Vehicles in Oklahoma City, Oklahoma

The study will examine the descriptive information of vehicles collected in Oklahoma City, Oklahoma. We expect that the most common vehicle makes in Oklahoma City will be Honda , which is the top vehicle maker in Japan. Honda makers are a Japanese company and one of the top-selling brands in Japan. It sells its vehicles within Japan and across the globe. This makes it a very competitive and profitable vehicle company. The reason why Honda vehicles are common is the price that is friendly , compared to other makers. Also, we expect the Cadillac vehicle maker to be the least common among the Oklahoma residents. Further, we expect cars to be the most common vehicle types preferred in Oklahoma City. We would not expect that larger vehicles such as the trucks, SUVs, and vans will be significantly different from smaller vehicles roll through. The study strongly believes that that the roll through of the vehicles majorly depends on the vehicle make. However, we will expect that there will be a significant difference linking the roll through and the vehicle status. It will be expected that the vehicle type will be significantly different from the roll through. It would be expected that vehicles would roll through stop signs more often in the evenings than during the afternoon. This is because traffic is generally heavier in the afternoon, and one might expect that more police will be on patrol in the afternoon than in the evening. 

Materials and Methods 

The participants in this study are the vehicle sample s in Oklahoma City, Oklahoma. A sample of 666 vehicles was sampled from 12 locations within the City. The locations include the N. Edmond, S. Edmond, N. OKC, S. OKC, Bethany, Warr Acres, Mustang, Midwest City, Del City, Norman, Moore, and Outside OKC Metro.  

Information for each of the vehicles was collected and recorded. This comprises of the make, vehicle status (High, Low), vehicle type (truck, SUV, car, sports car, van, and other), and roll through (Rolled through, did not roll through) for each vehicle. The vehicles were categorized as rolled through if they were trucks and SUVs. Those rates did not roll through if they were a car, sports car, van, and other vehicles.  

For the first question, we will analyze the vehicle make using the descriptive statistics to establish the most common vehicle make in Oklahoma City. The vehicle makes a nominal variable that is measured by categorizing the 29 different types of vehicles. The bar graph will be used in this context to display the information. The second question we will analyz ing the vehicle - type variable s to establish the common type of vehicle , used by most of the Oklahoma residents. The vehicle type is coded as (1 = Truck, 2 = SUV, 3 = Car, 4 = Sports Car, 5 = Van, 6 = Other). This is a nominal variable since there is no order in classifying the vehicle types. In the last question, we will examine if there is a significant difference in the roll through (1 = Roll through, 2 = Did not roll through) and type and status (1 = high, 2 = Low). We will use the analysis of variance to analyze the data.   

Procedure 

This is the procedure that was followed for data collection. The location plays a key role in collecting vehicle information. Various location w ere identified by the observer , that w ere neutral and provided a clear view and interaction with the drivers or vehicle owners. Failure to identify a good location that has a clear view will result in a collection of incorrect data. The observer began to collect the information about the vehicles in every 15 minutes. During the next 15 minutes, the observer watched as vehicles passed through the location , while recording the details of the vehicle. After 15 minutes, the observer then watched the next passing vehicles and recorded the details. The information was recorded in a checklist designed for the study. Next, the data was entered into excel following the records from the checklists.   

The 15 minutes interval is vital , since it helps to obtain a sample by minimizing selection bias. There were different observers in the 12 locations, and they made their own corresponding observations during 15-minute time intervals in Oklahoma City. All of the data was compiled into a single spreadsheet for analysis.  

Results 

The descriptive statistics were run to establish the most common make of each vehicle. A bar graph was used to describe the common vehicle make for the overall sample. 

Figure 1 : Most Frequent Vehicles Makes Observed 

Figure 1 above shows the vehicle type samples from Oklahoma City. The most frequently observed make of vehicle was Ford, which was observed 17.1% of the time. This was followed by Honda that was observed at 11.7 %. The least common vehicle make was Porsche at 0.3 % and others at 0.2 %. The main reason why they are the least common is that Porsche and others are the most expensive. 

Figure 2 : Frequency of Vehicle Types Observed 

Figure 2 above shows the vehicle types sample from the Oklahoma City. The most frequent vehicle types used are cars at 40.2 %, followed by SUV (34. 4 %) and Trucks comprising of 17.3 %. The least common type of vehicles are vans and others with 2.8 % and 0.5 % respectively. 

Figure 3 above indicates the frequency of the roll through of the vehicles. It is evident that 59.8 % of the vehicles sampled rolled through stop signs higher compared to 40.2 % of the vehicles that did not roll through stop signs. 

The ANOVA technique was employed to examine if there is a significant difference in vehicle status and the roll through stop signs. 

The test statistic,  F  (1, 665 = 7.965,  p =  0.005. Since the p-value is smaller compared to alpha = 0.05. Thus, we deduce that there a significant difference between vehicle status and the roll through stop signs. In addition, we will like to determine if there was a significant difference linking the vehicle type and status. The  F ( 6, 665) = 1.566,  p  = 0.154. This implies that there is an insignificant difference in vehicle type s and status.  

Conclusion 

The purpose of this study was to analy ze the vehicle data or information collected in Oklahoma City. The study set its expectations of the results after analyzing the data. We expected that Honda would be the most common make of vehicle in the City from the observed vehicles. Our expectation was not met since the Ford make was the leading in the City, followed closely by the Honda make. Also, we established that the cars were the most common type of vehicles used by the residents of Oklahoman City , from the observed information. Additionally, we tested the hypothesis to ascertain if there is a significant difference between the vehicle status (either low or high) and the rolling through. We found out that the hypothesis supported the claim of significan t difference linking the vehicle status and rolling through. However, the second hypothesis indicated that there is no significant difference in vehicle type s between high and low status. In this case, our hypothesis did not support our claim.  

I will expect that the most common type of errors arising from data collection is the selection bias. The error is a result of the selection criterion employed by the researcher. Further, the method used to select the vehicle using a time interval of 15 minutes is non-probabilistic , which may result in a lot of errors and biases , since it is not a representation of the population. Thus, it may provide to misleading results. The other problem we expect is the lack of training among the observers. When observers do not receive appropriate training, data collected may be inaccurate due to observation and recording errors. The study did not test for the validity and reliability of the collection tools used. Therefore, I recommend that in future studies , the observers should be trained on how to gather data of the moving vehicles.