With another study, where you also plan on evaluating a mean using the t statistic, you have a sample of n = 41 that has an SS of 600. What is the variance for the sample? 360,000 3.87 O 24.49 15 For a sample of n = 36 that has a sample variance of 1,296, what is the estimated standard error for the sample? 37 6 О 6.09 О 36 0000

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**Educational Content: Statistical Analysis**

### Sample Variance Calculation
In a study where you plan to evaluate a mean using the t-statistic, a sample is taken with the following data:
- Sample size (n) = 41
- Sum of Squares (SS) = 600

**Question:** What is the variance for the sample?

**Answer Options:**
- 360,000
- 3.87
- 24.49
- 15

### Estimated Standard Error Calculation
For another set of data:
- Sample size (n) = 36
- Sample variance = 1,296

**Question:** What is the estimated standard error for the sample?

**Answer Options:**
- 37
- 6
- 6.09
- 36

### Explanation:
1. **Sample Variance Formula:**  
   \[
   \text{Variance} = \frac{\text{SS}}{n-1}
   \]

2. **Standard Error Formula:**  
   \[
   \text{Standard Error} = \sqrt{\frac{\text{Sample Variance}}{n}}
   \]  

These calculations are fundamental when interpreting data and determining the reliability of statistical conclusions.
Transcribed Image Text:**Educational Content: Statistical Analysis** ### Sample Variance Calculation In a study where you plan to evaluate a mean using the t-statistic, a sample is taken with the following data: - Sample size (n) = 41 - Sum of Squares (SS) = 600 **Question:** What is the variance for the sample? **Answer Options:** - 360,000 - 3.87 - 24.49 - 15 ### Estimated Standard Error Calculation For another set of data: - Sample size (n) = 36 - Sample variance = 1,296 **Question:** What is the estimated standard error for the sample? **Answer Options:** - 37 - 6 - 6.09 - 36 ### Explanation: 1. **Sample Variance Formula:** \[ \text{Variance} = \frac{\text{SS}}{n-1} \] 2. **Standard Error Formula:** \[ \text{Standard Error} = \sqrt{\frac{\text{Sample Variance}}{n}} \] These calculations are fundamental when interpreting data and determining the reliability of statistical conclusions.
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