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geek i immediately wanted to know if there is a rational basis for the design choices car manufacturers seem to be making en masse i needed data sales data was easy to obtain at car sales base the emblem size was harder to find and i ended up finding photos of cars taken head on and measuring the size of the grill emblem relative to the car width to keep this undertaking manageable and fun i focused on three japanese mid size cars that compete directly against each other toyota camry honda accord and nissan altima the yearly unit sales and the relative grill logo sizes are plotted in the charts below yearly us unit sales of three mid size auto models that compete directly against each other note the late 2000 s housing bubble recession effect the grill emblem size relative to the width of the vehicle vs the model year its virtually impossible to tell from the plots of the sales and emblem sizes whether there is any association between the two however if we plot these data in a different way it becomes apparent in the plot below each point represents a pair of car models for a particular year the x coordinate of the point is the ratio of the unit sales for that year while the y coordinate is the ratio of the emblem sizes for that year if there was a model change that year i took the average emblem size the solid line has slope of 0 356 and the p value of this kind of correlation occurring at random is 0 002 each symbol represents a pair of car models in a particular year the x coordinate of each symbol is the ratio of the number of unit sales in that year and the y coordinate is the ratio of the relative grill emblem sizes the solid line is a linear fit we found a highly significant moderate correlation between the emblem size and sales but as any scientist repeats as a mantra correlation is not causation for example a change in the emblem size almost always coincides with the change in the model of the vehicle it s impossible to disentangle the effect of the emblem size from the effect of the new model on the sales at least some of the effect has to be due to the emblem size because it stayed relatively constant over several model changes in the end alas one would have to gather a richer and larger dataset to say with any certainty whether the emblem size influences sales leave a comment money plus endorsements equals votes posted in general by alexander lobkovsky meitiv on august 20 2018 montgomery county in maryland where i live is one of the largest and wealthiest counties in the us it is however not immune to the sense of dissatisfaction with business as usual in the government last year a ballot initiative which passed by a landslide introduced and championed by conservatives limited the number of consecutive terms a council member could serve as a result the nominations for three out of four at large seats on the county council were up for grabs in this year s primary the county council is a 9 person legislative body that makes important county wide decisions most notably allocating the 5 5 billion budget that s a lot of power it s therefore not a surprise that this year an unprecedented 33 candidates ran for the 4 at large seats including my spouse danielle meitiv the complete election results can be found on the maryland board of elections website a cursory look at the results and the several high profile such as washington post and the mcea apple ballot suggests that these endorsements may have been a key factor determining the outcome of the elections being a data scientist i wanted to go further than a cursory look and a explore the results of a rigorous regression analysis the dataset for the analysis was constructed by combining data from three different sources the maryland board of elections for the final vote count the seventh state blog for the endorsements maryland campaign reporting information system for the financial information and the number of times a candidate ran before in montgomery county the dataset assembled it was straightforward to use r to construct and run linear regression models a linear model assumes that all factors are independent and their effect on the total number of votes obtained is additive i started with a model which had all factors in it and proceeded to remove insignificant p value 0 05 factors one at a time rerunning the model after every removal until all factors were significant the significant influence factors that remained at the end of the factor elimination process are summarized in the table below factor votes baseline 2 475 votes in absence of other factors money raised 47 votes per 1k raised washington post 14 200 votes mcea 14 600 votes seiu500 7 340 votes gender m f 4 150 for f 0 for m the main conclusion of the analysis is the importance of the washington post endorsement and the apple ballot the model seems to suggest that bulk of the votes of the top 7 vote getters came from these endorsements another interesting conclusion is the male gender liability or female gender advantage this much talked about trump era effect is perhaps less pronounced in the moco election than in other races but nevertheless measurable one has to be cautions interpreting the results of simple regression models the fundamental assumption in computing the significance of factors is the statistical independence of the factor effects this is of course not true the washington post endorsement probably helped with fundraising or vice versa wapo endorsed the clear fundraising front runners another thorny issue is causality did the endorsements influence the outcome of the elections or did the endorsing organisation simply read the tea leaves better and predicted the winners if you would like to run your own models here is the mococouncilatlargevotedata leave a comment inspections fines and the honor system posted in economics transportation by alexander lobkovsky meitiv on september 25 2015 alexanderplatz u bahn station in berlin this summer we visited berlin and enjoyed its excellent public transportation system unlike the controlled access system prevalent in the us berlin s and prague s for that matter system is an honor system you can buy a ticket at any time and when you board a train or a bus you stamp the ticket with the date and time the trip started compliance is enforced by periodic inspections and the fine for riding without a validated ticket schwarzfahren is a modest 60 during our 8 days in berlin we saw inspectors once and did not get inspected ourselves given the ticket price is the combination of the fine size and inspection frequency sensible that is could bvg the transit authority save money by changing the fine amount and or number of inspectors it has to hire to answer this question we must know how people decide whether to buy a ticket or risk being fined many factors come into play here a purely rational and amoral consumer would buy the ticket if the product of the inspection probability and fine amount is larger than the ticket price this way not buying tickets costs more in the long run however there is a strong national pride and the concept of beitragen contributing which keeps schwarzfahren low just a few percent according to this article in german being caught without a valid ticket may also result not just in a fine but also in a court summons which presumably has a higher deterrent power let s explore the effect of the frequency of inspections and the associated cost and the fine amount on the total amount of money collected i will define the single ride ticket cost to be 1 and the fine amount to be to keep the formalism simple i will neglect the reality that the fine for repeat offenders is higher this fact reduces schwarzfahren but makes calculations more complicated in the long run the amount of the fine for repeat offenses is the important quantity i will assume for simplicity that the population of transit rides consists of two distinct pools the conscientious riders always buy the ticket population size the rest of the riders population size are rational fare evaders and buy tickets when the inspection probability per ride is greater than this assumption requires the ability to accurately measure the frequency of inspections either from personal observations or from those of friends and family the last ingredient in the recipe for computing the revenue of the system is the cost of inspections where is a new parameter which can be thought of as the cost per inspection within our simplistic model given the fine amount if the inspection probability is the evaders do not buy tickets and if the potential evaders always buy tickets because the cost of inspections is linear with there are two local maxima of the revenue either in which case the revenue is simply i e there are no inspectors and the evaders ride free or in which case the evaders buy the tickets the total revenue is therefore if the cost parameter is greater than the product of the fine amount and the fraction of fare avoiders it does not make sense to inspect another way to look at this equation is that the fine amount has to be at least the cost per inspection divided by the fraction of evaders to justify inspections of course the situation in real life is different most people are opportunistic evaders each person is characterized by a risk aversion parameter which balances the financial benefit of evasion with the emotional toll of being caught because of this fact some level of inspection is always necessary the level itself depends on the distribution of the risk aversion parameter in the population and the recovery rate of the fines as a function of the fine amount but that s for another post tagged with fare avoidance fines transit system leave a comment debunking the keep the ac on while away myth posted in economics by alexander lobkovsky meitiv on september 22 2015 we all heard the supposedly common wisdom of leaving the ac or heat on in the house when away on a trip this wisdom is perpetuated by the hvac professionals themselves well it s wrong at least under the simplifying assumptions below you will always save energy and money by turning the heat ac off when you go on a trip no matter how short to quantitatively compute the difference in the money electricity for the two scenarios 1 maintain house temperature and 2 turn the heat ac off while away we will need to know the cost of either removing or adding the amount of heat from to the house i will assume that the cost is simply proportional to this is probably a good assumption for combustion heating systems but may not be very accurate for heat pumps whose efficiency depends on the temperature difference between the inside and outside let me deal with this complication later let me assume that the house has heat capacity so that the amount of heat that enters or leaves the house to cause the temperature change is let me also assume that total heat conductance of the house s walls is this heat conductance is the coefficient of proportionality between the outside inside temperature difference and the total heat that flows into out of the house per unit time therefore under scenario 1 keep heat ac on if the duration of the trip is the total heat that needs to be pumped into out of the house is where is the mean difference between the indoor and outdoor temperatures when heat ac is off the house temperature will approach the mean outdoor temperature exponentially with the charateristic time scale let s assume that the heat ac system is so powerful that the heat is removed added quickly when you arrive from the trip so that the extra losses during the cooling heating period are small compared to the total amount of heat to be removed added then the total amount of heat to be removed added after the trip is comparing the two amounts of heat we see that because in other words the amount of heat that must be removed added from to the house is always greater when you keep the system on during the trip therefore unless there are counter indications like the pets dying or pipes freezing it is better to turn the heat ac off when you travel or at least change the thermostat to a setting that is closer to the outdoor temperature tagged with air conditioning heat keep on while away saving money leave a comment the optimal strategy for setting the price of event tickets posted in economics by alexander lobkovsky meitiv on september 10 2015 i love tennis and always visit the atp tournament in dc in august this year i went with a friend who bought the ticket at the last moment online and discovered that it was deeply discounted almost 50 that fact coupled with the observation that the stadium as less than half full got me thinking are the organizers employing the best ticket price strategy to maximize the receipts to determine the optimal ticket pricing strategy we need to know something about how people make buying decisions there are a number of different ways of describing this decision making process here i will make several simplifying assumptions to make the problem analytically tractable these assumptions can be relaxed in practice albeit at a cost of having more complex equations in the simplest formulation each person has an internal valuation of the ticket if the actual price is higher this person does not buy the ticket if the price is lower this person buys the ticket at a rate probability per unit time proportional to the difference between the valuation and the price the coefficient of proportionality is where is the characteristic time it takes to buy the ticket if the difference between the price and valuation is unity this can be called the customer response time i will make two simplifying assumptions 1 does not change with time this is not true in practice as people tend to make snappier buying decisions closer to the event and 2 every person has the same both assumptions can be relaxed by considering the response time that varies with time and is different for every individual the final ingredient in describing the population of people is the distribution of ticket valuations if the only information we have about this population is its size total number of people potentially interested in the event and the mean ticket valuation the number of people with valuations in a narrow interval between and is i e the distribution of valuations is exponential with a variance and mean equal to let me first consider the case of a fixed ticket price also suppose that tickets go on sale some time before the event the probability that a person with internal valuation buys the ticket during this time is therefore the expected number of tickets bought is the gross receipts because everyone pays the same price are maximized when i e when the price matches the mean valuation among the interested people this is the best fixed price strategy when the expected number of tickets sold at price is smaller than the capacity of the event when ...