Let's calculate the values:
1. **Value of the R phenotype observed:** 20 red birds
2. **Value of the r phenotype observed:** 180 green birds
Expected values were previously calculated:
3. **Value of the R phenotype expected:** 107
4. **Value of the r phenotype expected:** 14
Now, let's calculate the \( X^2 \) value:
\[ X^2 = \frac{{(O_1 - E_1)^2}}{E_1} + \frac{{(O_2 - E_2)^2}}{E_2} \]
Where:
- \( O_1 \) = Observed count of red birds = 20
- \( E_1 \) = Expected count of red birds = 107
- \( O_2 \) = Observed count of green birds = 180
- \( E_2 \) = Expected count of green birds = 14
Calculating \( X^2 \):
\[ X^2 = \frac{{(20 - 107)^2}}{107} + \frac{{(180 - 14)^2}}{14} \]
\[ X^2 = \frac{{(-87)^2}}{107} + \frac{{(166)^2}}{14} \]
\[ X^2 = \frac{{7569}}{107} + \frac{{27556}}{14} \]
\[ X^2 = 70.7664 + 1968.2857 \]
\[ X^2 ≈ 2039.0521 \]
6. **\( X^2 \) value:** \( \approx 2039.05 \)
Now, to find the p-value, we would use a Chi-squared distribution table with one degree of freedom and compare the \( X^2 \) value obtained to determine the significance level.