STAT515 - Exploratory Data Analysis - Statistics Assignment Help

Assignment Task

1. Objectives
 The final project has several objectives:
• To use data visualization software taught in class to support data analysis in one or more of the following ways:
o Exploratory data analysis, in which you create graphs or maps indicating possible relationships among variables and then perform additional analysis using methods taught in class;
o Visual data checks, in which you use data visualizations to examine data quality or to check successful completion of data processing procedures, such as displays of missing-data patterns and graphs of recoded variables and calculated summary statistics;
o Visual data-analysis methods, in which a data analysis method requires the examination of a data visualization, such as the examination of residual plots to determine if model assumptions are satisfied; and
o Visualization of analysis results, in which you create graphs /or maps to display the results of data analysis methods taught in class

• To gain experience in developing R scripts that correctly perform multiple data analysis methods taught in class,
• To gain experience in communicating about data visualizations, data analysis procedures, and data analysis results

2. Project components
The final project contains the following components:
2.1. Data set. You are responsible for obtaining a data set for your project. Appendix A lists possible sources, including both primary and secondary sources. Some secondary sources include additional information about their data sets, such as descriptions of variables, processing for cleaning the data, and successful analyses of the data sets. For some data sets, readily available R code or Tableau files exist that visualize the data or analyze the data set using an analysis method listed in Section 2.5. You cannot use such data sets for the STAT 515 final project to create the same visualizations or perform the same data analyses present in the associated R code or Tableau files1

The data set should contain a minimum of six variables, and if the data set were to be reshaped into "long format," the result would have a minimum of 100 records. The minimum of six variables, however, can contain meaningful "recodes" of existing variables. For example, if an agricultural-crops data set included variables for
1. Year (30 levels),
2. State (5 levels – Illinois, Indiana, Iowa, Minnesota, & Nebraska),
3. Crops (2 levels – corn or soybeans),
4. Acreage, and
5. Yield

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