1. how to do a data analysis
Stan Siranovich
Crucial Connection LLC
Prepared for SQL Saturday – Louisville 2018

2. The Story (based on a true life adventure)
You log into your first thing in the morning and the rumors are confirmed; your company is expanding with branch offices in three new cities. As you read, the Big Boss drops by your cubicle and says that she needs an analysis of the real estate situation in all three cities. The analysis needs to include summaries of prices based on factors such as number bedrooms, number of bathrooms, and number of square feet. It should include lots of visualizations, be clear and easy to understand, and point out any interesting relationships that you've uncovered. And you need to have it done by 11:30 a.m.

3. Summary Analysis for Louisville, Indianapolis, Cincinnati
Requirements
Plan of Attack
Analysis for Louisville, Indianapolis, Cincinnati
Beds, Baths, Sq. Ft., etc.
Clear Visualizations
Concise Report
Due in Two Hours
Use JMP data analysis software from SAS
Collect, clean and examine data
Summarize data
Explore data visually
Analyze data
Prepare report

4. Residential Real Estate Data

5. The Software

6. By the Numbers Download and Concatenate
Use Analyze > Distribution platform for visualization and data cleaning
Use Recode function for further cleaning
Use Analyze > Distribution platform for visualization and analysis

7. Concatenate Data in Analysis Software
Open files and import into JMP data table
Concatenate all three tables
Include Source Column

8. Main Table with Source Column

9. Visual Data Cleaning
Use Analyze > Distribution platform for first pass at cleaning

10. Partial Result from Analyze > Distribution

11. Cleaned Result from Analyze > Distribution

12. Recode Function

13. Recode Result with Formula Column Property
Displays Match function
Documentation
Reproducible work flows

14. Analyze > Distribution Window
Requested variables, all three cities

15. Result with Statistical Data and Boxplots

16. Box Plot Summary

17. Analyze > Distribution By Variable
By Source Table

18. Result with Statistical Data and Boxplot

19. Stacked Results
Red Triangle > Stack

20. Easy to Read Table
Right Click > Edit > Make table of graphs like this

21. Progress Summary of prices, beds, baths, sq. ft.
Done
Next
Summary of prices, beds, baths, sq. ft.
Visualizations – clear, easy to understand
Analysis
Visualize distributions
Comparisons of two variables
Fit Y by X platform
Data types and statistical measures

22. Output is Determined by Variable Type
Analyze > Fit Y by X
Examines the relationship between two variables
Output depends on the variable modeling type

23. Price vs. Source

24. Statistical Results Red Triangle > Means / Anova
Red Triangle > Compare Means > All Pairs, Tukey HSD

25. Multiple Variable vs. Source

26. Fit Y by X with Categorical and Continuous By Variables

27. Definitions
R-square
Measures the proportion of the variation accounted for by fitting means to each factor level. The remaining variation is attributed to random error. The R2 value is 1 if fitting the group means account for all the variation with no error. An R2 of 0 indicates that the fit serves no better as a prediction model than the overall response mean.
F ratio
Model mean square divided by the error mean square. If the analysis of variance model results in a significant reduction of variation from the total, the F ratio is higher than expected.
Mean Square is a sum of squares divided by its associated degrees of freedom.

28. THE END How to Do a Data Analysis
TITLE
AUTHOR
How to Do a Data Analysis
Stan Siranovich Principal Analyst Crucial Connection LLC Jeffersonville, IN
This work is the copyright of Stan Siranovich and Crucial Connection LLC