A boat heading out to sea starts out at Point AA, at a horizontal distance of 1023 feet from a lighthouse/the shore. From that point, the boat’s crew measures the angle of elevation to the lighthouse’s beacon-light from that point to be 11^{\circ} ∘ . At some later time, the crew measures the angle of elevation from point BB to be 2^{\circ} ∘ . Find the distance from point AA to point BB. Round your answer to the nearest foot if necessary.

Answers

Answer 1

Answer:22bb square roots

Explanation:none

Answer 2

A boat heading out to sea starts out at Point AA, at a horizontal distance of 1023 feet from a lighthouse/the shore. The distance from point AA to point BB is  -1114.01 feet.

What is the distance?

Distance is a numerical or qualitative measurement of the distance between two objects or places.

The distance of point A from the lighthouse = 1023 feet

The angle of elevation from point A to the lighthouse = 11°

The angle of elevation from point B to the lighthouse = 2°

tanΘ = opposite side to angle / adjacent side to the angle

tan7° = height from sea to the lighthouse / 1023 feet

Height to the lighthouse = tan(11°) × 1023 feet

0.1944 x  1023 feet =  198.8712

For point B

tan3° = 198.8712 feet

Horizontal distance =  198.8712 / tan3°

tan3° = -2.1850

Horizontal distance =  198.8712 / -2.1850 = -91.0165

D = Horizontal distance from B to LH - Horizontal distance from A to LH

Distance from A to B

-91.0165 - 1023 feet =  -1114.01

Therefore, the distance from point AA to point BB is  -1114.01 feet.

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The error variance can be Illustrated as σ^2 = E(μ^2) = Var(μ) = (1/n) * ∑(y - (b0 + b1x))^2

How to illustrate the error variance?

The simple linear regression model is of the form:

y = b₀ + b₁x + μ

y = the dependent variable

x = the independent variable

b₀ and b₁ = the parameters of the model, and μ is the error term.

The error term represents the difference between the observed value of the dependent variable and the value predicted by the model. In other words, it is the deviation of the observed y from the expected value of y, given the value of x.

The error variance, denoted by σ^2, is a measure of the spread of the error term. It is calculated as the average squared deviation of the error term from its mean value, which is zero:

σ^2 = E(μ^2) = Var(μ) = (1/n) * ∑(y - (b₀ + b₁x))^2

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