The research problem is:
The address does not matter in the underlying population.
The hypothesis being tested is:
H0: Address does not matter in the underlying population
Ha: Address matters in the underlying population
Col 1 | Col 2 | Total | ||
Row 1 | Observed | 72 | 28 | 100 |
Expected | 48.25 | 51.75 | 100.00 | |
O - E | 23.75 | -23.75 | 0.00 | |
(O - E)² / E | 11.69 | 10.90 | 22.59 | |
Row 2 | Observed | 71 | 29 | 100 |
Expected | 48.25 | 51.75 | 100.00 | |
O - E | 22.75 | -22.75 | 0.00 | |
(O - E)² / E | 10.73 | 10.00 | 20.73 | |
Row 3 | Observed | 25 | 75 | 100 |
Expected | 48.25 | 51.75 | 100.00 | |
O - E | -23.25 | 23.25 | 0.00 | |
(O - E)² / E | 11.20 | 10.45 | 21.65 | |
Row 4 | Observed | 25 | 75 | 100 |
Expected | 48.25 | 51.75 | 100.00 | |
O - E | -23.25 | 23.25 | 0.00 | |
(O - E)² / E | 11.20 | 10.45 | 21.65 | |
Total | Observed | 193 | 207 | 400 |
Expected | 193.00 | 207.00 | 400.00 | |
O - E | 0.00 | 0.00 | 0.00 | |
(O - E)² / E | 44.82 | 41.79 | 86.62 | |
86.62 | chi-square | |||
3 | df | |||
1.17E-18 | p-value | |||
7.815 | critical value |
The df is 3.
The critical value is 7.815.
The value of X2 is 86.62.
The p-value is 0.0000.
Since the p-value (0.0000) is less than the significance level (0.05), we can reject the null hypothesis.
Therefore, we can conclude that address matters in the underlying population.
Effect size = √X2/nt = √86.62/400*1 = 0.465
This is a very large effect size.
Odds ratio = [143/(143+57)]/(50/(50+150)] = 2.86
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