To calculate this, the Hardy-Weinberg principle can be used:
p² + 2pq + q² = 1 and p + q = 1
where p and q are the frequencies of the alleles (p - dominant, q - recessive), and p², q² and 2pq are the frequencies of the genotypes.
a) Since 32 plants have rough seed (recessive genotype: q²) out of 100 plants in total, then
q² = 32/100 = 0.32
b) q = √q² = √0.32 = 0.56
c) Since p + q = 1, then
p = 1 - q = 1 - 0.56 = 0.44
d) 19 plants with rough seeds (recessive genotype: q²) in a population of 100 means that q² = 19/100 = 0.19
We need to calculate p (the allele frequency for smooth seeds).
We can find q because we know q²:
q = √q² = √0.19 = 0.44
Since p + q = 1, then
p = 1 - q = 1 - 0.4 = 0.56
Answer: This is what Khan Academy said the answer was
Answer:
1.75
Step-by-step explanation:
If a and b are two numbers, then their arithmetic mean is

Given:

Divide this equation by 10:

Now, divide it by 2:

<h3>
Answer:</h3>
- using y = x, the error is about 0.1812
- using y = (x -π/4 +1)/√2, the error is about 0.02620
<h3>
Step-by-step explanation:</h3>
The actual value of sin(π/3) is (√3)/2 ≈ 0.86602540.
If the sine function is approximated by y=x (no error at x = 0), then the error at x=π/3 is ...
... x -sin(x) @ x=π/3
... π/3 -(√3)/2 ≈ 0.18117215 ≈ 0.1812
You know right away this is a bad approximation, because the approximate value is π/3 ≈ 1.04719755, a value greater than 1. The range of the sine function is [-1, 1] so there will be no values greater than 1.
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If the sine function is approximated by y=(x+1-π/4)/√2 (no error at x=π/4), then the error at x=π/3 is ...
... (x+1-π/4)/√2 -sin(x) @ x=π/3
... (π/12 +1)/√2 -(√3)/2 ≈ 0.026201500 ≈ 0.02620
Answer:
A is incorrect! <u>B. should be the correct answer</u>
Step-by-step explanation
After some research, i found that the correct answer is most likely <u><em>B. a regression line and trend line are equivalent terms</em></u>
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<em>A trendline and a regression can be the same.
</em>
<em>
A regression line is based upon the best fitting curve Y= a + bX Most often it’s a least-squares fit (where the squared distances from the points to the line (along the Y-axis) is minimized).
</em>
<em>
It can be quadratic or logistic or otherwise, but most often it is linear.
</em>
<em>
A trendline is often constructed by smoothing of the results, making it less peaked. (often by using a moving average); but can also come from ARIMA projections or curve fitting techniques (such as regression).</em>
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Let me know if i helped you!
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