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3 years ago

Is 890,353 mL bigger than 89 L 353mL

Mathematics

1 answer:

Gemiola [76]3 years ago

5 0

Yes, because 89L = 89,000ml. So 89L 353mL = 89,353mL. Thus, 890,353mL is greater.

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What is 16,473 to the nearest hundred

Verizon [17]

The number "4" is in the hundreds place. To the right is 7, and since it's greater than five it rounds up. So 16,473 to the nearest hundred is 16,500. Hope this helped!

4 0

3 years ago

3 6/4 in simplest form

ivanzaharov [21]

Answer:

4 1/2.

hope this helps!

6 0

2 years ago

To transmit information on the internet, large files are broken into packets of smaller sizes. Each packet has 1,500 bytes of in

erastova [34]

Answer: $x= 187.5p$

Step-by-step explanation:

Given : An equation relating packets to bytes of information : $b = 1,500p$ where<em> p</em> represents the number of packets and <em>b</em> represents the number of bytes of information.

1 byte = 8 bits

Let x= Number of bits such that 1 b = 8x

Put b = 8x in given equation , we get

$8x=1500p$

Divide both sides by 8 , we get

$x= 187.5p$

Hence, the equation represent the relationship between the number of packets and the number of bits : $x= 187.5p$

3 0

3 years ago

Read 2 more answers

. The estimate regression equation for a model involving two independent variables and 10 observations follows.y = 29.1270 + 0.5

beks73 [17]

Answer:

b) y = 289.815 when $x_1 = 180 \text{ and } x_2 = 310$

Step-by-step explanation:

We are given the following information in the question:

$y = 29.1270 + 0.5906x_1 + 0.4980x_2$

where y is the dependent variable,

$x_1, x_2$ are the independent variable.

The multiple regression equation is of the form:

$y = b_0 + b_1x_1 + b_2x_2$

where,

$b_0$ : is the intercept of the equation and is the value of dependent variable when all the independent variable are zero.

$b_1$ : It is the slope coefficient of the independent variable $x_1$ .

$b_2$ : It is the slope coefficient of the independent variable $x_2$ .

The regression coefficient in multiple regression is the slope of the linear relationship between the dependent and the part of a predictor variable that is independent of all other predictor variables.

Comparing the equations, we get:

$b_1 = 0.5906\\b_2 = 0.4980$