1. **Stating the problem:** We want to test if the proportion of new electric cars bought in 2024 is different from 19.2% (the 2023 proportion). This is a hypothesis test for a population proportion. 2. **Hypotheses:** - Null hypothesis $H_0$: $p = 0.192$ (the proportion in 2024 is the same as 2023) - Alternative hypothesis $H_1$: $p \neq 0.192$ (the proportion in 2024 is different) 3. **Significance level:** $\alpha = 0.05$ 4. **Test statistic formula:** $$ z = \frac{\hat{p} - p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}} $$ where $\hat{p}$ is the sample proportion in 2024, $p_0=0.192$ is the 2023 proportion, and $n$ is the sample size. 5. **Using answers from parts (c)(i) and (c)(ii):** - Suppose from (c)(i) and (c)(ii) we have $\hat{p} = 0.23$ and $n=500$ (example values, replace with actual values from previous parts). 6. **Calculate the standard error:** $$ SE = \sqrt{\frac{0.192 \times (1-0.192)}{500}} = \sqrt{\frac{0.192 \times 0.808}{500}} = \sqrt{\frac{0.155136}{500}} = \sqrt{0.000310272} \approx 0.01761 $$ 7. **Calculate the test statistic:** $$ z = \frac{0.23 - 0.192}{0.01761} = \frac{0.038}{0.01761} \approx 2.16 $$ 8. **Determine critical value:** For a two-tailed test at 5% significance, critical z-values are $\pm 1.96$. 9. **Decision:** Since $|2.16| > 1.96$, we reject the null hypothesis. 10. **Conclusion:** There is sufficient evidence at the 5% level to conclude that the proportion of new electric cars bought in 2024 is different from 19.2% in 2023. 11. **Reason:** The test statistic falls in the rejection region, indicating the sample proportion differs significantly from the 2023 proportion.