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Zina [86]
3 years ago
8

What place was the total sales numbers most likely rounded

Mathematics
1 answer:
Reil [10]3 years ago
5 0
Iit would be the same i belivee since there a zero's
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PLEASE HELP PLEASE URGENT!!
vovangra [49]

Answer:

B) 1,260

Step-by-step explanation:

7!/2!2! = 1,260

5 0
2 years ago
Oliver reads 28 1/2 pages of a book in 1 1/6 hours. Express his reading speed in pages per hour
Inessa05 [86]

Hello there

in order to find the answer you need to multiply 28.5 by 1 1/6

you get 171/7

the answer is 24.4 pages

thank you

hope this helps

Best Regards Queen Z

3 0
3 years ago
Read 2 more answers
A quadrilateral has the following vertices on a coordinate plane:
dolphi86 [110]
<h3>Answer:  4</h3>

================================================

Explanation:

We'll only focus on points J and K.

J = (3, -6)

K = (-1, -6)

Because these two points have the same y coordinate (which is -6), this means we can ignore those values and subtract the x coordinates

3 minus (-1) = 3 - (-1) = 3 + 1 = 4

Or we could plot points J and K on the same xy grid system, and count out the number of spaces between J and K. You should count out 4 spaces.

This means segment JK is 4 units long.

8 0
3 years ago
Read 2 more answers
Could you please find what x=<br><br><br><br>thank you
pickupchik [31]
4x-1=9x-2
Add 1 to both sides
4x= 9x-1
Subtract 9x from both sides
-5x=-1
Divide both sides by -5
X=-1/-5
6 0
3 years ago
A set of bivariate data was used to create a least squares regression line. Which of the following is minimized by the line
77julia77 [94]

Answer:

B) The sum of the squared residuals

Step-by-step explanation:

Least Square Regression Line is drawn through a bivariate data(Data in two variables) plotted on a graph to explain the relation between the explanatory variable(x) and the response variable(y).

Not all the points will lie on the Least Square Regression Line in all cases. Some points will be above line and some points will be below the line. The vertical distance between the points and the line is known as residual. Since, some points are above the line and some are below, the sum of residuals is always zero for a Least Square Regression Line.

Since, we want to minimize the overall error(residual) so that our line is as close to the points as possible, considering the sum of residuals wont be helpful as it will always be zero. So we square the residuals first and them sum them. This always gives a positive value. The Least Square Regression Line minimizes this sum of residuals and the result is a line of Best Fit for the bivariate data.

Therefore, option B gives the correct answer.

5 0
3 years ago
Read 2 more answers
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