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faust18 [17]
2 years ago
7

How many seconds are in 7.5 minutes?

Mathematics
2 answers:
Ilya [14]2 years ago
8 0
450, make sure you googl.e your question before asking lol
dimulka [17.4K]2 years ago
5 0

Answer:

450 seconds

Step-by-step explanation:

7.5 x 60= 450

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3/8 * 4 ____*____=_____=______
kirill [66]

Answer:

your answer is 1.5 150% or 3/2 there all the same anyway

Step-by-step explanation:

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3 years ago
What’s the slope?................
Orlov [11]
M = y2-y1/x2-x1
m = 7- (-2)/ -6-(-7)
m = 9/1

slope = 9
7 0
3 years ago
Perform the operation and reduce the answer fully. 3/4 + 1/10
Assoli18 [71]

Answer:

17/20

Step-by-step explanation:

8 0
3 years ago
Read 2 more answers
A waterfall has a height of 900 feet. A pebble is thrown upward from the top of the falls with an initial velocity of 24 feet pe
aliya0001 [1]

Answer:

The pebble will hit the ground 8.29 second after it is thrown.

Step-by-step explanation:

Given that,

The height of the pebble after t seconds is given by the equation

h= -16t²+24 t+900.

When the pebble hit the ground, the height of the pebble will be zero.

So putting h=0 in the given equation.

0 = -16t²+24 t+900

⇒-4(4t²-6t-225)=0

⇒4t²-6t-225=0

Applying quadratic formula t=\frac{-b\pm\sqrt{b^2-4ac}}{2a}, here a=4, b= -6 and c= -225.

\therefore t=\frac{-(-6)\pm\sqrt{(-6)^2-4.4.(-225)}}{2.4}

⇒ t = 8.29, -6.79

Since time can not negative.

∴t=8.29 seconds.

The pebble will hit the ground 8.29 second after it is thrown.

7 0
3 years ago
Which statements about residuals are true for the least-squares regression line? I. A random scattering of residuals indicates t
Daniel [21]

Answer:

II. The sum of the residuals is always 0.

Step-by-step explanation:

A least squares regression line is a standard technique in regression analysis used to make the vertical distance obtained from the data points running to the regression line to become very minimal or as small as possible.

For any least-squares regression line, the sum of the residuals is always zero.

Basically, residuals are used to measure or determine whether or not the line of regression is a good fit or match for the data by subtracting the difference between them i.e the predicted y value and the actual y value, for the x value respectively.

Hence, the statement about residuals which is true for the least-squares regression line is that the sum of the residuals is always zero (0).

7 0
3 years ago
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