Q#3: Two separate samples receive different treatments. After treatment, the first sample has n = 5 with SS = 79, and the second has n = 11 with SS = 83. Calculate the estimated standard error for the sample mean difference.

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**Question 3:** Two separate samples receive different treatments. After treatment, the first sample has \( n = 5 \) with \( SS = 79 \), and the second has \( n = 11 \) with \( SS = 83 \). Calculate the estimated standard error for the sample mean difference.

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For educational purposes, the problem requires an understanding of statistical concepts such as sample size (\( n \)) and the sum of squares (\( SS \)), which are used to compute the estimated standard error for the sample mean difference. The formula for the standard error of the difference between two means is:

\[
SE = \sqrt{\left(\frac{SS_1}{n_1 - 1}\right) + \left(\frac{SS_2}{n_2 - 1}\right)}
\]

Where:
- \( SS_1 \) and \( SS_2 \) are the sum of squares for each sample.
- \( n_1 \) and \( n_2 \) are the sample sizes.

Using this information, learners can practice computing the standard error to understand the variability between sample means when comparing different treatments.
Transcribed Image Text:**Question 3:** Two separate samples receive different treatments. After treatment, the first sample has \( n = 5 \) with \( SS = 79 \), and the second has \( n = 11 \) with \( SS = 83 \). Calculate the estimated standard error for the sample mean difference. --- For educational purposes, the problem requires an understanding of statistical concepts such as sample size (\( n \)) and the sum of squares (\( SS \)), which are used to compute the estimated standard error for the sample mean difference. The formula for the standard error of the difference between two means is: \[ SE = \sqrt{\left(\frac{SS_1}{n_1 - 1}\right) + \left(\frac{SS_2}{n_2 - 1}\right)} \] Where: - \( SS_1 \) and \( SS_2 \) are the sum of squares for each sample. - \( n_1 \) and \( n_2 \) are the sample sizes. Using this information, learners can practice computing the standard error to understand the variability between sample means when comparing different treatments.
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