Answer:
x² + 10x - 12
Step-by-step explanation:
Each term in the second factor is multiplied by each term in the first factor, that is
x(2x - 2) + 6(2x - 2) ← distribute both parenthesis
= 2x² - 2x + 12x - 12 ← collect like terms
= 2x² + 10x - 12
Find angle a2 which is 40 degrees because it is parallel to angle c.
Find the total of d1 and d2.
total of d1 and d2: 180 - 40 - 40 = 100 degrees
Find d1 and d2 separately.
100 divided by 2 = 50 degrees
Use d1 to find b1 to find total of a1 and a2.
b1 is parallel to d1 so b1 = 50 degrees
a1 and a2 = 180 - 50 - 50 = 80 degrees
a1 = 80 divided by 2 = 40
Since a1 and c1 are parallel due to alternate angles, c1 is 40 degrees
Find b2 now which requires you to do total - minus all angles in the triangle with angle b2.
180 - 40 - 50 - 40 = 50 degrees (angle b2)
AOB has b1 and a1.
40 + 50 = 90 degrees (a1 + b1 = AOB)
The answer is 90 degrees
Answer:
- parallel: 60 ft
- perpendicular: 30 ft
- area: 1800 ft^2
Step-by-step explanation:
Let x represent the length of fence parallel to the house. Then the length perpendicular is ...
y = (120 -x)/2
The area of the yard is the product of these dimensions, so is ...
A = xy = x(120-x)/2
This is the equation of a parabola that opens downward and has zeros at x=0 and x=120. The maximum (vertex) is on the line of symmetry, halfway between these zeros, at x=60.
The fence parallel to the house is 60 feet.
The fence perpendicular to the house is (120-60)/2 = 30 feet.
The area of the yard is (60 ft)(30 ft) = 1800 ft^2.
<em>In base four, each digit in a number represents the number of copies of that power of four. That is, the first digit tells you how many ones you have; the second tells you how many fours you have; the third tells you how many sixteens (that is, how many four-times-fours) you have; the fourth tells you how many sixty-fours (that is, how many four-times-four-times-fours) you have; and so on.
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Answer:
B. the variability around the regression line.
Step-by-step explanation:
The standard errors represents the distance (how sparse) the observed values fall from the regression line.
Standard errors for regression are measures of the spread of variables around the average (regression line)
The standard error is dependent on the standard deviation of the observations and the reliability of the test.
When the test is perfectly reliable, the standard error is zero and when unreliable, it is equal to the standard deviation of the observations.
