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strojnjashka [21]
3 years ago
15

For every 15 seventh graders in band, there are 20 eighth graders. What is the ratio of eighth graders to seventh graders?

Mathematics
1 answer:
mamaluj [8]3 years ago
4 0

Answer:

The answer is 20:15, or 4:3 simplified

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PLS HELP HURRY NEED rigGHT ANWSER
Alex787 [66]

Answer:

(3, -4)

Step-by-step explanation:

Lines are intersecting at points (3, -4).

So, the solution of the given system of equations is (3, -4).

Hope it helps you in your learning process.

4 0
2 years ago
In Ms. Craig’s class, 75% of the students are boys. There are 18 boys in the class. What is the total number of students in Ms.
topjm [15]

Answer:

24

Step-by-step explanation:

75% of x = 18

75/100 * x = 18 (x =  total number of students)

solving

x = 24

total number of students = 24

Checking:

75/100 * 24 = 18

5 0
3 years ago
Figure abcd is a square. Find the measure of < ABE
kotegsom [21]

Answer:

45 degrees

Step-by-step explanation:

Because the shape is a square, all of the sides are equal and all of the angles are 90 degrees. From there you simply divide  90 by 2.

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3 years ago
Read 2 more answers
Expand the expression 5(p - 1) + 3(p - 0.75)
Whitepunk [10]

Answer:

=8p−7.25

Step-by-step explanation:

=(5)(p)+(5)(−1)+(3)(p)+(3)(−0.75)

=5p+−5+3p+−2.25

=5p+−5+3p+−2.25

=(5p+3p)+(−5+−2.25)

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6 0
3 years ago
The time it takes for a planet to complete its orbit around a particular star is called the? planet's sidereal year. The siderea
BartSMP [9]

Answer:

(a) See below

(b) r = 0.9879  

(c) y = -12.629 + 0.0654x

(d) See below

(e) No.

Step-by-step explanation:

(a) Plot the data

I used Excel to plot your data and got the graph in Fig 1 below.

(b) Correlation coefficient

One formula for the correlation coefficient is  

r = \dfrac{\sum{xy} - \sum{x} \sum{y}}{\sqrt{\left [n\sum{x}^{2}-\left (\sum{x}\right )^{2}\right]\left [n\sum{y}^{2} -\left (\sum{y}\right )^{2}\right]}}

The calculation is not difficult, but it is tedious.

(i) Calculate the intermediate numbers

We can display them in a table.

<u>    x   </u>    <u>      y     </u>   <u>       xy     </u>    <u>              x²    </u>   <u>       y²    </u>

   36       0.22              7.92               1296           0.05

   67        0.62            42.21              4489           0.40

   93         1.00            93.00           20164           3.46

 433        11.8          5699.4          233289        139.24

 887      29.3         25989.1          786769       858.49

1785      82.0        146370          3186225      6724

2797     163.0         455911         7823209    26569

<u>3675 </u>  <u> 248.0  </u>    <u>   911400      </u>  <u>13505625</u>   <u> 61504        </u>

9965   537.81     1545776.75  25569715   95799.63

(ii) Calculate the correlation coefficient

r = \dfrac{\sum{xy} - \sum{x} \sum{y}}{\sqrt{\left [n\sum{x}^{2}-\left (\sum{x}\right )^{2}\right]\left [n\sum{y}^{2} -\left (\sum{y}\right )^{2}\right]}}\\\\= \dfrac{9\times 1545776.75 - 9965\times 537.81}{\sqrt{[9\times 25569715 -9965^{2}][9\times 95799.63 - 537.81^{2}]}} \approx \mathbf{0.9879}

(c) Regression line

The equation for the regression line is

y = a + bx where

a = \dfrac{\sum y \sum x^{2} - \sum x \sum xy}{n\sum x^{2}- \left (\sum x\right )^{2}}\\\\= \dfrac{537.81\times 25569715 - 9965 \times 1545776.75}{9\times 25569715 - 9965^{2}} \approx \mathbf{-12.629}\\\\b = \dfrac{n \sum xy  - \sum x \sum y}{n\sum x^{2}- \left (\sum x\right )^{2}} -  \dfrac{9\times 1545776.75  - 9965 \times 537.81}{9\times 25569715 - 9965^{2}} \approx\mathbf{0.0654}\\\\\\\text{The equation for the regression line is $\large \boxed{\mathbf{y = -12.629 + 0.0654x}}$}

(d) Residuals

Insert the values of x into the regression equation to get the estimated values of y.

Then take the difference between the actual and estimated values to get the residuals.

<u>    x    </u>   <u>      y     </u>   <u>Estimated</u>   <u>Residual </u>

    36        0.22        -10                 10

    67        0.62          -8                  9

    93        1.00           -7                  8

   142        1.86           -3                  5

  433       11.8             19               -  7

  887     29.3             45               -16  

 1785     82.0            104              -22

2797    163.0            170               -  7

3675   248.0            228               20

(e) Suitability of regression line

A linear model would have the residuals scattered randomly above and below a horizontal line.

Instead, they appear to lie along a parabola (Fig. 2).

This suggests that linear regression is not a good model for the data.

4 0
3 years ago
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