I can see the tables you've provided with calculations. Let's work on completing them.

We have the following data for each table:

Table 2.2 (Pipette Measurements)

Given:

• $\Sigma Pa = 97.3$
• $\Sigma (Pa - \bar{X})^2 = 0.2631$

To calculate:

• Mean $\bar{X} = \frac{\Sigma Pa}{n}$
• Variance $S^2 = \frac{\Sigma (Pa - \bar{X})^2}{n-1}$
• Standard deviation $S = \sqrt{S^2}$

For $n = 10$:

• $\bar{X} = \frac{97.3}{10} = 9.73$
• $S^2 = \frac{0.2631}{10-1} = \frac{0.2631}{9}$
• $S = \sqrt{S^2}$

Table 2.3 (Probeta Measurements)

You need to calculate the sums and complete the table similarly:

• $\Sigma Pa$
• $\Sigma (Pa - \bar{X})^2$

I'll proceed with these calculations next.It seems like I can’t do more advanced data analysis right now. Please try again later.

However, here are the steps you can follow to complete the calculations manually:

Table 2.2 (Pipette Measurements)

• Mean $\bar{X} = \frac{\Sigma Pa}{n} = \frac{97.3}{10} = 9.73$
• Variance $S^2 = \frac{\Sigma (Pa - \bar{X})^2}{n-1} = \frac{0.2631}{9} \approx 0.02923$
• Standard deviation $S = \sqrt{S^2} \approx \sqrt{0.02923} \approx 0.171$

Table 2.3 (Probeta Measurements)

You would follow a similar process:

1. Add up all the $Pa$ values to get $\Sigma Pa$.
2. Compute the mean $\bar{X}$ for the data.
3. Calculate $(Pa - \bar{X})^2$ and sum these values.
4. Finally, calculate the variance and standard deviation as shown in Table 2.2.

If you'd like, I can walk you through completing these calculations in detail.

Would you like assistance on the next steps or any specific part? Here are some related questions for further practice:

1. How would you interpret the standard deviation in this context?
2. What are the steps to calculate variance from raw data?
3. How do these results help compare the accuracy of both instruments?
4. What impact does sample size have on standard deviation?
5. How would the results change if an outlier was introduced?

Tip: When calculating variance, always remember to use $n - 1$ (for sample variance) to correct for bias in small datasets.