To determine if there is a significant relationship between sales performance level and level of investment in customer satisfaction, we will use the Chi-Square Test of Independence. This test compares the observed frequencies to the expected frequencies under the assumption that the two variables (sales performance level and investment in customer satisfaction) are independent.

Steps to calculate the test statistic:

1. Observed Frequencies: These are the values given in the contingency table:

Investment in Customer Satisfaction	Top Performers	Middle Performers	Bottom Performers	Row Totals
Annually	59	303	43	405
Every 2 to 4 years	35	232	22	289
Never	9	155	28	192
Column Totals	103	690	93	886

2. Expected Frequencies: The expected frequency for each cell is calculated using the formula:

   $E_{ij} = \frac{(\text{Row Total}_i \times \text{Column Total}_j)}{\text{Grand Total}}$ where $E_{ij}$ is the expected frequency for the cell in the ith row and jth column.

3. Chi-Square Statistic Formula: The test statistic is calculated as:

   $\chi^2 = \sum \frac{(O_{ij} - E_{ij})^2}{E_{ij}}$ where $O_{ij}$ is the observed frequency and $E_{ij}$ is the expected frequency.

Let's compute these values.The calculated Chi-Square test statistic is approximately $\chi^2 = 17.28$.

Would you like further details on the next steps, or do you have any specific questions?

Here are 5 related questions to explore:

1. How do you calculate the degrees of freedom for the Chi-Square test?
2. What is the critical value for the Chi-Square test at the 0.01 significance level?
3. How do you interpret the Chi-Square test results?
4. Can the Chi-Square test be used for continuous data?
5. What are the assumptions of the Chi-Square test?

Tip: Always ensure that the expected frequencies in each cell are sufficiently large (generally at least 5) to apply the Chi-Square test reliably.