The task requires calculating the missing test statistic $t$, interpreting the results, and determining the correct answer based on the given data.

Solution Steps:

1. Identify the correlation coefficient $r$: Given $r = -0.471$.

2. Calculate the test statistic $t$: The formula for $t$ is: $t = \frac{r \sqrt{n - 2}}{\sqrt{1 - r^2}}$ Where $n$ is the sample size (number of data points).

3. Determine the sample size: From the data, $n = 5$ (5 data points).

4. Substitute into the formula: $t = \frac{-0.471 \sqrt{5 - 2}}{\sqrt{1 - (-0.471)^2}}$

   Simplify step-by-step:

   • $r^2 = (-0.471)^2 = 0.221841$
   • $1 - r^2 = 1 - 0.221841 = 0.778159$
   • $\sqrt{n - 2} = \sqrt{3} \approx 1.732$
   • $\sqrt{1 - r^2} = \sqrt{0.778159} \approx 0.882$
   • $t = \frac{-0.471 \cdot 1.732}{0.882}$

   Final computation: $t \approx \frac{-0.816732}{0.882} \approx -0.926$

   So, $t \approx -0.926$.

5. Interpret the P-value and significance level:

   • Given $P = 0.669$, which is greater than $\alpha = 0.05$, we fail to reject the null hypothesis.

6. Conclusion:

   • The correlation is not statistically significant.
   • There is insufficient evidence to suggest a linear relationship between lemon imports and crash fatality rates.

7. Correct Answer:

   • The correct option is A: The results do not suggest any cause-effect relationship between the two variables.

Would you like me to explain any of the steps in more detail or clarify further? Here are some follow-up questions you might explore:

1. What does a P-value signify in hypothesis testing?
2. How is the test statistic $t$ used to assess significance?
3. Why can correlation not imply causation?
4. How does the sample size $n$ impact the test statistic?
5. Can a negative $r$ still indicate a strong linear relationship?

Tip: Always examine whether the context and data make a causal relationship plausible before making conclusions!