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4 years ago

Convert to length 2.1 cm to mm

Mathematics

1 answer:

Anastasy [175]4 years ago

7 0

Here we need to convert 2. 1 cm to mm .

And for that we will use the following conversion .

1cm = 10 mm

So for 2.1 cm, we will multiply 2.1 and 10 to convert it to mm. Therefore 2.1 cm = 2.1* 10mm = 21mm

And that's the required answer .

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Solve for x: 3(x + 1) = −2(x − 1) + 6

In-s [12.5K]

Answer:

Step-by-step explanation:

$3(x + 1) = −2(x − 1) + 6$

Expand the terms in the bracket and simplify

That's

$3x + 3 = - 2x + 2 + 6 \\ 3x + 3 = - 2x + 8$

<u>Add 2x to both sides of the equation</u>

$3x + 2x + 3 = 2x - 2x + 8 \\ 5x + 3 = 8$

<u>Subtract 3 from both sides of the equation</u>

$5x + 3 - 3 = 8 - 3 \\ 5x = 5$

<u>Divide both sides by 5</u>

$\frac{5x}{5} = \frac{5}{5} \\$

We have the final answer as

Hope this helps you

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3 years ago

Does anyone know the answer to this problem?

Mariana [72]

The regression equation is y = 16.585x - 639.769 and the linear correlation coefficient indicates a strong linear relationship

<h3>How to determine the regression equation?</h3>

To do this, we make use of a statistical calculator;

Using the statistical calculator, we have the following summary:

Sum of X = 554
Sum of Y = 4070
Mean X = 69.25
Mean Y = 508.75
Sum of squares (SSX) = 725.5
Sum of products (SP) = 12032.5
Correlation coefficient, r = 0.9768

The regression equation is

ŷ = bx + a

Where:

b = SP/SSX = 12032.5/725.5 = 16.58511

a = MY - bMX = 508.75 - (16.59*69.25) = -639.76912

Hence, the regression equation is y = 16.585x - 639.769

<h3>The number of cans sold at 90 degrees F</h3>

This means that"

x = 90

So, we have:

y = 16.585 * 90 - 639.769

Evaluate

y = 852.881

Approximate

y = 853

Hence, 853 cans were sold at 90 degrees F

<h3>The linear correlation coefficient</h3>

In (a), we have:

r = 0.9768

This means that the linear correlation coefficient is 0.9768

<h3>The conclusion</h3>

We have:

r = 0.9768

The above value is closer to 1 because

|r| > 0.9

i.e.

0.9768 > 0.9

Hence, the linear correlation coefficient indicates a strong linear relationship