To complete the table, we need to calculate the following values:

1. A (Observed Count for the category "4 - more than 100,001 followers")

   To find this, we can use the total observed count (113) minus the sum of the observed counts for categories 1, 2, and 3.

   $A = 113 - (22 + 28 + 34) = 113 - 84 = 29$

   So, A = 29.

2. B (Expected Count for the category "1 - less than 10,000 followers")

   The expected count is calculated using the total expected count of 113 and the percentage for each category. The percentage for category 1 is 10%. Therefore, we can calculate:

   $B = \frac{10}{100} \times 113 = 0.1 \times 113 = 11.3$

   So, B = 11.3 (this value is already provided in the table).

3. C (Chi-squared value for category "3 - 50,001 to 100,000 followers")

   The chi-squared statistic is calculated using the formula:

   $\chi^2 = \frac{(Observed - Expected)^2}{Expected}$

   For category 3:

   $C = \frac{(34 - 56.5)^2}{56.5} = \frac{(-22.5)^2}{56.5} = \frac{506.25}{56.5} \approx 8.961$

   So, C ≈ 8.961.

Updated Table

	1 (less than 10,000)	2 (10,001-50,000)	3 (50,001-100,000)	4 (more than 100,001)	Total
Observed Count	22	28	34	29	113
Expected Count	11.3	11.3	56.5	33.9	113
Chi-squared	10.132	24.681	8.961	0.708	44.481

Would you like more details or have any other questions?

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Here are five questions that build on this topic:

1. How does the chi-squared statistic help determine the goodness of fit in this problem?
2. What is the null hypothesis in this chi-squared test?
3. How do we calculate degrees of freedom for a chi-squared test in this context?
4. How does changing the observed counts affect the chi-squared value?
5. Why is it important to compare observed and expected counts in statistical analysis?

Tip: The chi-squared test is sensitive to sample size, so a larger sample can make small differences statistically significant.