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MArishka [77]
3 years ago
6

A right rectangular prism has these dimensions:

Mathematics
2 answers:
ki77a [65]3 years ago
5 0
Volume of rectangular prism = 1 1/3 x 5/6 x 2/3 = 4/3 x 5/6 x 2/3 = 20/27

Volume of cube = 1/6 x 1/6 x 1/6 = 1/216

Number of cubes that will pack the rectangular prism = 20/27 / 1/216 = 160
MakcuM [25]3 years ago
4 0

Answer:

The number of cubes required is 160.

Step-by-step explanation:

The dimensions of the right rectangular prisms are

l=1\frac{1}{3} \;units

w=\frac{5}{6} \;units

h=\frac{2}{3} \;units


The volume of the right rectangular prism is

V=l\times b\times h.

We substitute the dimensions to get,

V=1\frac{1}{3}\times \frac{5}{6}\times \frac{2}{3}.


We convert the first mixed number to improper fraction,

V=\frac{4}{3}\times \frac{5}{6}\times \frac{2}{3}.


We multiply out to obtain,

V=\frac{40}{54}


V=\frac{20}{27} cubic units.



We need to determine the volume of the cube of side length,

l=\frac{1}{6} units.


The volume of a cube is given by,

V=l^3

This implies that,

V=(\frac{1}{6})^3


This gives us,

V=\frac{1}{216} cubic units.


We now divide the volume of the right rectangular prism by the volume of the cube to determine the number of cubes required.


Number\:of\:cubes=\frac{\frac{20}{27} }{\frac{1}{216} }


We simplify to get,

Number\:of\:cubes=\frac{20}{27} \div \frac{1}{216}


This implies that,

Number\:of\:cubes=\frac{20}{27} \times \frac{216}{1}


Number\:of\:cubes=20\times8


Number\:of\:cubes=160





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Answer:

a)Slope: \hat \beta_1 =\frac{7625.9}{1248.9}=6.106

Intercept: \hat \beta_o = 157.955 -6.106 (69.686)=-267.548

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Step-by-step explanation:

Data given and definitions

The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.  

The sum of squares "is the sum of the square of variation, where variation is defined as the spread between each individual value and the grand mean"    

When we conduct a multiple regression we want to know about the relationship between several independent or predictor variables and a dependent or criterion variable.  

n=110, \sum x_i y_i = \sum (X-\bar X)(Y-\bar Y) =7625.9,\sum x^2_i=\sum (x-\bar x)^2 =1248.9 , sum y^2_i=\sum(y-\bar y)^2 =94228.8  

\sum Y_i =17375 , \sum X_i = 7665.5

Part a

The slope is given by this formula:

\hat \beta_1 =\frac{\sum (x-\bar x) (y-\bar y)}{\sum (x-\bar x )^2}

If we replace we got:

\hat \beta_1 =\frac{7625.9}{1248.9}=6.106

We can find the intercept with the following formula

\hat \beta_o = \bar y -\hat \beta_1 \bar x

We can find the average for x and y like this:

\bar X=7665.5/110 =69.686, \bar y= 17375/110=157.955

And replacing we got:

\hat \beta_o = 157.955 -6.106 (69.686)=-267.548

Part b

In order to calculate the correlation coefficient we can use this formula:  

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

For our case we have this:  

n=110, \sum x_i y_i = \sum (X-\bar X)(Y-\bar Y) =7625.9,\sum x^2_i=\sum (x-\bar x)^2 =1248.9 , sum y^2_i=\sum(y-\bar y)^2 =94228.8  

So we can find the correlation coefficient replacing like this:

r=\frac{7625.9}{\sqrt{[1248.9][94228.8]}}=0.657  

And the determination coeffecient is r^2 = 0.657^2 =0.432  

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