logistic regression models and their assumptions.

For this task, you will select one of the Data Sets and Associated Data Dictionaries from the following link:

Data Sets and Associated Data Dictionaries

You will then review the data dictionary related to the raw data file you have chosen, and prepare the data set file for multiple regression modeling. The organizations connected with the given data sets for this task seek to analyze their operations and have collected variables of possible use to support decision-making processes. You will analyze your chosen data set using multiple regression modeling, create visualizations, and deliver the results of your analysis. It is recommended that you use the cleaned data set from your previous course.

Note: The link to the data files can also be found below in the web links section. If you have trouble accessing the link, copy and paste the link directly into your web browser.


Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. The originality report that is provided when you submit your task can be used as a guide.

You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.

Tasks may not be submitted as cloud links, such as links to Google Docs, Google Slides, OneDrive, etc., unless specified in the task requirements. All other submissions must be file types that are uploaded and submitted as attachments (e.g., .docx, .pdf, .ppt).

Part I: Research Question

A.  Describe the purpose of this data analysis by doing the following:

1.  Summarize one research question that is relevant to a real-world organizational situation captured in the data set you have selected and that you will answer using multiple regression.

2.  Define the objectives or goals of the data analysis. Ensure that your objectives or goals are reasonable within the scope of the data dictionary and are represented in the available data.

Part II: Method Justification

B.  Describe multiple regression methods by doing the following:

1.  Summarize the assumptions of a multiple regression model.

2.  Describe the benefits of using the tool(s) you have chosen (i.e., Python, R, or both) in support of various phases of the analysis.

3.  Explain why multiple regression is an appropriate technique to analyze the research question summarized in Part I.

Part III: Data Preparation

C.  Summarize the data preparation process for multiple regression analysis by doing the following:

1.  Describe your data preparation goals and the data manipulations that will be used to achieve the goals.

2.  Discuss the summary statistics, including the target variable and all predictor variables that you will need to gather from the data set to answer the research question.

3.  Explain the steps used to prepare the data for the analysis, including the annotated code.

4.  Generate univariate and bivariate visualizations of the distributions of variables in the cleaned data set. Include the target variable in your bivariate visualizations.

5.  Provide a copy of the prepared data set.

Part IV: Model Comparison and Analysis

D.  Compare an initial and a reduced multiple regression model by doing the following:

1.  Construct an initial multiple regression model from all predictors that were identified in Part C2.

2.  Justify a statistically based variable selection procedure and a model evaluation metric to reduce the initial model in a way that aligns with the research question.

3.  Provide a reduced multiple regression model that includes both categorical and continuous variables.

Note: The output should include a screenshot of each model.

E.  Analyze the data set using your reduced multiple regression model by doing the following:

1.  Explain your data analysis process by comparing the initial and reduced multiple regression models, including the following elements:

•  the logic of the variable selection technique

•  the model evaluation metric

•  a residual plot

2.  Provide the output and any calculations of the analysis you performed, including the model’s residual error.

Note: The output should include the predictions from the refined model you used to perform the analysis. 

3.  Provide the code used to support the implementation of the multiple regression models.

Part V: Data Summary and Implications

F.  Summarize your findings and assumptions by doing the following:

1.  Discuss the results of your data analysis, including the following elements:

•  a regression equation for the reduced model

•  an interpretation of coefficients of the statistically significant variables of the model

•  the statistical and practical significance of the model

•  the limitations of the data analysis

2.  Recommend a course of action based on your results.

Part VI: Demonstration

G.  Provide a Panopto video recording that includes all of the following elements:

•  a demonstration of the functionality of the code used for the analysis

•  an identification of the version of the programming environment

•  a comparison of the two multiple regression models you used in your analysis

•  an interpretation of the coefficients.

Note: The audiovisual recording should feature you visibly presenting the material (i.e., not in voiceover or embedded video) and should simultaneously capture both you and your multimedia presentation.

Note: For instructions on how to access and use Panopto, use the “Panopto How-To Videos” web link provided below. To access Panopto’s website, navigate to the web link titled “Panopto Access,” and then choose to log in using the “WGU” option. If prompted, log in using your WGU student portal credentials, and then it will forward you to Panopto’s website.

To submit your recording, upload it to the Panopto drop box titled “Multiple Regression Modeling – NBM2 | D208.” Once the recording has been uploaded and processed in Panopto’s system, retrieve the URL of the recording from Panopto and copy and paste it into the Links option. Upload the remaining task requirements using the Attachments option.

H.  List the web sources used to acquire data or segments of third-party code to support the application. Ensure the web sources are reliable.

I.  Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized.

J.  Demonstrate professional communication in the content and presentation of your submission.

Order a unique copy of this paper
(550 words)

Approximate price: $22

Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

We value our customers, and so ensure that our papers are 100 percent original. Our Team of professionals does not miss the mark; they ensure that step by step each paper is written uniquely. We never duplicate or work as we compare papers rest assured. We deliver our work a day before time to ensure that you don’t miss your deadlines. It is not only doing the work but delivering it at the right time. We capture the consequences of late remittances. .

Money-back guarantee

We value customer satisfaction here at and make sure that you get the best value for your Money. It happens that sometimes you can pay twice for your order or may want to cancel it, or you feel that it doesn’t meet your requirements; our money back guarantee will give you the opportunity to get back your money. We will also refund 100% of money paid double. In case your paper does not satisfy your requirements , we request that you notify us via writing within 2 days otherwise on the third day we will assume that you have been satisfied. Do all your correspondences through our email address

Read more

Zero-plagiarism guarantee

At, our professional writers know the consequence plagiarism does for our clients. We have updated software’s such as article checker and copyscape to check for originality of the custom papers before submission of the final paper to the you. Our guarantee to the customer is that we will write 100% original papers for them that are quality, timely and of low cost. We have experienced professional and competent PhD writers who will write quality custom papers for you..

Read more

Free-revision policy

. At, we are proud to provide top-quality Essay writing service to our esteemed customers. We are ready to take up that challenging academic assignment that is giving you sleepless nights and simplify it for you according to your desired requirements. We are willing to revise your paper if it does not meet your requirements. At, we do not compromise with quality; thus, we offer unlimited free revisions until the customer is satisfied with their custom paper. Our unlimited free revision services are provided under the following terms:.. .

Read more

Privacy policy knows that client’s information is an essential tool for our company. It means that whatever the client requests from our service is kept strictly confidential. It means that whoever writes for this company understands the terms and conditions hence you should not be worried because you will never see your work somewhere else...

Read more

Fair-cooperation guarantee

Rest assured that we will always be attentive to your needs and requirements. We belief in the phrase treat your neighbour as you would want them to treat you. We leave nothing to chance and always look forward to a good interaction with each other.. .

Read more

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
The price is based on these factors:
Academic level
Number of pages