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pantera1 [17]
2 years ago
15

Without actual calculating the cubes, evaluate using identities:

id="TexFormula1" title="-11^3+8^3+3^3" alt="-11^3+8^3+3^3" align="absmiddle" class="latex-formula">
Mathematics
2 answers:
djyliett [7]2 years ago
5 0

Answer:

-792

Step-by-step explanation:

wariber [46]2 years ago
5 0

\huge\textsf{Hey there!}


\mathsf{-11^3 + 8^3 + 3^3}

\mathsf{= -11\times -11\times -11 + 8\times8\times8 + 3\times3\times3}

\mathsf{= 121\times -11+ 64\times8+9\times3}

\mathsf{= -1,331 + 512 + 27}

\mathsf{= -819 + 27}

\mathsf{= -792}


\huge\textsf{Therefore, your answer should be: \boxed{\mathsf{-792}}}\huge\checkmark


\huge\textsf{Good luck on your assignment \& enjoy your day!}


~\frak{Amphitrite1040:)}

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It costs y dollars to buy x cans of tomatoes. What is the constant of proportionality?
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Step-by-step explanation:

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Graph ​ y−2=−34(x−6) ​ using the point and slope given in the equation.
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In a standard normal distribution, what z value corresponds to 17% of the data between the mean and the z value?
hjlf
You're looking for a value z such that

\mathbb P(0

Because the distribution is symmetric, the value of z in either case will be the same.

Now, because the distribution is continuous, you have that

0.17=\mathbb P(0

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0.17=\mathbb P(Z

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4 0
3 years ago
HELPP I NEED THIS ASAP
Verizon [17]

Answer:

you have it done? what do you need

Step-by-step explanation:

4 0
3 years ago
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HELP ASAP Calculate the residuals for your scatterplot in step 2d.
postnew [5]

The residuals of the linear regression equation are -1.36, 0.32, 0.01, 0.62, 0.17, -0.24, 0.89, 0.09, 0.18 and -0.7

<h3>How to determine the residuals?</h3>

The regression equation is given as:

y = 0.141x + 0.842

Next, we calculate the predicted values (y) at the corresponding x values.

So, we have:

y = 0.141 * 13.8 + 0.842 = 2.78

y = 0.141 * 18 + 0.842 = 3.38

y = 0.141 * 16.7 + 0.842 = 3.20

y = 0.141 * 18 + 0.842 = 3.38

y = 0.141 * 0.7 + 0.842 = 0.94

y = 0.141 * 21.9 + 0.842 = 3.93

y = 0.141 * 15.5 + 0.842 = 3.03

y = 0.141 * 9.2 + 0.842 = 2.14

y = 0.141 * 19.5 + 0.842 = 3.59

y = 0.141 * 16.7 + 0.842 = 3.20

The residuals are then calculated using:

Residual = Actual value - Predicted value

So, we have:

Residual = 1.42 - 2.78 = -1.36

Residual = 3.7 - 3.38 = 0.32

Residual = 3.21 - 3.20 = 0.01

Residual = 4 - 3.38 = 0.62

Residual = 1.11 - 0.94 = 0.17

Residual = 3.69 - 3.93 = -0.24

Residual = 3.92 - 3.03 = 0.89

Residual = 2.23 - 2.14 = 0.09

Residual = 3.77 - 3.59 = 0.18

Residual = 2.5 - 3.20 = -0.7

Hence, the residuals of the linear regression equation are -1.36, 0.32, 0.01, 0.62, 0.17, -0.24, 0.89, 0.09, 0.18 and -0.7

Read more about residuals at:

brainly.com/question/16180255

#SPJ1

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