Answer:
<h3>A. The slope describes the amount of change in Y for a one-unit increase in X
.</h3><h3>B. The regression equation is the line that best fits a set of data as determined by having the least squared error.</h3>
Step-by-step explanation:
In statistics, linear regression is a analysis we do to describe the relationship between two variables. With this study, we pretend to know if there's a positive or negative correlation between those variables, if that correlation is strong or weak.
In a linear regression analysis, we modeled the data set using a regression equation, which is basically the line that best fits to the data set, this line is like the average where the majority of data falls. That means choice A is right.
When we use linear equations, we need to know its characteristics, and the most important one is the slope, which is the ratio between the dependent variable and the independent variable. Basically, the slope states the unit rate between Y and X, in other words, it states the amount of Y per unit of X. That means choice B is correct.
Therefore, the correct answers are A and B.
It rose 29 degrees in that time frame
Answer: perimeter = 26.5
Area = 106
Step-by-step explanation:
The given polygon is an octagon. The apotherm which is the perpendicular line from the midpoint of the octagon is 8,
The formula for determining the area of a polygon is expressed as
Area = a² × n × tan 180/n
Where n represents the number of sides of the polygon.
n = 8
Therefore,
Area = 8² × 4 × tan(180/8)
Area = 256 × tan 22.5
Area = 106
The formula for determining the perimeter of a regular polygon is
P = 2 × area/apotherm
Perimeter = 2 × 106/8
Perimeter = 26.5
Consider the expression
To factorize the expression in the denominator we use difference of squares:
To factorize
we use the following method:
where a, b are 2 numbers such that a+b= -1, the coefficient of x,
and a*b= -6, the constant.
such 2 numbers can be easily checked to be -3 and 2
(-3*2=6, -3+2=-1)
So
for x>2
thus
for x>2,
Answer:
for x>2
, (but the expression is never 0)
