Answer:
If a female child has hemophilia it is possible that the mother is a carrier of the hemophilia gene and the father has hemophilia.
Explanation:
- A daughter gets X chromosome from both her parents.
- It is generally seen in males.
- Hemophilia is generally recessive in females.They act as carriers of hemophilia. This occurs because they have a X chromosome that dominates the hemophilia affected gene that they inherit from any parent.
- But, if both the parents have faulty genes ,i.e the mother is the carrier of the gene and the father is hemophiliac, then the chances are the daughter has hemophilia too.
Answer:
By looking at the gene sequences. See the mutations.
Explanation:
<span>Steroids are like hormones that greatly influences bodily activities mostly in growth and function. These functions may modify, change and control one’s body organs or system that’ll vary in form, shape or structure due to these hormone-like compounds and also, it can affect one’s psychological and neurological state. There are several types of steroids. Examples can include: </span><span><span>
1. </span>Sex steroids. These are can influence reproduction and sex characteristics.</span> <span><span>
2. </span>Anabolic steroids. Most abused in many athletic activities. These steroids are used for muscle and bone growth.<span>
</span></span>
Imagine you are surveying a population of a mountain range where the inhabitants live in the valleys with no inhabitants on the large mountains between. If your sample area is the valleys, and you use this to estimate the population across the entire mountain range, <u>you overestimate the actual population size</u>
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Explanation:
- An estimate that turns out to be incorrect will be an overestimate if the estimate exceeded the actual result, and an underestimate if the estimate fell short of the actual result.
- The mean of the sampling distribution of a statistic is sometimes referred to as the expected value of the statistic. Therefore the sample mean is an unbiased estimate of μ.
- Any given sample mean may underestimate or overestimate μ, but there is no systematic tendency for sample means to either under or overestimate μ.
- Bias is the tendency of a statistic to overestimate or underestimate a parameter. Bias can seep into your results for a slew of reasons including sampling or measurement errors, or unrepresentative samples
