Question 1189355
Here's how to conduct an F-test to compare the variances of the load times for the two web pages:

**1. State the Hypotheses:**

*   Null Hypothesis (H₀): The variances of the load times for the new page and the current page are equal.  σ₁² = σ₂²  (where σ₁² is the variance of the new page and σ₂² is the variance of the current page).
*   Alternative Hypothesis (H₁): The variance of the load time for the new page is greater than the variance of the load time for the current page. σ₁² > σ₂² (We're testing for *more* variation, so it's a one-tailed test.)

**2. Determine the Level of Significance:**

α = 0.05 (given)

**3. Calculate the F-Statistic:**

The F-statistic is the ratio of the larger sample variance to the smaller sample variance.  In this case:

F = s₁² / s₂²

Where:

*   s₁² = sample variance of the new page = (22)² = 484
*   s₂² = sample variance of the current page = (10)² = 100

F = 484 / 100 = 4.84

**4. Determine the Degrees of Freedom:**

*   df₁ = n₁ - 1 = 16 - 1 = 15 (numerator degrees of freedom)
*   df₂ = n₂ - 1 = *We don't have n₂ for the current page. We will assume it is large enough that it does not impact the result.*

**5. Find the Critical Value:**

Using an F-distribution table or calculator, look up the critical value for a one-tailed test with α = 0.05, df₁ = 15, and a large df₂. The critical value is approximately 2.40.

**6. Make a Decision:**

*   Compare the calculated F-statistic to the critical value: Our calculated F (4.84) is greater than the critical value (2.40).

*   Conclusion: Because our F-statistic exceeds the critical value, we reject the null hypothesis.

**7. Interpret the Results:**

There is sufficient evidence at the 0.05 significance level to conclude that there is more variation in the load time of the new web page compared to the current web page.