A local government wants to install red light cameras on specific intersections to reduce the number of accidents from cars running red lights. They would like to know the current level of support from the public. A random sample is found by calling 50 registered voters from each of the five districts. The sample result was that \( 62 \% \) of those surveyed were in support of adding the red light cameras. Match with the appropriate concept. \( \begin{array}{l}\text { Each districts } 50 \text { registered } \\ \text { voters that were surveyed. } \\ \text { 1. Sample for the Survey. } \\ \text { fall registered of red light cameras. } \\ \text { five districts. }\end{array} \)

The approach of surveying 50 registered voters from each of the five districts is a classic example of stratified sampling. This method allows researchers to ensure that different segments of the population are represented, which is particularly important in diverse communities. By focusing on each district, the government can better gauge public opinion and implement the red light cameras in a way that reflects the preferences of all constituents! In real-world scenarios, it’s crucial to interpret survey data accurately. While a 62% approval rate sounds promising, it’s essential to consider factors such as the margin of error and how responses might be influenced by different demographics. Also, engaging the public further through informational sessions about the benefits and goals of red light cameras can help fine-tune the proposal and address any concerns the community may have.