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Refor to the accompanying data display that results from a sample of airport data speods in Mbps. Complete parts (a) through (c) below. Click the icon to view a t distribution table. a. What is the number of degrees of freedom that should be used for finding the critical value \( t_{\alpha / 2} \) ? \[ \mathrm{df}=49 \] (Type a whole number.) b. Find the critical value \( t_{\alpha / 2} \) corresponding to a \( 95 \% \) confidence level. \[ t_{\alpha / 2}= \] \( \square \) (Round to two decimal places as needed.)

Ask by Schultz Davey. in the United States
Mar 15,2025

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\( \textbf{(a)} \) Degrees of freedom: 49 \( \textbf{(b)} \) Critical value \( t_{\alpha / 2} \approx 2.01 \)

Solution

\( \textbf{(a)} \) The sample size \( n = 50 \) (since the degrees of freedom are \( n-1 \)). Thus, the degrees of freedom are \[ \mathrm{df} = 50 - 1 = 49. \] \( \textbf{(b)} \) For a \(95\%\) confidence level the significance level is \( \alpha = 0.05 \), so the area in each tail of the \( t \) distribution is \[ \frac{\alpha}{2} = 0.025. \] Using the \( t \) distribution table for \(\mathrm{df} = 49\), the critical value \( t_{0.025} \) is approximately \[ t_{0.025} \approx 2.01. \]

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When working with confidence intervals and the t-distribution, it’s important to understand that the critical value illuminates the degree of uncertainty in your estimates. For a 95% confidence level with 49 degrees of freedom, the t-value reflects how much room you can allow for sampling variability. This typically means checking the t-table and finding the critical value that corresponds to the upper tail of 0.025 (since 0.05 is split into two tails). Now, using the t-distribution table for \( df = 49 \) at a 95% confidence level, you'll find that \( t_{\alpha/2} \approx 2.009 \). Remember to always round to two decimal places when reporting the value to keep precision neat and tidy! For many analyses, using a t-distribution is vital when sample sizes are smaller than 30 or the population standard deviation is unknown. Just make sure you're clear about the data and its underlying distribution!

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