7p²-30p + 8 how do you factor this trinomial
1 answer:
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9514 1404 393
Answer:
- 20 months
- $5500
Step-by-step explanation:
There are a couple of ways to work a problem like this. You have probably been taught to write equations for each of the payment amounts as a function of time, then equate those values to solve for the time that makes them equal.
at dealer 1, the total amount paid (y) will be a function of months (x):
y = 2500 +150x
at dealer 2, the corresponding equation is ...
y = 3000 +125x
These are equal when ...
y = y
2500 +150x = 3000 +125x
25x = 500 . . . . . . . . . subtract 125x +2500 from both sides
x = 500/25 = 20
The total paid will be the same after 20 months.
That amount is ...
y = 2500 +150(20) = 5500
$5500 will be paid to either dealer after 20 months.
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The other way to work the problem is to "cut to the chase". The difference in down payment is made up at the rate of difference in monthly payments. So The number of monthly payments (x) required to equal the difference in down payments is ...
25x = 500 . . . . . . . . . you may recognize this equation from above
x = 500/25 = 20
1) Mean of the set of data: 29.88
2) Mean absolute deviation: 10.13
3) See explanation
Step-by-step explanation:
1)
The mean of a set of data it is calculated as
where
N is the number of data in the set
is the value of each point in the dataset
For the set of data in this problem, we have:
And the number of values is
N = 8
Therefore, we can calculate the mean:
2)
The mean absolute deviation of a set of data is given by
where
N is the number of values in the dataset
are the single values
is the mean of the dataset
The dataset here is
The mean, calculated in part 1), is
And
N = 8
Therefore the mean absolute deviation is
3)
The mean of a dataset is the sum of the single values of the dataset divided by the number of values. The mean represents the value for which, if the dataset would have N values all equal to
, the sum of the values of the dataset would be the same as the sum of the actual values.
The mean absolute deviation for a set of data represents the average of the absolute deviations of the single points from the mean of the dataset. This quantity gives a measure of the "dispersion" of the points around the mean: in fact, the larger the mean absolute deviation is, the more the points are "spread" around the mean of the dataset. Instead, if the mean absolute deviation is small, it means that the points are closer to the mean value.
Learn more about mean and spread of a distribution:
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