C) 2/9 , because dividing both 8 and 36 by 4 will leave the numerator as 2 (8/4=2) and the denominator as 9 (36/4=9).
Answer:
P=90
q=122
Step-by-step explanation:
Your post (" <span>f(x) = 2/3(6)x ") would be clearer and less ambiguous if you'd please format it as follows:
</span><span>f(x) = (2/3)(6)^x. The (2/3) shows that 2/3 is the coefficient of the exponential function 6^x. Please use " ^ " to indicate exponentiation.
Start by graphing </span><span>f(x) = (2/3)(6)^x. The y-intercept, obtained by setting x=0, is (0, 2/3). Can you show that the value of f(x) is (2/3)*6, or 4, at x=1, (2/3)*6^2, or 24, at x = 2, and so on? What happens if x becomes increasingly smaller? The graph approaches, but does not touch, the x-axis.
If you complete this graphing assignment, then all you'd have to do is to flip the whole graph over vertically, reflecting it in the x-axis. You'll see that the graph never touchs the x-axis. Therefore, the range of this flipped graph is (-infinity, 0).</span>
Answer:
a/(bxy) if b≠0, x≠0, y≠0, x≠y
Step-by-step explanation:
You have correctly simplified the expression. The conditions placed on both the original and simplified forms are that none of the denominator factors may be zero. The denominator factors are b, x, y, and (x-y).
Answer:
standard error = 2.11
Step-by-step explanation:
First we stablish the data that we have for each sample:
<u>Population 1</u> <u>Population </u>2
n₁ = 100 n₂ = 90
x¯1= 95 x¯2 = 75
σ₁ = 14 σ₂ = 15
To calculate the standard error of each sample we would use the formulas:
σ = σ₁/√n₁
σx¯2 = σ₂/√n₂
Now, in order to obtain the standard error of the differences between the two sample means we combine those two formulas to obtain this:
σx¯1 - σ x¯2 = √(σ₁²/n₁ + σ₂²/n₂ )
So as you can see, we used the square root to simplify and now we require the variance of each sample (σ²):
σ₁² = (14)² = 196
σ₂² = (15)² = 225
Now we can proceed to calculate the standard error of the distribution of differences in sample means:
σx¯1 - σx¯2 = √(196/100 + 225/90) = 2.11
This gives an estimate about how far is the difference between the sample means from the actual difference between the populations means.
