The Chi Square Expected Value is a statistical measure used to assess the relationship between observed and expected values in a contingency table. It helps researchers determine whether there is a significant difference between the observed and expected frequencies.

Formula: The Chi Square Expected Value is calculated using the formula: Σ((O-E)² / E), where O is the observed frequency, E is the expected frequency, and Σ denotes the sum over all categories.

How to Use:

- Enter the observed values in the “Observed Values” input, separated by commas.
- Enter the expected values in the “Expected Values” input, separated by commas.
- Click the “Calculate” button to obtain the Chi Square Expected Value.

Example: Suppose you conducted a survey on the preferences of two groups (A and B) regarding a product. The observed values are [25, 30] for group A, and the expected values are [20, 35]. Input these values into the calculator, click “Calculate,” and the Chi Square Expected Value will be displayed.

FAQs:

**What is the Chi Square Expected Value?**The Chi Square Expected Value is a statistical measure used to assess the difference between observed and expected frequencies in a contingency table.**When should I use the Chi Square test?**The Chi Square test is appropriate when analyzing categorical data to determine if there is a significant association between variables.**Can I use this calculator for more than two categories?**Yes, you can. Simply enter the observed and expected values for each category separated by commas.**What does a higher Chi Square Expected Value indicate?**A higher Chi Square Expected Value suggests a greater discrepancy between observed and expected frequencies.**Is this calculator suitable for small sample sizes?**The Chi Square test is more reliable with larger sample sizes, so caution should be exercised when using it with small samples.

Conclusion: The Chi Square Expected Value calculator simplifies the process of assessing the statistical significance of observed and expected frequencies. By inputting your data and utilizing this tool, you can quickly obtain valuable insights into the relationships within categorical data sets.