Answer:
x^2 – 3xy + 2y^2
Step-by-step explanation:
Factor the following:
x^2 - 3 x y + 2 y^2
Hint: | Factor the quadratic x^2 - 3 x y + 2 y^2.
The factors of 2 that sum to -3 are -1 and -2. So, x^2 - 3 x y + 2 y^2 = (x - 1 y) (x - 2 y):
Answer: (x - y) (x - 2 y)
To find how much Henry can expect to receive from Social Security on a monthly basis, we first need to find how much he cant expect to receive from social security per year.
We know form our problem that Henry averaged an annual salary of $45,620, so to find how much can Henry expect to receive from Social Security per year, we just need to find the 42% of $45,620.
To find the 42% of $45,620, we are going to convert 42% to a decimal by dividing it by 100%, and then we are going to multiply the resulting decimal by $45,620:
Social security annual payment = (0.42)($45,620) = $19,160.40
Since there are 12 month in a year, we just need to divided the social security annual payment by 12 to find how much he can expect to receive each month.
Social security monthly payment = 
= $1.596.70
We can conclude that Henry can expect to receive $1.596.70 monthly from Social Security.
Answer:
17. Scale Factor is 3:1
18. Scale Factor is 1:3
Step-by-step explanation:
Scale Factor: In two similar shapes, the ratio of their corresponding sides is called scale factor.
17. Give the scale factor of Figure A to Figure B
Figure A has sides:
Hypotenuse = 15
Perpendicular = 12
Base = 9
Figure B has sides:
Hypotenuse = 5
Perpendicular = 4
Base = 3
So, if we divide all sides of figure A by 3 we get Figure B
So, Figure A : Figure B
3:1
18. Give the scale factor of Figure B to Figure A
Figure B has sides:
Hypotenuse = 5
Perpendicular = 4
Base = 3
Figure A has sides:
Hypotenuse = 15
Perpendicular = 12
Base = 9
If we multiply 3 with the sides of Figure B we can get the sides of Figure A.
So scale factor is 3.
So, Figure B : Figure A
1:3
<span>4ab + 4a − 3b − 3
=4a(b + 1) - 3(b+1)
= (b+1)(4a - 3)
(b+1) and (4a - 3) are factors
answer
</span><span>4a − 3</span>
Answer: Quadratic Regression model best fits the data set.
Step-by-step explanation:
