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tatiyna
3 years ago
7

Cone W has a radius of 10 cm and a height of 5 cm. Square pyramid X has the same base area and height as cone W.

Mathematics
1 answer:
Brrunno [24]3 years ago
4 0

This question is incomplete because it lacks the appropriate attachment containing the argument of Paul and Manuel

Kindly find attached the appropriate attachment necessary to solve this question.

The attachment was sourced online

Answer:

a) Paul's argument is correct, Manuel used the wrong formula to find the volume of square pyramid X

Step-by-step explanation:

From the above question, we are given two shapes: Cone W and Square Pyramid

Cone W: Radius = 8cm, Height = 5cm

Volume of a Cone = 1/3 πr²h

Where π = 3.14

Volume of Cone X = 1/3 × 3.14 × 8² × 5

= 334.93cm³

For Square pyramid X

Volume = 1/3 × Base Area × Height

Base Area of Cone W = Base Area of square pyramid X = πr²

Where π = 3.14

Base area = 3.14 × 8² = 200.96cm²

Height of Square pyramid X = Height of Cone W = 5cm

Volume of Square pyramid = 1/3 ×200.96cm² × 5cm

= 334.93cm³

Therefore, from my above calculation and compared with the arguments of Paul and Manuel, Option a) "Paul's argument is correct, Manuel used the wrong formula to find the volume of square pyramid X" is the correct option.

The reason why is because the correct formula for the volume of a square pyramid = 1/3 × Base area × Height

This was the formula Paul used.

Manuel on the other hand used the formula: Base Area × Height to find the volume of cone W. This formula is wrong.

Option a, is the correct answer.

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Answer:

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Step-by-step explanation:

The general form of a linear regression is:

y=a+bx

Here,

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\begin{aligned}        a &= \frac{\sum{Y} \cdot \sum{X^2} - \sum{X} \cdot \sum{XY} }{n \cdot \sum{X^2} - \left(\sum{X}\right)^2} =             \frac{ 6857 \cdot 55 - 15 \cdot 17558}{ 6 \cdot 55 - 15^2} \approx 1083.476 \\ \\b &= \frac{ n \cdot \sum{XY} - \sum{X} \cdot \sum{Y}}{n \cdot \sum{X^2} - \left(\sum{X}\right)^2}        = \frac{ 6 \cdot 17558 - 15 \cdot 6857 }{ 6 \cdot 55 - \left( 15 \right)^2} \approx 23.743\end{aligned}

The linear regression equation that represents  the set of data is:

y=1083.48+23.73x

Compute the predicted value of the number of new cases for 2013 (i.e. <em>x</em> = 12) as follows:

y=1083.48+23.73x

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Thus, the predicted number of new cases for 2013 is 1368.

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