What is the area of the front view of the hat ?

Please help me my aleks topics are due tomorrow and i will give you brainliest :(

aksik [14]

Answer:

a is 3x

b is 6(8y-5) and 6x8y-6x5

Step-by-step explanation:

first a is 3x

b is 6(8y-5) 6x8y-6x5

5 0

3 years ago

A student graphed f(x)=x and g(x)= f(x)-8 on the same coordinate grid. Which statement best describes how the graph of f and g a

luda_lava [24]

Answer:

The correct option is D

The graph of f is shifted 8 units down to create the graph of g.

Explanation:

Given f(x) = x

If the graph of this function is shifted 8 units down, we have

x - 8

This is the function f(x) - 8

Since g(x) = f(x) - 8, we conclude that the graph of f is shifted 8 units down to create the graph of g.

7 0

2 years ago

If the answer and the explanation are right I'll given brainliest.

Anna007 [38]

Answer:

>

Step-by-step explanation:

d>1/3

d is greater than 1/3

not much to it

8 0

3 years ago

Read 2 more answers

Kelly wants to buy cupcakes for her family. The radius of the cupcake is 12

Masja [62]

Answer:

75.4 inches

Step-by-step explanation:

circumference = 2πr = 24π ≅ 75.4 inches

It's really more of a tubcake than a cupcake.

5 0

3 years ago

The correlation between the height and weight of children aged 6 to 9 is found to be about r = 0.8. Suppose we use the height x

Andrew [12]

Answer:

$r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}$

The value of r is always between $-1 \leq r \leq 1$

And we have another measure related to the correlation coefficient called the R square and this value measures the % of variance explained between the two variables of interest, and for this case we have:

$r^2 = 0.8^2 = 0.64$

So then the best conclusion for this case would be:

c. the fraction of variation in weights explained by the least-squares regression line of weight on height is 0.64.

Step-by-step explanation:

For this case we know that the correlation between the height and weight of children aged 6 to 9 is found to be about r = 0.8. And we know that we use the height x of a child to predict the weight y of the child

We need to rememeber that the correlation is a measure of dispersion of the data and is given by this formula:

$r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}$

The value of r is always between $-1 \leq r \leq 1$

And we have another measure related to the correlation coefficient called the R square and this value measures the % of variance explained between the two variables of interest, and for this case we have:

$r^2 = 0.8^2 = 0.64$

So then the best conclusion for this case would be:

c. the fraction of variation in weights explained by the least-squares regression line of weight on height is 0.64.

3 0

3 years ago