Data Analysis Project SPSS ANOVA Regression

Data Analysis Project SPSS ANOVA Regression

1. ONE paragraph summary about the topic and THREE questions for each of the project ideas that hope to answer through your data analysis.

2. Data Acquisition and Description. For this phase, you will find, explore, and upload the dataset(s) needed for the analysis. In addition, you will adapt and/or recreate a dictionary documenting the contents of the dataset(s).
• A copy of the dataset(s) you will use for the analysis (either a CSV or Excel file(s)).

• A data dictionary describing the dataset(s).

3. Data Preparation & Initial Visualizations + the NAPKIN dashboard. For this phase, you will submit a Tableau packaged workbook (*.TWBX) containing your prepared and extended data. Your submission should contain all necessary joins and/or blends, any additional data preparation identified in previous milestones, and at least one (1) hierarchy. Finally, on a NAPKIN draw a rough draft of how you imagine your final dashboard (there will be an example of this in the files I uploaded).
– If necessary, a revised version of your dataset that addresses any issues/needs outlined in your M2 review.
-A Tableau packaged workbook (*.TWBX) meeting the specifications outlined above.
An image in *.jpg format of your Napkin dashboard rough draft. Do not turn in your image as file type -> *.HEIC

4. Completed Interactive Dashboard. Using Tableau and the data you have prepared, develop an interactive dashboard to present the discoveries you made when analyzing your data. Your dashboard should consist of NO LESS than 4 objects (i.e. graphs, charts, maps, and/or tables) that have the ability to filter (via filters and/or using charts as filters) and allow the user to drill down to lower levels of a hierarchy on at least one of the objects.

5. Completed Story/Project Overview Using the Tableau file submitted in M4, create a story like an overview of the project

-A general description of your analysis topic and why it interested you.
– An overview of your dataset(s)
– A demonstration of your vizzes and dashboard.
– Your analysis questions and the answers to those questions using story points.
-lessons learned and conclusion. In this case, lessons learned refers to what you learned about the data analysis process from your project experience. What were your key takeaways from the project?

From a technical standpoint, your story should contain a minimum of four-story points. Each story point should have a clear, concise caption and highlight one of the key findings of your analysis. Story points can contain a single visualization or your dashboard. Any settings you used (e.g., filters/highlight actions, drill-down, selected marks, annotations) to discover your answer should be applied to the story point.