1. The problem asks to perform an independent two-sample t-test using RStudio on the dataset from Question 1 and interpret the p-value. 2. The independent two-sample t-test compares the means of two independent groups to see if there is a statistically significant difference between them. 3. The formula for the t-test statistic is: $$t = \frac{\bar{x}_1 - \bar{x}_2}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}$$ where $\bar{x}_1, \bar{x}_2$ are sample means, $s_1^2, s_2^2$ are sample variances, and $n_1, n_2$ are sample sizes. 4. In R, the code to run the independent two-sample t-test is: ```R t.test(group1, group2, var.equal = TRUE) ``` where `group1` and `group2` are numeric vectors of the two samples. 5. The output includes the t-statistic, degrees of freedom, and the p-value. 6. Interpretation: If the p-value is less than the significance level (commonly 0.05), we reject the null hypothesis and conclude there is a significant difference between the two groups. 7. Since the dataset from Question 1 is not provided here, you would replace `group1` and `group2` with your actual data vectors. 8. Example output from R might look like: ``` Two Sample t-test data: group1 and group2 t = 2.45, df = 28, p-value = 0.021 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.15 1.85 sample estimates: mean of x mean of y 5.2 4.1 ``` 9. Since the p-value 0.021 < 0.05, we reject the null hypothesis and conclude the means are significantly different. 10. This is how you run and interpret an independent two-sample t-test in RStudio.