Answer:
The correct answer is C.
Explanation:
In the question, it is stated that the woman is heterozygous, which means she posseses both the dominant allele and the recessive allele (A and a). So taking this information into account, she can produce eggs given with the option C (Aa) which include both the dominant and recessive alleles.
I hope this answer helps.
Answer:
c. 1:2:1
The results are consistent with incomplete dominance for this trait, with pink flowers being heterozygous.
Explanation:
If flower color were determined by a gene showing incomplete dominance, the possible genotypes and phenotypes are as follows:
- RR- red
- ww - white
- Rw - pink
If pink sweet peas are self-pollinated, then a cross between two heterozygous individuals is done (Rw x Rw).
<u>From this cross the expected ratios are:</u>
- 1/4 RR (red)
- 2/4 Rw (pink)
- 1/4 ww (white)
So the null hypothesis is that the observed results exhibit a 1:2:1 ratio.
<h3><u>Chi square test</u></h3>

<u>The observed frequencies were:</u>
Total 150
<u>The expected frequencies for our null hypothesis are:</u>
- 1/4 x 150 = 37.5 Red
- 2/4 x 150 = 75 Pink
- 1/4 x 150 = 37.5 white


The degrees of freedom (DF) are calculated as number of phenotypes - 1; in this case DF = 3-1 = 2.
If we look at the Chi square table, for 2 DF and a probability of p0.05, the critical value is 5.991
Our X^2 value of 0.5067 is less than the critical value, so we do not reject the null hypothesis. The results are consistent with incomplete dominance for this trait, with pink flowers being heterozygous.
Answer:d.Additional evidence will change the theory of evolution into a law.
Explanation:
Statistical power is the likelihood that a test (statistical test) will detect an effect when there is an effect there to be detected. Statistical power<span> is inversely related to </span><span>the probability of making a </span>Type II error (Type II errors<span>, or </span>false negatives, occur when you don’t see things that are there) = beta<span>.
statistical power = 1 – </span>β. The critical value<span> is the </span>value corresponding to a given significance level. The statistical power<span> is </span>influenced by the choice of significance level for the test (by the critical value). Larger critical value means increased power of the test: <span> the chance of obtaining a statistically significant result is increased (reduces the risk of a </span>Type II error<span> (false negative regarding whether an effect exists) is reduced) . </span>