Answer:
A. the difference of two squares
Step-by-step explanation:
Please use the symbol " ^ " to indicate exponentiation.
Then we have x^2 - 11^2, which is the difference of two squares:
x^2 is a square, the square of x; and 11^2 is a square, the square of 11.
A. the difference of two squares
Note that a "difference of squares" is easily factored:
a² - b² = (a - b)(a + b)
and so your x² - 11² factors as follows: (x - 11)(x + 11)
Answer: (1) mean = 9.46; standard deviation = 3.74
Step-by-step explanation:
Answer:
13r²(2rs + 4r³ - 3s⁴)
Step-by-step explanation:
In equation 26r³s + 52r⁵ - 39r²s⁴;
The GCF of 26, 52, and 39 = 13
The GCF of r³, r⁵ and r² = r²
The GCF of s, (no "s"), and s⁴ = no "s" (Since one of the number doesn't have "s")
Now we can factor out 13r² from all three expressions;
26r³s + 52r⁵ - 39r²s⁴
=> <em>13r²(2rs) + 13r²(4r³) - 13r²(3s⁴)</em>
To factor it all together;
<u>13r²(2rs + 4r³ - 3s⁴)</u>
Hope this helps!
We assume data and prediction as question is incomplete
Answer and Step-by-step explanation:
Least squares regression line equations are used to model the relationship that exists between two variables, dependent and independent variables. The equation has the form y=a+bx where y is the dependent variable and x is independent variable, a is a constant and is the y intercept and b is the slope of the line. This relationship is then used to predict future outcomes.
Given that data for 2004-2005 for the basketball players are :
James- 20 points
John- 30 points
Chris- 50 points
Dave-15 points
Donaldson- 32 points
Richard -40 points
We predict the scores/points for James (for example) for the following year using the equation of the regression line y=0.79x+1544
We substitute his points x=20 I'm the equation:
Y=0.79*20+1544
=1599.8
The predicted value is 1599.8
Answer:
b. The greater the number of independent variables measured, the more difficult it is to interpret higher-order interactions.
