Let's solve this problem step by step:

Part (a): State the null hypothesis $H_0$ and the alternate hypothesis $H_1$.

We are testing whether there is a difference between the mean distances (in thousands of km) driven before the tires need replacement for the two brands, Puma and Eternal.

• Null Hypothesis $H_0$: There is no difference in the population mean distances driven before replacement for Puma and Eternal tires. $H_0: \mu_1 - \mu_2 = 0$ where $\mu_1$ is the mean distance for Puma tires and $\mu_2$ is the mean distance for Eternal tires.

• Alternative Hypothesis $H_1$: There is a difference in the population mean distances driven before replacement for Puma and Eternal tires. $H_1: \mu_1 - \mu_2 \neq 0$

Part (b): Determine the type of test statistic to use.

Since we are comparing the means of two independent samples and we know the sample variances, we will use a two-sample t-test. The appropriate test statistic for comparing two means is given by: $t = \frac{(\bar{x}_1 - \bar{x}_2)}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}$ where:

• $\bar{x}_1 = 61.23$ (sample mean for Puma)
• $\bar{x}_2 = 52.28$ (sample mean for Eternal)
• $s_1^2 = 5.44$ (sample variance for Puma)
• $s_2^2 = 78.88$ (sample variance for Eternal)
• $n_1 = 14$ (sample size for Puma)
• $n_2 = 15$ (sample size for Eternal)

The degrees of freedom for this test can be calculated using the formula: $df = \frac{\left(\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}\right)^2}{\frac{\left(\frac{s_1^2}{n_1}\right)^2}{n_1-1} + \frac{\left(\frac{s_2^2}{n_2}\right)^2}{n_2-1}}$

Would you like me to continue with the calculations to complete the hypothesis test?

Additional questions:

1. How do we calculate the p-value for this two-tailed test?
2. Why do we use a two-tailed test in this scenario?
3. What would be the conclusion if the test statistic falls outside the critical values at the 0.01 significance level?
4. How does the sample size affect the power of the hypothesis test?
5. What assumptions must be met for the two-sample t-test to be valid?

Tip: When conducting hypothesis testing, always check if the assumptions (normality, independence, etc.) are met to ensure valid results.