<span>This question is a simple one. To answer this question, you need to understand the description in the question and determine to multiply or divide the number.
The first problem would be:
50.75 x 0.18= 9.135
</span>If you need to estimate, 50.75 is near 50; 0.18 is near 0.2 or 1/5 so it would be: 50/0.2= 10<span>
The second problem would be:
196 / 0.499: 392.785
If you need to estimate, 0.499 is near 0.5 then 196/0.5 would be 392</span>
Area = perimeter + 132.
Let each side of the city be x miles long, then:-
x^2 = 4x + 132
x^2 - 4x - 132 = 0
x = [-(-4) +/- sqrt((-4)^2 - 4 * 1 *-132)] / 2
x = 13.66, -9.66 We ignore the negative
So the city has dimension of 13.66 * 13.66
13.7 * 13.7 to nearest 10th
Answer: The value of x in trapezoid ABCD is 15
Step-by-step explanation: The trapezoid as described in the question has two bases which are AB and DC and these are parallel. Also it has sides AD and BC described as congruent (that is, equal in length or measurement). These descriptions makes trapezoid ABCD an isosceles trapezoid.
One of the properties of an isosceles trapezoid is that the angles on either side of the two bases are equal. Since line AD is equal to line BC, then angle D is equal to angle C. It also implies that angle A is equal to angle B.
With that bit of information we can conclude that the angles in the trapezoid are identified as 3x, 3x, 9x and 9x.
Also the sum of angles in a quadrilateral equals 360. We can now express this as follows;
3x + 3x + 9x + 9x = 360
24x = 360
Divide both sides of the equation by 24
x = 15
Therefore, in trapezoid ABCD
x = 15
Answer:
-95.78
Step-by-step explanation:
As the researcher decided to make the number of parties attended per week the explanatory variable, this would be variable x in the regression line, and of course, the variable y would be the number of text messages sent per day.
After constructing the linear regression equation, the researcher found that an approximate value 
for the actual value of y could be represented by the line
Since this is an approximate value, it is not expected that it coincides with the actual value of y. We define then the residual for each value of x as the difference between the actual value of y and the approximation for the given x.
For the value x = 2 (the student attended 2 parties that week) the actual value of y is 20 (the student sent 20 text messages per day that week).
The approximate value of y would be according to the regression line
Hence, the residual value for x=2 would be
