See explanation below.
Explanation:
The 'difference between roots and factors of an equation' is not a straightforward question. Let's define both to establish the link between the two..
Assume we have some function of a single variable
x
;
we'll call this
f
(
x
)
Then we can form an equation:
f
(
x
)
=
0
Then the "roots" of this equation are all the values of
x
that satisfy that equation. Remember that these values may be real and/or imaginary.
Now, up to this point we have not assumed anything about
f
x
)
. To consider factors, we now need to assume that
f
(
x
)
=
g
(
x
)
⋅
h
(
x
)
.
That is that
f
(
x
)
factorises into some functions
g
(
x
)
×
h
(
x
)
If we recall our equation:
f
(
x
)
=
0
Then we can now say that either
g
(
x
)
=
0
or
h
(
x
)
=
0
.. and thus show the link between the roots and factors of an equation.
[NB: A simple example of these general principles would be where
f
(
x
)
is a quadratic function that factorises into two linear factors.
Answer:
A. divide only on the right side of the equation by 2.
Step-by-step explanation:
I took the test yesterday and I got it hope this helps.
Answer:
m∠R = 60° and AB ≅ MQ
m∠Q = 56° and CB ≅ RQ
Step-by-step explanation:
Given data :
Prove ΔABC ≅ ΔMQR using SAS
The missing information to prove ΔABC ≅ ΔMQR using SAS
Answer: 2nd graph and the point is (6,7)
Step-by-step explanation:
Answer:
At least one of the population means is different from the others.
Step-by-step explanation:
ANOVA is a short term or an acronym for analysis of variance which was developed by the notable statistician Ronald Fisher. The analysis of variance (ANOVA) is typically a collection of statistical models with their respective estimation procedures used for the analysis of the difference between the group of means found in a sample. Simply stated, ANOVA helps to ensure we have a balanced data by splitting the observed variability of a data set into random and systematic factors.
In Statistics, the random factors doesn't have any significant impact on the data set but the systematic factors does have an influence.
Basically, the analysis of variance (ANOVA) procedure is typically used as a statistical tool to determine whether or not the mean of two or more populations are equal through the use of null hypothesis or a F-test.
Hence, the null hypothesis for an ANOVA is that all treatments or samples come from populations with the same mean. The alternative hypothesis is best stated as at least one of the population means is different from the others.
