Mohamed decided to track the number of leaves on the tree in his backyard each year. the first year, there were 500 leaves. each year thereafter, the number of leaves was 40% more than the year before. let f(n) be the number of leaves on the tree in Mohamed's backyard in the n^th year since he started tracking it. f is a sequence. what kind of sequence is it?
Number of leaves on the tree in first year = 500
The number of leaves was 40% more than the year before.
So rate of increase is 40/100 = 0.4
We use exponential growth formula,
f(n) = a(1+r)^n
Where a is the initial number, r is the rate of growth, n is the number of years
We know a= 500, r= 0.4
f(n) = 500(1+0.4)^n
f(n) = 500(1.4)^n
Plug in n=1,2,3...
f(1) = 500
f(2) = 500 * 1.4^1
f(3) = 500 * 1.4^2 and so on
From this we can see that the common ratio is 1.4
Hence it is a Geometric sequence.
Answer:
the answer is A=0.1 and B=0.85
Step-by-step explanation:
just took the test on edge
Well I don't know.
Let's think about it:
-- There are 6 possibilities for each role.
So 36 possibilities for 2 rolls.
Doesn't take us anywhere.
New direction:
-- If the first roll is odd, then you need another odd on the second one.
-- If the first roll is even, then you need another even on the second one.
This may be the key, right here !
-- The die has 3 odds and 3 evens.
-- Probability of an odd followed by another odd = (1/2) x (1/2) = 1/4
-- Probability of an even followed by another even = (1/2) x (1/2) = 1/4
I'm sure this is it. I'm a little shaky on how to combine those 2 probs.
Ah hah !
Try this:
Probability of either 1 sequence or the other one is (1/4) + (1/4) = 1/2 .
That means ... Regardless of what the first roll is, the probability of
the second roll matching it in oddness or evenness is 1/2 .
So the probability of 2 rolls that sum to an even number is 1/2 = 50% .
Is this reasonable, or sleazy ?
Its
C. Draw a line connecting the the intersection of the arcs below and above the segment.
Answer:
The correct answer is:
the amount of difference expected just by chance (b)
Step-by-step explanation:
Standard error in hypothesis testing is a measure of how accurately a sample distribution represents a distribution by using standard deviation. For example in a population, the sample mean deviates from the actual mean, the mean deviation is the standard error of the mean, showing the amount of difference between the sample mean and the actual mean, occurring just by chance. Mathematically standard error is represented as:
standard error = (mean deviation) ÷ √(sample size).
standard error is inversely proportional to sample size. The larger the sample size, the smaller the standard error, and vice versa.
