Step 1 :Identify the null and alternative hypotheses for this test. Let population 1 correspond to the passenger cars and population 2 correspond to the commercial trucks. Let a success be a vehicle that only has a rear license plate. The null and alternative hypotheses are: \[H_{0}: p_{1}=p_{2}\] \[H_{1}: p_{1} Step 2 :Calculate the sample proportions and the pooled sample proportion. For passenger cars (population 1), the sample size n1 is 2082 and the number of successes x1 is 233, so the sample proportion p1 is \(\frac{233}{2082} = 0.112\). For commercial trucks (population 2), the sample size n2 is 332 and the number of successes x2 is 45, so the sample proportion p2 is \(\frac{45}{332} = 0.136\). The pooled sample proportion p is \(\frac{233 + 45}{2082 + 332} = 0.115\). Step 3 :Calculate the test statistic using the formula for a two-proportion z-test: \[z = \frac{p1 - p2}{\sqrt{p(1 - p) * [(1/n1) + (1/n2)]}}\] Substituting the calculated values gives \[z = \frac{0.112 - 0.136}{\sqrt{0.115(1 - 0.115) * [(1/2082) + (1/332)]}} = -1.25\]. Step 4 :Calculate the P-value. The P-value is the probability of observing a test statistic as extreme as the one calculated, assuming the null hypothesis is true. The P-value is 0.105. Step 5 :Since the P-value is greater than the significance level of 0.05, we do not reject the null hypothesis. Therefore, we do not have sufficient evidence to support the claim that commercial trucks owners violate laws requiring front license plates at a higher rate than owners of passenger cars. Step 6 :The final answer is: The null and alternative hypotheses are: \[H_{0}: p_{1}=p_{2}\] \[H_{1}: p_{1}