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Columbia Law School ​hi my name is Laura speakman and today I'm going to share with you a presentation on whether or not acting drivers are as effective as promoted drivers in the Colorado Springs Fire Department the Colorado Springs Fire Department needs a total of 119 positions filled in a single day and in order to fill those positions we staff about 130 people a day and this takes into account unplanned and planned absences the goal is to balance shortages with excess staffing because too few personnel will lead to too much overtime and overworking your people and too few people are too many people leads to access some ways so there's two ways to address shortages we can either hire back which is effectively over time or we can allow lower ranking personnel to act as promoted ranks are promoted ranks are captain's lieutenants drivers and paramedics and our unpromoted rings are our firefighters acting is beneficial because it saves the department money lower ranks cost less and it also provides critical on-the-job training to our firefighters specifically our drivers are are used to man are 28 heavy apparatus and they also perform the engineering engineering duties of the intern of the truck so they're very specially trained but on mid recent retirements the cs ft is facing some choices do we hire additional drivers and accept a level of excess or do we allow more firefighters continue acting as drivers so an acting driver is typically a higher ranking firefighter who is training to specifically become a promoter driver so we want to measure how effective these two groups are and if there are any differences the first way ought to measure is to grade them based on how well they need the city's 8minute first company response standard this standard mandates that the CSF D will arrive to ninety percent of emergent incidents within eight minutes or less so we will look at the rate at which each groups of drivers arrived eight minute incidents to see how a minute maintenance or not to see how effective they are and our question is are acting drivers less likely to make the eight minute response standard than the promoted counterparts and they're specific factors which will affect the response success rate the second way we will grade our drivers is to measure the absolute value of the response time the question is are asking drivers slower to arrive to an incident than the promoter counterparts and if they are how much longer so the data was collected from the Colorado Springs Fire information system which is an Oracle database I developed a brand new as quote SQL query that will relate the experience and rank of a personnel to an incident and this query included about 150,000 calls in the original data set from 2010 2 2013 however I couldn't use everything one of those incidents I had to remove invalid entry that were missing key variables such as departure time or had impractical entries such as very long dispatch times so this eliminated about 40,000 volts i used a a a application online to determine the sample size that i would require the hypothesis test was that actors perform just as well as promoted drivers or the ultimate that actors do not perform as what was promoted driver shows a one-sided test and the results of this analysis indicated that i was going to mean about 7,000 instance with acting drivers and about 64,000 of promoted drivers a simple random sample was collected from these about 70,000 incidents using excels random number generator these are the variables i use so there was a year 2010 2 2013 emergence status which indicates whether or not that unit dispatch lights and siren to the whole route the PES which is the geographical planning evaluation zone of the city there's nine geographical regions the month of the call the season of the call whether or not the actor was promoted or an acting driver I'm sorry where yeah uh-huh whether or not the driver was shipped out shipping out means that they are no longer at their home station the hour the call whether the hour was a peak hour of traffic whether the call was dispatched as an omega alpha Bravo Delta Charlie or echo call and these are in the whole of ascending priority except for the last one none and whether or not the call could be grouped into either a low or high priority call alpha Bravo Magus and nuns were grouped into low and Charlie dead tell echoes and highs so the first question our firefighters act firefighters acting as drivers less likely the promoted drivers to meet the eighth minute response standard this required a testa to proportions and in order to perform this test the sampling distribution must be normals the number normal than so the number of observed failures must be greater than or equal to 5 for both populations and the analysis requires a large sample size you can see from the bottom chart that both of these requirements are satisfied for the second question are there any variables which indicate whether an acting driver promoted drivers more or less likely to meet the a minute response standard i used a chi-square test of Independence so the data must be independent the data must be normal in the data must be from a simple random sample the data is independent easy to incident in location and time is a random independent event we're going to evaluate and see that the most of the data does have um cell values that are greater than 5 so they are normal and of course we did juice in the rain example this is the result to conform confirm normality so there was only


one cell the alpha level of the call that had less than five so that was eliminated from the analysis for the third question what factors will have most affect the absolute response time of a motive actor I did an iterative analysis of variance so there was a full model that was then reduced all the observations must be independent which we already establish the residual sum must be normal variants mystery constant and on this test is relatively robust with personal deviation so we're going to say that's okay here we I did a normal probability plot we can see that it's pretty non normal so either box Hawks transformation which suggests either a cube root or logarithmic transformation so did both here and you can see that the cube root transformation on the left is better and has a better Anderson darling statistic and both are both pretty much improved the normality of the data to the same degree it's slightly improved so we're going to go with the key transformation for the rest of the analysis to further endorse formality I evaluated a histogram of all the residuals for all the factors and they were all all the residuals were normally distributed around zero so that was good and to support constant variance a total Levene's test for constant variance at alpha equals 0.01 and on the year and the month of the call the seasonal indicator whether or not the driver was acting are promoted and whether or not that the driver was shipped out all passed the test for constant variance additionally i decided to include emergent status and the geographical PES into the analysis as of locking variables so for the last question what are the significant differences between response times I did the two keys I did to his post hoc tests and on all the exploratory data analysis of the two key and I'll for the two key test is satisfied the same requirements of the ANOVA so we're good here so here's an overall summary of the analysis was performed my four questions along the top and all the variables that were evaluated um I chose in particular when I draw your attention to the last column right I chose the seasonal indicator rather than the month of the call I figured everything out for the ANOVA I figured these two were highly correlated and on the seasonal indicator would be more useful to the department so here are the results for the test the two proportions um you can see that the p value of the test was 0.4 37 which means it was not significant and so the answer to our first question is that there is no statistically significant difference between the rate at which actors and promoted drivers achieve the evening response standard here are the results the chi-square test of Independence um there are some differences a few differences between the factors that affect actors versus drivers shipping out does not affect the rate which actors achievement response while there can be arguments made that it does affect drivers the peak hour does affect promoted drivers while a does not a strongly affect acting drivers so here yellow indicates significance alpha equals zero point zero one more green indicates factors that are nearly significant and definitely of interest but it was surprised me I would set the season doesn't affect the rate at which either actors or drivers armed promoted drivers achieve the unit responds to whether it's know you're mild out doesn't make a difference so here are the initial results for the full model for the analysis of variance and the variables that I included so you can see season afternoon and ohm shipped out are my fixed variables and emergence attison finding evaluations on my blocking variables the variables here in red are on not significant so I'm going to eliminate them for the reduced bottle and that leaves me with a merchant status and planning evaluation zones my blocking still and acting and whether or not they're shipped out as my two other factors all of the terms are now significant actors are less so but are kept that variables kept in the model to preserve the hierarchy of the model since acting and shipping out our but the interaction is significant there the residuals of the model or I'm sorry the I the Diagnostics of the reduced model will show that the residuals are normal and random and variance is constant here we have the main effects of the reduced model plenty evaluation zone emergence Attis razon highly variable response times while acting and shipping out just the main effects don't actually have large hugely large impacts on response times at all however the interaction here shows that shipping out has a larger effect on after than it does on promoted drivers and shipping out actors have a larger response time then shipped out drivers so to kind of evaluate the absolute difference between these results I did the two key analysis and basically what I got is that the mean differences range anywhere from about 4 to 10 seconds and differences so not a huge difference however the bottom table here shows that acting drivers that are shipped out our slower than promoted drivers and acting drivers that are shipped out they are the slowest group and traders so in summary for the Tesla to proportions that um we showed that the actors are no less likely to choose eight minute response gender and we also showed that shipping out does not affect an actor's rate of success meeting me in a response but it does affect to promote a driver success a potential explanation that actors tend to move faster zanden familiar situations while drivers are more likely to take it easy in scenarios in which they are more familiar so the anova finally also showed that the absolute response time is nearly identical with about a three to ten second difference between actors and drivers depending whether or not they are shipped out so the evidence does not suggest that there's a large negative impact the community by utilizing drivers and there's actually a lot of benefits invaluable experience to the actors and financial savings to the department however actors should be a sign of their home station as often as possible because even though the rate of success and meeting the standard doesn't change the absolute


response does change and there could be extreme cases where actors are much slower and an emergency response winter in an emergency response model one terrible response can have a lot of impact and be a very high risk there's also an additional consideration as to how this applies to the implementation of location-based dispatching location basis fashion is where we just best units based on their physical location rather than their static district this is shows that the closest unit is always on call are always assigned to the call but the result is that units will find themselves creeping across the city and slowly getting away from their home station which is equivalent effectively to shipping out which we know affects response time there are some deviations from normality in this analysis but we attempted to mitigate them by evaluating treatments on earth concert variants and also evaluating all the histograms the residuals which showed they are all normal however for future studies we might consider evaluating only emergent distance which we reduce the number of outliers can hopefully improve normality I'd like to make some acknowledgments here to my predecessor William Wallace for planting the seed for this analysis doing a lot of the work for that showed that um that the number of higher backs and over time required could be modeled I'd also like to give a shout out to the Colorado Springs Fire Department to that develop the fire information system and also the Australia bio security took Cooperative Research Center who develop the tool used to derive myselfi size and here are my references thank you so much College of Osteopathic Medicine, Harlem.

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