Answer: The Nth power xN of a number x was originally defined as x multiplied by itself, until there is a total of N identical factors. By means of various generalizations, the definition can be extended for any value of N that is any real number.
(2) The logarithm (to base 10) of any number x is defined as the power N such that
x = 10N
(3) Properties of logarithms:
(a) The logarithm of a product P.Q is the sum of the logarithms of the factors
log (PQ) = log P + log Q
(b) The logarithm of a quotient P / Q is the difference of the logarithms of the factors
log (P / Q) = log P – log Q
(c) The logarithm of a number P raised to power Q is Q.logP
log[PQ] = Q.logP
Step-by-step explanation:
For this case we must resolve the following inequality:

Applying distributive property on the left side of inequality we have:

Subtracting 5 from both sides of the inequality:

Subtracting 4x from both sides of the inequality:

Thus, the result is 
Answer:

Need more information. Can’t answer d=2 the answer would have to be “d”
The distance between the trains is changing at the rate of (70 -60) = 10 mph. They will be in the same place (45 mi)/(10 mi/h) = 4.5 hours after they leave their respective cities. They will be 10 miles apart both 1 hour before that time and one hour after that time.
The trains will be 10 miles apart 3.5 hours after leaving.
They will be 10 miles apart the second time 5.5 hours after leaving.
Answer:
Linearly Dependent for not all scalars are null.
Step-by-step explanation:
Hi there!
1)When we have vectors like
we call them linearly dependent if we have scalars
as scalar coefficients of those vectors, and not all are null and their sum is equal to zero.
When all scalar coefficients are equal to zero, we can call them linearly independent
2) Now let's examine the Matrix given:

So each column of this Matrix is a vector. So we can write them as:
Or
Now let's rewrite it as a system of equations:

2.1) Since we want to try whether they are linearly independent, or dependent we'll rewrite as a Linear system so that we can find their scalar coefficients, whether all or not all are null.
Using the Gaussian Elimination Method, augmenting the matrix, then proceeding the calculations, we can see that not all scalars are equal to zero. Then it is Linearly Dependent.


