The h in p will start the p and the glock I believe
Subtract 6 from 50:
50-6 = 44
Divide 44 by 2:
44/2 = 22
add 6:
22+6 = 28
There is 22 in the smaller class and 28 in the larger class.
Answer:
a. 84 < n + (n + 2) + (n + 4) < 96
b. 26 < n < 30
step-by-step explanation:
using 'n' to represent the smallest even number, the next even number would be 'n + 2' and the next even number would be 'n + 4'. so, the expression to represent three consecutive even numbers is: n + (n + 2) + (n + 4).
since the sum of the these numbers needs to be between 84 and 96, we can set up the following inequality:
84 < n + (n + 2) + (n + 4) < 96
in order to solve for 'n', we must first combine like terms:
84 < 3n + 6 < 96
subtract 6 from all sides: 84 - 6 < 3n + 6 - 6 < 96 - 6 or 78 < 3n < 90
divide 3 by all sides: 78/3 < 3n/3 < 90/3 or 26 < n < 30.
Answer:
x (Falafel) = $5.50
y (Turkey BLT) = $7.50
z (Paninis) = $6.00
Step-by-step explanation:
Step 1: Write out systems of equations
5x + 15y + 20z = 260
8x + 18y + 14z = 263
12x + 16y + 12z = 258
There are multiple ways to solve for this systems of equations. I will use an augmented matrix for this:
Top row: [5 15 20 | 260]
Middle row: [8 18 14 | 263}
Bottom row: [12 16 12 | 258]
We find RREF form of the augmented matrix to find our answers:
Top row: [1 0 0 | 11/2]
Middle row: [0 1 0 | 15/2]
Bottom row: [0 0 1 | 6]
And we have our answer!
Answer:
It is a good Estimator of the Population Mean because the distribution of the sample midrange is just same as the distribution of the random variable.
Step-by-step explanation: from the table,
Minimum value = 34
maximum values = 1084
The sample mid-range can be computed as:
(Min.value + max.value)/2
(34 + 1084)/2
Sample mid-range = 55
The sample midrange uses only a small portion of the data, but can be heavily affected by outliers.
It provides information about the skewness and heavy-tailedness of the distribution which is just same as the distribution of the random variable.
The nature of this distribution is not intuitive but the Central Limit in which it will approach a normal distribution for large sample size.