An automaker is interested in information about how long transmissions last. A sample of transmissions is run constantly, and the number of miles before the transmission fails is recorded. The automaker claims that the transmissions can run constantly for over 150,000 miles before failure. The results of the sample are given below.

\begin{tabular}{l|r}
\hline \multicolumn{1}{c|}{ Miles (1000s of miles) } & \\
\hline Mean & 150.7 \\
\hline Variance & 4.551 \\
\hline Observations & 42 \\
\hline Hypothesized Mean & 150 \\
\hline df & 41 \\
\hline [tex]$t$[/tex] Stat & 2.17 \\
\hline [tex]$P($[/tex] (Test) one-tail & 0.018 \\
\hline [tex]$t$[/tex] Critical one-tail & 1.683 \\
\hline [tex]$P($[/tex] (Test) two-tail & 0.036 \\
\hline [tex]$t$[/tex] Critical two-tail & 2.02 \\
\hline Confidence Level ([tex]$95.0\%$[/tex]) & 0.665 \\
\hline
\end{tabular}

\begin{array}{ll}
n=\text{ Ex: } 5 & \bar{x}=\text{ Ex: } 1.2 \\
\text{Degrees of freedom}=\text{ Ex: } 5 & s=\text{ Ex: } 1.234
\end{array}



Answer :

Alright, let’s analyze the given data and solve the problem step-by-step.

### Problem Interpretation
The auto maker wants to determine statistically whether the transmissions last longer than 150,000 miles on average. To do this, a sample is tested, and results are given.

### Step-by-Step Solution

1. Formulate the Hypotheses:

- Null Hypothesis ([tex]\(H_0\)[/tex]): The mean mileage before failure is not greater than 150,000 miles (i.e., [tex]\(\mu \leq 150\)[/tex]).
- Alternative Hypothesis ([tex]\(H_a\)[/tex]): The mean mileage before failure is greater than 150,000 miles (i.e., [tex]\(\mu > 150\)[/tex]).

This is a one-tailed test.

2. Sample Statistics:
- Sample mean ([tex]\(\bar{x}\)[/tex]): 150.7 thousand miles
- Sample variance ([tex]\(s^2\)[/tex]): 4.551 thousand miles
- Sample size ([tex]\(n\)[/tex]): 42
- Hypothesized population mean ([tex]\(\mu_0\)[/tex]): 150 thousand miles

3. Test Statistic:
- Given the [tex]\(t\)[/tex] statistic: 2.17

4. Degrees of Freedom (df):
- [tex]\(df = n - 1 = 42 - 1 = 41\)[/tex]

5. Critical [tex]\(t\)[/tex] Value for One-Tailed Test:
- Given the critical [tex]\(t\)[/tex] value for a one-tailed test at the chosen significance level: 1.683

6. P-Value for One-Tailed Test:
- Given the [tex]\(p\)[/tex] value for one-tailed test: 0.018

### Decision Rule

- If the [tex]\(t\)[/tex] statistic is greater than the critical [tex]\(t\)[/tex] value (1.683), we reject the null hypothesis.
- Alternatively, if the p-value is less than the significance level ([tex]\(\alpha\)[/tex]), we reject the null hypothesis. Typically, [tex]\(\alpha = 0.05\)[/tex] for a 95% confidence level.

### Analysis

- Our [tex]\(t\)[/tex] statistic (2.17) is greater than the critical [tex]\(t\)[/tex] value (1.683).
- The p-value for the one-tailed test (0.018) is less than the typical significance level of 0.05.

### Conclusion

Given that the [tex]\(t\)[/tex] statistic is greater than the critical [tex]\(t\)[/tex] value and the p-value is less than the significance level, we reject the null hypothesis. There is sufficient evidence at the 95% confidence level to support the claim that the transmissions last more than 150,000 miles on average.

### Additional Information

- Two-Tailed Test Information:
- Critical [tex]\(t\)[/tex] value for two-tailed test: 2.02
- p-value for two-tailed test: 0.036

- Confidence Interval:
- The margin for the 95% confidence interval was given as 0.665 thousand miles.

- Although our primary analysis was for a one-tailed test, provided values for a two-tailed test and confidence intervals can be useful for other related inferences or checks.

This comprehensive step-by-step solution concludes that the transmissions are statistically shown to last more than 150,000 miles before failure.