All categories

2 years ago

Plssssssssssssssssss slove I don't know how to solve this

Mathematics

1 answer:

Reika [66]2 years ago

4 0

Answer:

I think the second blank is 5 and the third blank is 2.

Input 1: Output 5

Input 5: Output 2

You might be interested in

Write 6.74 * 10^9 in standard notation.

kaheart [24]

That would be 6,740,000,000

7 0

3 years ago

Read 2 more answers

The number of bacteria (b) in a batch of refrigerated food is given by the function b(t) = 20t2 − 70t + 300, where t is the temp

nata0808 [166]

We want to combine b(t) and t(h) to create a function that described the number of bacteria, b, as a function of time, h.
This function can be called b(t(h)), or b(h).
Consider the function b(t), or bacteria as a function of temperature.
b(t) = 20t² - 70t +300
This describes the number of bacteria, given the temperature.

t(h) described the temperature as a function of time, specifically hours after refrigeration:
t(h) = 2h + 3

Since the time h can tell us the temperature t, and the temperature t can tell us the # of bacteria b, we can create a function that tells us the number of bacteria, b, given hours following refrigeration, h.

To do this, we plug t(h) in for every t in the b(t) function:
b(t(h)) = 20 (2h+3)² - 70(2h+3) + 300
We can also call this function b(h), since we now can express b as a function of h.

Simplify the function:
b(h) = 20 (4h²+12h+9)-14h-210+300
b(h) = 80h² +100h +270

The Answer is B

7 0

3 years ago

Read 2 more answers

What is the mean of 8,8,8,7,7,8,3

aleksley [76]

8 + 8 + 8 + 7 + 7 + 8 + 3 = 49
49 / 7 = 7
The mean is 7

7 0

3 years ago

Read 2 more answers

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