Extrapolation

         Overall, the original population we chose to sample is not appropriate for estimating the proportion of red and green cars in the entire U.S. The three parking lots we chose to study were not really significantly populated enough to make such a broad comparison, mainly because we only sampled vehicles from North Olmsted. We also chose to sample the parking lots of two apartment buildings, and a grocery store. The entire U.S. includes a much larger selection of data, such as business buildings, parking garages, restaurants, etc. Therefore, our population of study should be widened in order to accommodate a larger sampling selection in order to produce more accurate data.

 

Weaknesses

         There were several weaknesses in our study that prevented it from being completely accurate. Numerous issues with our sampling technique were present. Among these were the fact that the range of cars we wanted to sample was only between 2000 and 2011. This limited our options of which cars could even be included in the study. We had to leave some vehicles out of our count if they weren’t included in what we think is a reasonable age range for the cars in function in North Olmsted. Another issue we had was how each of our three parking lots were of similar sizes, and the fact that they weren’t necessarily a good sampling area because they were not always filled up. This brings us to the other issue of the parking lots having different population densities at different times of the day. For example, on a Friday night , the parking lot at the Westbury was usually not entirely full, as a result of people going out. To combat this issue, we tried to take samples at all different times during the day, but the fact that we had trouble controlling this factor certainly weakened our study.

 

Suggestions

     There are several ways we could widen this study in order to make our sample population more representative of the population of red and green cars in the U.S. If we could include more diverse samples, we could also take into consideration other aspects of the population. For example, we could sample and stratify parking lots one of each type of establishment such as: restaurant, business, apartment building, grocery store, movie theater, etc. Furthermore, we should take samples at the same times across multiple days in order to produce more accurate data. With the extra control we would have over extraneous factors, our data would be more precise.