Use the accompanying 200 Los Angeles commute times to test the claim that the mean Los Angeles commute time is less than 37 minutes. Assume that a simple random sample has been selected. Use a 0.01 significance level. Compare the sample mean to the claimed mean of 37 minutes. Is the difference between those two values statistically significant?
Click the icon to view the Los Angeles commute times.
Identify the null and alternative hypotheses.
\[
\begin{array}{l}
H_{0}: \mu=37 \\
H_{1}: \mu=37
\end{array}
\]
(Type integers or decimals. Do not round.)
Identify the test statistic.
(Round to two decimal places as needed.)

Step 1 ：Set up the null and alternative hypotheses. The null hypothesis is that the mean commute time is equal to 37 minutes, represented as \(H_{0}: \mu=37\). The alternative hypothesis is that the mean commute time is less than 37 minutes, represented as \(H_{1}: \mu<37\).

Step 2 ：Calculate the test statistic. The test statistic is a measure of how far our sample mean is from the hypothesized population mean, in terms of standard errors. It is calculated as (sample mean - hypothesized mean) / standard error. The standard error is the standard deviation of the sample divided by the square root of the sample size.

Step 3 ：However, without the sample data, we can't calculate the test statistic. If we had the sample data, we could calculate the sample mean and standard deviation, and then use these to calculate the test statistic.

Step 4 ：We could then compare the test statistic to a critical value from the t-distribution (since we don't know the population standard deviation) to determine whether to reject the null hypothesis.

Step 5 ：Without the sample data, we can't proceed with the hypothesis test. So, the final answer is that we can't identify the test statistic without the sample data.

Step 6 ：Final Answer: \(\boxed{\text{We can't identify the test statistic without the sample data.}}\)