Answer:
25 bc I mena I did count the scares but then I took the ones that weren’t fully squares and counted them as a quarter and that’s what I got not sure don’t trust meh bc I just guess this
Step-by-step explanation:
Answer:
The given expression 
on multiplying is 
Step-by-step explanation:
Consider the given two expressions 
and 
We have multiply both expressions,
To multiply two terms first multiply constant numbers that is 6 × 2 = 12
For x , y and z apply property of exponent,
Then power of x together will be,
Similarly for y powers,
Since first term do not have any expression for z so it will remain same.
Thus, the given expression on multiplying become,
is
Answer:
Slope: 3
Y-Intercept: -10
Step-by-step explanation:
This equation is in slope-intercept form, it is written as y = mx + b where m is the slope and b is the y-intercept. This means 3 is the slope and -10 is the y-intercept.
9514 1404 393
Answer:
(c) g(x) = 1/3·f(x)
Step-by-step explanation:
You can pick a point, such as the vertex at (2, -3) on f(x) and see which of the transformations gives you a point on the graph of g(x).
You will find that g(x) represents a vertical scaling by a factor of 1/3, so ...
g(x) = 1/3·f(x)
__
The point (2, -3) on f(x) corresponds to the point (2, -1) on g(x).
Answer:
d) Squared differences between actual and predicted Y values.
Step-by-step explanation:
Regression is called "least squares" regression line. The line takes the form = a + b*X where a and b are both constants. Value of Y and X is specific value of independent variable.Such formula could be used to generate values of given value X.
For example,
suppose a = 10 and b = 7. If X is 10, then predicted value for Y of 45 (from 10 + 5*7). It turns out that with any two variables X and Y. In other words, there exists one formula that will produce the best, or most accurate predictions for Y given X. Any other equation would not fit as well and would predict Y with more error. That equation is called the least squares regression equation.
It minimize the squared difference between actual and predicted value.
