24 written as a percentage
1 answer:
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Answer:
See proof below.
Step-by-step explanation:
True
For this case we need to use the following theorem "If are eigenvectors of an nxn matrix A and the associated eigenvalues
are distinct, then
are linearly independent". Now we can proof the statement like this:
Proof
Let A a nxn matrix and we can assume that A has n distinct real eingenvalues let's say
From definition of eigenvector for each one needs to have associated an eigenvector
for
And using the theorem from before , the n eigenvectors are linearly independent since the
are distinct so then we ensure that A is diagonalizable.
d = log₂p represents a logarithmic function, The expression log₂(10) tells us when the population reaches 10,000 and the equation 7 = log₂(128) tells us that the population reaches 128,000 in 7 days.
<h3>What is an exponential function?</h3>An exponential function is in the form:
y = abˣ
Where a is the initial value of y and b is the multiplication factor.
Let p represent the bacteria population in thousand after d days.
Since each day, the bacteria population grows by a factor of 2, hence:
Hence, d = log₂p represents a logarithmic function
The expression log₂(10) tells us when the population reaches 10,000 and the equation 7 = log₂(128) tells us that the population reaches 128,000 in 7 days.
Find out more on exponential function at: brainly.com/question/12940982
Answer:
Answer is 27, 32, 45
Step-by-step explanation:
In the right angle triangle, hypontenous is the longest side.
∴ In each option square of longest side has to be equal to the sum of square of other two side.
We know, ( as per pythogorean theorem)
If we check the last option; 27, 32, 45
In the given set of integer, we have longest side as 45
∴ conisdering 45 as hyptenous
Subtituting the value in the formula
⇒
⇒
⇒
∴
Hence, the set of given integer is not a pythagorean triple and are not side length of right angle.
Answer:
μ=23.710740....
σ=3.592592....
Step-by-step explanation:
At first, we are told that it´s a normal distribution and the following:
P(x≤15.34)=0.1
P(x≤16.31)=0.2
We start looking for those values (0.1 and 0.2) and the probabilities that gives us those values in a normal standard distribution. There´s some tools that´ll help us out but i´ll be using a chart with the values of some probabilities in a normal standard distribution (That it´s attached).
The probability of P(z≤ a.b) where "a" is the whole part of a number and "b" the decimal part is in the coordinates (a,b). For example for our problem:
P(z≤-2.33)=0.099≈0.1
P(z≤-2.06)=0.197≈0.2
To find a probability of a normal (not standard) distribution we use a method called Normalize that proceeds:
P(x≤a)=P( (x-μ)/σ ≤ (a-μ)/σ )
Where μ is the Mean of the data and σ is the Standard deviation. P( (x-μ)/σ ≤ (a-μ)/σ ) is now part of a normal standard distribution and we are able to look for it in the chart.
We will use it to find the Mean and the STD for our distribution.
P(x≤15.34)=P(z≤-2.33)≈0.1
P(x≤16.31)=P(z≤-2.06)≈0.2
so we have the equations:
(15.34 - μ)/σ= -2.33 and (16.31 - μ)/σ= -2.06
And we solve them for μ and σ:
(15.34 - μ)/-2.33= σ and (16.31 - μ)/-2.06= σ
(15.34 - μ)/-2.33 = (16.31 - μ)/-2.06
2.06*(15.34 - μ) = 2.33(16.31 - μ)
2.33μ - 2.06μ = 2.33(16.31) - 2.06(15.34)
μ= 6.4019 / 0.27
μ= 23.710740...
With this value we find σ in one equation:
(15.34 - (23.710740...)/σ = -2.33
(15.34 - (23.710740...)/-2.33 = σ
σ=3.592592...
We finally have our answers:
μ=23.710740....
σ=3.592592....
