Answer:
a) 3⁵5³.
b) 1
c) 23³
d) 41·43·53
e) 1
f) 1111
Step-by-step explanation:
The greatest common divisor of two integers is the product of their common powers of primes with greatest exponent.
For example, to find gcd of 2⁵3⁴5⁸ and 3⁶5²7⁹ we first identify the common powers of primes, these are powers of 3 and powers of 5. The greatest power of 3 that divides both integers is 3⁴ and the greatest power if 5 that divides both integers is 5², then the gcd is 3⁴5².
a) The greatest common prime powers of 3⁷5³7³ and 2²3⁵5⁹ are 3⁵ and 5³ so their gcd is 3⁵5³.
b) 11·13·17 and 2⁹3⁷5⁵7³ have no common prime powers so their gcd is 1
c) The only greatest common power of 23³ and 23⁷ is 23³, so 23³ is the gcd.
d) The numbers 41·43·53 and 41·43·53 are equal. They both divide themselves (and the greatest divisor of a positive integer is itself) then the gcd is 41·43·53
e) 3³5⁷ and 2²7² have no common prime divisors, so their gcd is 1.
f) 0 is divisible by any integer, in particular, 1111 divides 0 (1111·0=0). Then 1111 is the gcd
Answer:
870
Step-by-step explanation:
$435 + $395 = $830
$830 + $10 = $840
$2 x 15 = $30
$840 + $30 = $870
Answer:
A, B, and C
Step-by-step explanation:
a). -6 ÷ 2 x 5 1/3
= -3 x 5 1/3
= -16
b). 24 ÷ (-3) + 1/2
= -8 + 1/2
= -7.5
c). 36 ÷ 4 x (-2)
= 9 x -2
= -18
d). -54 ÷ (-9) x (-3 2/3)
6 x -3 2/3
= -22
ANSWER
D. 18 m^2
EXPLANATION
The area of a kite is half the product of the diagonals.
The diagonals of the kite are
3+3=6m
and
2+4=6m
The area of the kite
The correct answer is D.
Answer:
The correct option is;
C. The pattern is random, indicating a good fit for a linear model
Step-by-step explanation:
A graph that has the residuals (the difference between the value observed and the value expected (regression analysis) on the vertical axis and the variable that is not affected by the other variables (independent variable) on the x or horizontal axis is known as a residual plot
A linear regression model is suited in a situation where the points are dispersed randomly on both sides of the horizontal axis
Therefore, given that the first point is below the horizontal axis and the next point is above the horizontal axis, while the third and the fourth points are below the horizontal axis, the fifth, sixth, and seventh points are above the horizontal axis and the eighth point is below the horizontal axis, the points are random around the horizontal axis, indicating the suitability of a linear regression model.
