MTAS Benchmarking

Data Analysis & Empirical Decision Making (EDM) 

Matthew Marshall


City of Athens, TN

"For those of us who have
mistaken passion for a solution...
hope is not a course of action."

-Poet Buddy Wakefield

P-Values & Grouping.

Pearson's R

A statistical method used to determine coorelation/no correlation between to data sets.


The practice of categorizing cities based on  population, MSA status, proximity to the interstate, and service population. 


Athens, Paris, Crossville, Sevierville, and Springfield


Bartlett, Brentwood, Franklin, Goodlettsville, and Red Bank

Sub Major City:

 Morristown, Cleveland, Kingsport, Greenville, and Tullahoma

Major City: 

Knoxville, Chattanooga, and Murfreesboro

The Effects of Grouping 

For Micropolitans a moderate and Major Cities a strong negative correlation was established between Inspections and Structure fires per 1,000 residents.

Micropolitans P= -0.50          Moderate Negative Relationship   

Major Cities P= -0.98             Very Strong Negative Relationship

For Suburbs and Sub Major Cities we found only moderate correlation between Inspections and Structure fires, and in the case of Sub Major Cities a positive correlation per 1,000 residents.

Suburbs P= -0.08                   Very Weak Negative Relationship  

Sub Major Cities P= 0.47      Moderate Positive Relationship             

If we look at the correlation with all benchmarking cities we see a strong positive relationship, P= 0.55.

The Effects of Grouping

Pearson's R for all Cities = 0.55, but when you break down the cities into their groups you see a different story.

For Micropolitans we found no relationship correlations between the Number of Sworn Officers and TIBRS A crimes per 1,000.

Micropolitans P= 0.14

For Suburbs, Sub Major and  Major Cities we found a very strong correlation between Number of Sworn Officers and TIBRS A crimes per 1,000.

Suburbs P= 0.83

Major Cities P= 0.85

Sub Major Cities P= 0.58



Council member X - "Police Chief Ziegler do you know why we have so many Robberies this year?"


Police Chief Ziegler - "It's how we measure time."

We use Z Tests and T-Tests to determine when a statistic becomes Statistically Significant and when it is just more/less than the year before.



1.  Establish the most statistically significant correlation.

2.  Determine a Line of Best fit (may or may not be linear).

3.  Set expectations for your outputs based on your inputs.

4.  Review your results and determine if the Line of Best fit is            matching your results.

Predicting Using a Line of Best Fit.


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by monkndylan


Public - 3/29/16, 1:02 PM