Multiple Regression Analysis

Using a dataset of your choice, develop an appropriate number of research questions with associated hypotheses (null and alternative) to assess the relationship between at least two Independent Variables (IVs) and one Dependent Variable (DV) using the multiple regression statistical method. Include appropriate diagnostics for identifying problems of multicollinearity. Include "a priori" expectations of the relationships between the IVs and the DV and compare your expectations to the actual results. Also include your "final regression model" in the "results" section.

The general content of each mini-project should include the following components:

• Executive Summary : Overview of the dataset, the analysis, and results
• Background : Description of the dataset and the variables (e.g., discrete, continuous, nominal, ordinal, interval, ratio, etc.)
• Description of the sample : Do you consider this dataset a population or a sample.
• Methods : What types of analyses did you run (e.g., measures of central tendency, mean, mode, median, measures of dispersion, variance, standard deviation, z-scores, confidence intervals, ANOVA, regression, etc.)
• Data screening : What did you do with missing data? Address any diagnostic tests associated with your statistical method.
• Analysis : Analysis must include appropriate tables and graphs of results. Analysis must include calculations of central tendency and dispersion. Include graphs as appropriate.
• Interpretations and conclusions : Draw any conclusions and interpretations from the statistical calculations addressed within your report.

Your Introduction section (approx 1 or 2 paragraphs) should clearly delineate the topic of your study and your specific research questions.

Your Method section (approx 1-2 pages) should be very brief on participants and variables. Devote most of your section to describing the models that will be employed to test your specific research question(s). Include information as appropriate for alpha levels to be used, etc. You should check/clean data and/or create new variables as needed. I do not want you to run every possible model for a given data set. Choose one outcome variable and small sets of predictors and build a final model using the steps described in class (e.g., data screening, outlier identification, regression diagnostics, etc.).

Your Results section (approx 2-5 pages) should provide answers to your research question(s). Include appropriate tables/graphs as necessary.

Your Discussion section (approx 1-2 paragraphs) should present a thoughtful critique (but not a simple restatement) of your results, including any deficiencies in the study/questions/analyses, etc.

Papers will be evaluated primarily on correctness, completeness, and conciseness. Grades will be determined using the following equally-weighted criteria:

• Accuracy : Data are analyzed using the correct tests and the procedure yields the correct results.
• Interpretation : All results are correctly interpreted.
• Presentation : Results are presented clearly and concisely. All of the appropriate and relevant information is included, but the narrative is free of jargon and extraneous information.
• Professional format : Format of the paper is professional and tables are clear and concise. (PLEASE DO NOT ENCLOSE YOUR PAPER IN A BINDER OR PLASTIC COVER).
• Professional writing : Title page is included and professionally formatted. Writing is free of grammar, spelling, and mechanical errors. Entire paper is professionally prepared.

Price: $35.31

Solution: The downloadable solution consists of 24 pages, 1131 words and 8 charts.
Deliverable: Word Document